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BMC Bioinformatics
BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature
Methodology article
Guillermo de la Calle1,
Miguel García-Remesal1,
Stefano Chiesa1,
Diana de la Iglesia1 &
Victor Maojo1
BMC Bioinformatics volume 10, Article number: 320 (2009) Cite this article
The rapid evolution of Internet technologies and the collaborative approaches that dominate the field have stimulated the development of numerous bioinformatics resources. To address this new framework, several initiatives have tried to organize these services and resources. In this paper, we present the BioInformatics Resource Inventory (BIRI), a new approach for automatically discovering and indexing available public bioinformatics resources using information extracted from the scientific literature. The index generated can be automatically updated by adding additional manuscripts describing new resources. We have developed web services and applications to test and validate our approach. It has not been designed to replace current indexes but to extend their capabilities with richer functionalities.
We developed a web service to provide a set of high-level query primitives to access the index. The web service can be used by third-party web services or web-based applications. To test the web service, we created a pilot web application to access a preliminary knowledge base of resources. We tested our tool using an initial set of 400 abstracts. Almost 90% of the resources described in the abstracts were correctly classified. More than 500 descriptions of functionalities were extracted.
These experiments suggest the feasibility of our approach for automatically discovering and indexing current and future bioinformatics resources. Given the domain-independent characteristics of this tool, it is currently being applied by the authors in other areas, such as medical nanoinformatics. BIRI is available at http://edelman.dia.fi.upm.es/biri/.
The number of public online bioinformatics resources has grown exponentially over the past few years. Bioinformatics professionals can access and use a large number of resources for their research --including databases, tools and services. Discovering and accessing the appropriate bioinformatics resource for a specific research task has become increasingly important, as suggested in earlier reports [1].
To address this issue, various significant projects and initiatives have been carried out, leading to several pioneering indexes of bioinformatics resources that are currently available over the Internet. For instance, BioPortal [2] is a web repository of biomedical ontology resources, developed at the National Center for Biomedical Ontology (NCBO) [3]. This application enables collaborative development of biomedical ontologies. BioPortal includes a service called Open Biomedical Resources (OBR) for annotating and indexing biomedical resources [4]. Resources are annotated using concepts from a domain ontology. The OBR service enables researchers to locate resources by specifying ontology concepts.
Other examples of such indexes include the biomedical database collection compiled by Galperin [5]--a yearly updated web-based list of molecular biology databases sorted in alphabetical order-- or the Bioinformatics Links Directory (BLD) [6, 7] --a catalogue of links to bioinformatics resources, tools and databases classified into eleven major categories-- where resources can be searched using keyword-based queries.
The European Bioinformatics Institute (EBI) provides a searchable index of an alphabetically-sorted inventory of bioinformatics resources [8]. Resources are classified according to the type of service they provide--databases, tools and (web) services. The index includes both internal and external resources.
The BioMoby platform provides an annotated registry of bioinformatics web services enabling other applications to integrate and use such services [9, 10]. There are various major installations of BioMoby, such as, for instance, the PlaNet Consortium [11], the Australian Centre for Plant Functional Genomics [12], the Generation Challenge Program of the Consultative Group for International Agricultural Research [13], Genome Canada [14], or the Spanish National Institute of Bioinformatics (INB) [15]. They provide access to different bioinformatics resources depending on their own interests and needs.
A consortium composed of the seven US National Centers for Biomedical Computing [16] has recently developed another index of bioinformatics resources called iTools [17, 18]. Web services are used here to provide access to resources that are annotated according to their functionality. A web-based interface enables researchers to locate the resources they need using advanced search and visual navigation tools. Another initiative for organizing bioinformatics resources is the BIO2RDF system [19]. This system considers the information contained in biological databases, providing uniform access to biological data stored in public databases. Data is converted into RDF format using a common reference ontology. More than 20 publicly available databases --e.g. Entrez Gene, OMIM, GO, OBO, PDB, GeneBank, Prosite, etc. -- have been successfully integrated, including more than 160 million RDF documents.
In this paper we present a tool to automatically create a searchable index of bioinformatics resources from the scientific literature. The resources are annotated with metadata regarding their functionality. Metadata are extracted from abstracts of research papers retrieved from PubMed®[20] and the ISI Web of Knowledge®[21] using pattern-matching techniques. The generated index can be incrementally updated by feeding our tool with abstracts of articles describing new resources. In the evaluation section, we present and analyze some figures that compare the results of our approach with other existing indexes.
We extract relevant information about bioinformatics resources from papers published in high-impact journals. We focus on manuscript abstracts, which tend to condense the key information. We use classic natural language processing techniques--such as, for instance, tokenizers, parsers, transition networks or part-of-speech taggers--to extract this information. These techniques provide the basic framework for extracting data from textual resources [22]. All the techniques we have used are detailed in the following sections. The outcome of this process is an indexed knowledge base including the most relevant information about the resources.
Building the knowledge base
We propose a five-phase method for automatically generating a knowledge base of bioinformatics resources using research paper abstracts. Figure 1 shows an overview of the proposed methodology. Next we detail the five activities.
BIRI knowledge base automatic construction phases. We propose a five-step methodology for automatically building the knowledge base: i) abstract selection, ii) surrogate pre-processing, iii) information extraction, iv) resource classification and v) manual curation. Abstracts are extracted from scientific papers taken from the ISI Web of Knowledge®.
Abstract selection and generation of abstract surrogates
First we retrieve a collection of papers from well-known scientific literature web sites. Each paper is then analyzed to produce a structured surrogate, including the title, authors, abstract and electronic references to the paper--i.e. the PubMed® Identifier (PMID). The structure of the surrogate and a complete example is shown within the "Papers" section in Table 1. Such surrogates simplify the pattern-matching process. For instance, the title of the paper usually contains the name of the resources. We use that knowledge as a heuristic to discard unnecessary searches in other sections of the manuscripts.
Table 1 Complete resource information stored in the knowledge base
Surrogate pre-processing
Once the surrogates have been generated, the titles and abstracts are divided into sentences, using strong punctuation marks as sentence delimiters. Then, each sentence is pre-processed by a lexical analyzer. The analyzer extracts the words of the sentences using blanks and punctuation marks as word delimiters. This produces a sorted set of tokens tagged with lexical annotations. Next, each token is labelled with its corresponding part-of-speech (POS) tag using a probabilistic POS-tagger [23]. Stop words--such as articles, prepositions, etc.--are also filtered during this process. Finally, a stemming activity is performed to reduce each token to its root form, thus returning a set of lexemes tagged with morphological information [24].
Manual generation of patterns and transition networks
To automatically extract information from the abstracts, a group of five experts with background in different bioinformatics and text mining techniques created a set of linguistic patterns and then analyzed the selected abstracts. The set of linguistic patterns characterizes the type of information to be extracted. We used an initial training set of 100 abstracts of randomly chosen papers from PubMed®, describing bioinformatics resources--e.g. database and software papers published in the BMC Bioinformatics Journal [25] or application notes from the Bioinformatics Journal [26]. The selected abstracts were analyzed to discover and identify three different types of patterns: i) resource-naming patterns (RNP), ii) functionality patterns (FP), and iii) classification patterns (CP). RNPs aim to automatically extract the resource names together with their corresponding URL. Conversely, FPs aim to extract short textual descriptions of resource functionalities. By contrast, CPs focus on either i) the category of the resource or ii) its target domain.
A set of categories and domains was defined by the team of experts based on the taxonomy developed by the Bioinformatics Links Directory. This set includes the key categories of bioinformatics resources--e.g. databases, annotation services, visualization tools, etc. Each category is represented by a preferred name, together with a set of synonyms. Each resource category is linked to a collection of topics for its target domain(s). For instance, examples of domains for the "database" category are "DNA", "microarray", "polymorphisms" or "protein". Table 2 shows the complete list of BIRI categories and domains.
Table 2 Full list of BIRI categories and domains
Once the experts had identified the patterns, they were translated into Transition Networks (TNs) [27]. TNs are simple but effective abstract machines that determine whether a string belongs to a language defined by a regular expression. We adopted this approach since TNs are simple yet powerful tools suitable for performing pattern-matching tasks. In our approach, TNs are used to recognize instances of the patterns in the abstracts. We defined two different TNs. The first TN is designed to detect and extract the names of the resources together with brief descriptions of their functionalities. This TN is based on the extracted RNPs and FPs. Conversely, the second TN is designed to classify the resources into different categories depending on their functionalities and target domains. Figure 2 shows an extract of the first TN. This is represented as a finite state machine. Each state is represented by a numbered circle. The labelled edges show the required textual input for transiting from one state to another. Given an input, the aim is to reach a final state (for instance, state 9 in Figure 2) from an initial state (for example, state 0). If a final state is reached, then the TN outputs the extracted information for the matched resource.
Fragment of the transition network to detect resource names. This is an extract of the transition network used to identify resource names from the surrogates. According to the inputs (labels over the arcs), the transition network passes from state 0 to other states until it reaches a final state that determines the identification of a new resource. Different tags are used as labels: punctuation marks and words within quotes represent literals---i.e. marks or words that are in the surrogates---, and abbreviations in capital letters that represent morphological tags. For instance, NP is a proper noun, CC represents a conjunction, NN refers to common noun, DT symbolizes a determiner, JJ denotes an adjective, RB is a general adverb, OF and IN represent prepositions, PDT denotes a determiner, VB represents a verb, VBG refers to a verb in gerund form (ending in -ing) or SYM is a symbol or formula.
Information extraction
Extracted patterns are applied to the abstracts as follows. First, the abstract is analyzed by the first TN to locate the substrings that match any of the patterns. Then, the relevant information is extracted from the substring. For instance, the input sentence "The FSSP database of structurally aligned protein fold families" matches the RNP ["The" + Proper Noun + "database" + "of"]. In this case, the analyzer identifies "FSSP" as a proper noun. Another example of RNP could be [Proper Noun + ":" + "a"] that would match the sentence "EMBL-Align: a public nucleotide and amino acid multiple sequence database. In this case, "EMBL-Align" would be recognized as a proper noun.
FPs are often more complex to match than RNPs and CPs since their associated patterns require further lexical components for instantiation--e.g. verb tenses, part-of-speech tags, etc. For instance, the input sentence "A novel method for fast and accurate multiple sequence alignment" matches the following pattern ["A" + JJ++ "method" + "for" + (JJ + (CC + JJ)?)? + JJ* + NN++ "."], where JJ represents an adjective, CC identifies a conjunction and NN is a common name. We use special characters of regular expressions to specify the patterns, where '?' represents optionality, '+' one or more occurrences and '*' zero or more occurrences of the component. In the last example, the functionality extracted would be "sequence alignment". During the pattern-matching process, the instantiation of one of the patterns triggers a procedure that extracts and stores the relevant information in the knowledge base.
Resources classification
Once the name and the description of the functionalities for a given resource have been extracted, we run the second TN to perform the classification. The TN is fed with the extracted description(s) of the functionality of the current resource, and it tries to match this description with a resource category and domain. If successfully matched, the resource is labelled with the preferred term associated with the respective category or domain. Note that a resource could be assigned to several categories or domains. For instance, one possible pattern for detecting and extracting a resource category and domain is: [("program" | "tool" | "server") + ("for"|"to") + (JJ* + (NN | NNS))? + "^annot" + ("of" + JJ* + (NN | NNS)+)? + "."]. Applying this pattern to the sentence "AMIGene: a tool for annotation of microbial genes", the category we get is "annotation" and the resource domain is "genes".
Additionally, any extra information--such as resource inputs and outputs--can also be extracted by the second TN when available. We refer to the type of data received by the resources when they are invoked as 'inputs' and to the type of data they return as a result as 'outputs'. For instance, applying the pattern ["discovery" + "of" + (JJ + CC?)* + (NN | NNS)++ ("within" | "in") + JJ* + (NN | NNS)++ "."] to the sentence "We present here a new tool for discovery of unstructured or disordered regions within proteins.", "proteins" would be the resource input data.
Data curation
Once all relevant information contained in the abstracts has been extracted, a team of experts reviews the contents of the knowledge base to assess its correctness. The curation process mainly focuses on the detection of categories and domains incorrectly assigned to specific resources. Then, the experts compare the previously extracted functionality with the assigned categories and domains. If any errors are detected, the inaccurate entries are removed from the BIRI knowledge base.
Description of the knowledge base
Once the information has been filtered, it is stored in a knowledge base. For each discovered resource, we record the following data: i) the name of the resource, ii) its corresponding URL, iii) a set of natural language descriptions of its functionalities, iv) the resource's assigned categories, v) its target domain(s) and vi) the resource's inputs and outputs when available. Using the same example as above, this information could be extended and used to automatically orchestrate workflows involving multiple resources. The knowledge base also stores data about the original papers, including the paper's title, author(s), abstract and PubMed® and ISI Web of Knowledge® identifiers.
Figure 3 is a conceptual view of the knowledge base. As Figure 3 shows, each resource can be linked to one or more natural language descriptions of its functionalities--i.e. a tool may provide different functionalities. Each functionality consists of a natural language textual description extracted from the papers. The descriptions of the functionalities are connected with their respective categories and target domains. For instance, the 'FATCAT' resource in Figure 3 is linked to two different functionalities: 'Functionality 2.1' and 'Functionality 2.2'. Similarly, 'Functionality 2.1' is linked to the category 'Search' and to the domain 'Protein', whereas 'Functionality 2.2' is connected to the 'Comparison' category and to the 'Protein' domain. Therefore, the 'FATCAT' resource can be regarded as a tool for searching and comparing proteins. Similarly, 'CrossLink' can be identified as a tool for exploring RNA sequences, 'MATRAS' can be considered as a tool for aligning and comparing proteins and 'GenePublisher' can be identified as a resource for analyzing DNA microarrays.
Conceptual schema of the BIRI knowledge base. Each resource in the knowledge base is associated with one or more functionalities. Functionalities are textual natural language descriptions, directly extracted from the papers. Each functionality is linked with one category and one domain of the existing resource list of categories and domains.
We implemented the data extraction tool and the web services layer (WSL) providing the query primitives to access the knowledge base using the Java programming language and associated technologies [28]. This includes the Java Web Services Developer Pack (JWSDP) [29].
The system architecture includes a web services query layer developed to cover the contents of the knowledge base. To test the functionality of the developed WSL, we created a pilot web application on top of the WSL to provide users with a searchable web index of bioinformatics resources, by using popular web browsers including Internet Explorer®, Mozilla Firefox® and Safari®.
As Figure 4 shows, the pilot web application provides the following search capabilities: i) retrieve all the information related to a specific resource given its name, and ii) search for relevant resources belonging to a given category and/or target domain. In both cases, the user is presented with a list of records matching the user query and containing basic information on the retrieved resources. This includes the resource name with a link to the complete information of the resource, a short textual description of the provided functionality, and its assigned categories and domains. Note that the indexing engine may have classified a single resource into multiple categories and domains, thus producing several entries in the results list. Clicking on the resource name, the system shows the complete information on this resource, including additional links to i) the official web page of the resource (if it is actually available in the abstract) and ii) the actual paper record at the PubMed® website from which the information was extracted.
Web Application Interface for Accessing the BIRI Knowledge Base. The BIRI web application interface is useful for making search-by-category, search-by-domain and search-by-resource name queries. A list of resources satisfying a query is provided, showing the names of the resources plus links, functionalities, categories and domains.
A use case
Let us suppose that a researcher wants to identify resources for analyzing information from microarrays. The web application will show the existing categories and domains in two combo-boxes. These categories and domains are dynamically loaded using different web services. When the user selects a category, the domain combo-box is automatically updated to show the domains associated with the selected category only. Similarly, when the user selects a domain; the category combo-box is updated with the appropriate categories. For instance, the researcher may select "analysis" from the combo-box category. Then, the combo-box domain will be automatically updated with all the available domains for the chosen category. It includes an extra option for selecting "All" domains. The researcher will select the "microarray" domain from that list and then click on the Search button. The category and domain selection order is not mandatory. The user could select the domain first and the category afterwards.
After the user clicks the Search button, the web application displays a table with information about the resources contained in the knowledge base that meet the user-imposed restrictions. In the above example, the table contains eleven entries, as shown in Figure 4. Each entry contains the name of and a link to the resource, its functionality, category and domain. Further details on a given resource can be retrieved by clicking on the resource name link.
The web application has been tested with multiple queries selecting different combinations of categories/domains. Users could also search for a resource by its name. Searches by name are case-insensitive. The results would be similar but restricted to the specified resource.
Another interesting feature of the application is to incrementally update the index with new resources. This is achieved by entering the respective research papers in the update module. This module verifies the new papers added to prevent double entries. Papers that have been previously processed by the system are passed up, and the others are applied as described above. Users can also contribute by suggesting the inclusion of additional tools/abstracts by sending an email to the contact address provided on the BIRI web page.
As stated previously, resources are automatically classified and indexed according to a custom-created list of categories and domains shown in Table 2. This list is composed of 28 categories--i.e. resource types--targeting nine different domains--i.e. data types. We conducted an experiment using an input set of 400 abstracts extracted from the ISI Web of Knowledge®. To create the test set, we queried the system with the term "bioinformatics resources". Then, we sorted the results by impact factor and date, and finally we selected the first 392 documents (the most relevant) according to this classification [see Additional file 1]. We also included a small number of documents (eight, in this case) unrelated to bioinformatics resources to test the robustness of our approach. As Figure 5a shows, the first TN extracted the names of 376 resources (94%)--i.e. 24 manuscripts (6%) did not produce anything. From these 24 manuscripts, 16 papers (4%) were discarded even though they contained information about bioinformatics resources and the remaining 8 papers were the control set that we manually created and included for this experiment.
Summary of experiment results. The results of the experiment show that 88% of the functionalities were successfully extracted from the analyzed abstract. The functionality was incompletely extracted in 10% of the cases, and only 2% of the cases were incorrect.
Regarding the functionality extraction process, the first TN discovered 505 descriptions of functionalities. They were assigned to the 333 identified resources. Note that a single resource may be assigned to one or more functionalities. As Figure 5c shows, a high percentage of the extracted functionalities (88%) provided complete and coherent descriptions. Conversely, 10% were incomplete descriptions that still provided valuable information for automatically extracting their associated category and domain. The remaining 2% matched up with incorrect or incoherent descriptions. The coherence of the extracted functionalities was manually assessed to verify the correctness of the extraction method.
Conversely, Figure 5b shows the results of the classification process carried out by the second TN. This was fed with the set of descriptions extracted by the first TN. The second TN successfully assigned at least one category to 305 resources, and at least one domain to 253 resources. Table 3 shows some sample entries of the generated knowledge base.
Table 3 Samples of short-form knowledge base entries
These entries present the extracted information for four different bioinformatics resources. The first column shows the name of the extracted tools--i.e. "CrossLink", "FATCAT", "MATRAS" and "GenePublisher"--whereas the second column provides a short description of their functionality. The third column shows the category in which the corresponding resource has been classified. This classification is further refined in the fourth column, which lists the specific application domain for this resource. For instance, as shown in the Table 3, the "CrossLink" resource has been classified under the category "Exploration" and targets the "RNA" domain. Therefore, "CrossLink" can be regarded as a tool to explore RNA sequences. Also, additional information can be retrieved for each resource. Table 1 shows an example of all the information BIRI provides about any resource. This information has been automatically extracted from the scientific papers using the proposed method. It contains the resource name, functionalities, categories, domains, inputs, outputs and paper from which the information has been extracted. Links to the official web site of the resource and to the whole paper --in PubMed®-- are provided if they are available in the abstract.
To evaluate the suitability of our approach, we have compared BIRI with other existing public indexes according to two dimensions: i) features and characteristics provided by the indexes, and ii) the number of resources contained in each index that also appear in BIRI. Table 4 focuses on the first dimension--i.e. features and characteristics of the indexes. The information used to complete Table 4 has been extracted from the literature and the official web sites where the indexes are publicly available. We considered five different features: i) generation of the index, --i.e. whether the index was created manually or (semi-)automatically--, ii) indexation of external resources--i.e. whether other resources have also been indexed in addition to resources that actually belong to the institution or consortium that set up the index--, iii) the type of search capabilities provided by the index, iv) whether or not the resources are annotated, and v) whether the index includes any form of resource classification. As shown in Table 4, all the indexes that have been analyzed provide some kind of resource classification and annotation. Most of these indexes consider external resources and provide other advanced search capabilities. The major differences lie in the index generation process since, in most cases, the indexes are manually generated.
Table 4 Comparison of existing resource indexes
Table 5 deals with the second dimension of the evaluation. It compares the indexes according to the number of resources that they contain. The yardstick was the curated BIRI knowledge base. After the curation process, it contained 316 different resource names. Then, we compared BIRI with the other indexes listed in Table 4. From this comparison, we were able to obtain the full list of stored resources. Table 5 shows the total number of resources for each index, the number of matches with BIRI--i.e. the number of resources that appear simultaneously in both indexes--, and the number of resources discovered by BIRI that the other indexes do not contain. As we can see in the table, BIRI obtains high matching rates (over 73%) with two indexes--the BLD and the Online Bioinformatics Resource Collection. Matching rates for the other indexes are from 3% to 10%. The difference is due to the kind of resources each index contains. The indexes containing heterogeneous resources, such as BIRI, achieve higher matching rates than others containing only one type of resources, such as Molecular Biology Database or Database of Databases --which only index databases.
Table 5 Evaluation of the BIRI contents against other indexes
Based on pattern matching methods, our method can automatically create a knowledge base of bioinformatics resources by i) detecting resource names, ii) extracting short descriptions of functionalities and iii) classifying the extracted artefacts according to a list of categories and domains of bioinformatics resources, which extends the BLD classification [7] on which our list is based. We believe that creating a standardized taxonomy or ontology of bioinformatics resources is a crucial task to facilitate collaborative and integrative approaches [1].
For the methodology proposed in this paper to work, the bioinformatics resources have to have been previously published and indexed in PubMed® or the ISI Web of Knowledge®. Otherwise, the resources would never be found using this method. In contrast, our approach guarantees that only high-quality resources are indexed. Once these resources have passed a peer-review process, confidence in their quality can be actually higher.
Table 4 compares our approach with the most relevant indexes available at the time of writing this paper --and considering that BIRI is a prototype, currently being expanded. As shown in Table 5, from an original set of over 400 papers, the system automatically discovered more than 230 resources that also appear in BLD or the Online Bioinformatics Resource Collection. Another interesting fact pointed out by Table 5 is that BIRI contains several resources not included in other indexes. The number of new resources in BIRI ranges from 81 (when compared to BLD) to 306 (when compared to Pathguide). This happens since existing indexes are often updated only considering manuscripts published in a reduced set of journals. This limited vision hinders the creation and maintenance of an exhaustive list of resources. Conversely, our methodology is not centered in any particular journal. It follows a more general approach using PubMed® or the ISI Web of Knowledge® as information sources. This provides us with a broader vision of recent developments. Once new manuscripts are available in PubMed® or the ISI Web of Knowledge®, our system can be updated with the new resources.
Our approach extends most of the search capabilities provided by other existing tools. In addition, the index is automatically generated and updated in our approach. The update process is a very time-consuming and tedious task that is usually performed manually by groups of experts. Applying the same methodology detailed above, the contents of our index and knowledge base can be automatically updated by just entering new manuscripts and abstracts into the tool.
Our index provides users with advanced search capabilities. It can, for instance, perform complex searches, such as searching for resources matching a definite category and/or target domain. As stated previously, our knowledge base is built automatically using pattern-matching techniques, whereas other indexes are created, maintained and updated manually. Besides, our knowledge base can be automatically updated with new resources simply by feeding the developed information extraction tool with manuscripts describing recently developed tools, databases and services.
Considering other existing indexes, our knowledge base provides additional information, such as, for instance, the target domains where the different types of resources can be applied or the resource inputs and outputs. Using the resources' names and extending the information about inputs and outputs, BIRI could be useful for automatically orchestrating workflows like applications combining several resources. An example of resource combination through workflow definition is described in a previous work carried out by the authors, where multiple databases are queried to retrieve information regarding the proteins involved in a genetic disease [30]. The results provided by a database are used to build the query for the next database.
Additionally, text-mining based methods for information extraction reported that they could benefit from manuscripts with more structured abstracts [31].
Our tool automatically extracts and organizes relevant information about bioinformatics resources from research papers describing the resources. Our method, based on a domain-independent approach, can be used to create inventories targeting different scientific fields [32, 33]. For instance, this approach is currently being applied in the European Commission-funded Action-GRID project, coordinated by the authors [34]. In one of the workpackages of this project, we are creating an inventory of bioinformatics and nanomedical resources that is intended to help researchers in these areas [35].
Several initiatives have been carried out aimed at cataloguing the existing bioinformatics resources. Although our tool could work as a standalone application, it has not been designed for this purpose. Our tool is intended not as a replacement for but an add-on to existing indexes and applications. We are working on integrating our tool as a plug-in for other consolidated applications, such as BioPortal or BioMoby. Additionally, tools for defining workflows, such as TAVERNA [36], could also benefit from the information provided by an extended version of our index.
The inventory of resources could also be collaboratively updated by other external contributing researchers. This collaborative approach is being successfully applied in other fields, such as, for instance, developing biomedical ontologies at the NCBO [3]. At the NCBO, different collaborative tools--developed using Web 2.0 techniques--are available for developing biomedical ontologies. Researchers can contribute by entering new information or comments about the existing information.
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Chiesa S, Garcia-Remesal M, de la Calle G, de la Iglesia D, Bankauskaite V, Maojo V: Building an Index of Nanomedical Resources: an Automatic Approach Based on Text Mining. Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES): 3–5 September 2008; Zagreb (Croatia) 2008, (2):50–57.
Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, Li P, Oinn T: Taverna: a tool for building and running workflows of services. Nucleic Acids Res 2006, (34 Web Server):W729–732.
Bader GD, Cary MP, Sander C: Pathguide: a Pathway Resource List. Nucleic Acids Research 2006, (34 Database):D504-D506.
Chen YB, Chattopadhyay A, Bergen P, Gadd C, Tannery N: The Online Bioinformatics Resources Collection at the University of Pittsburgh Health Sciences Library System-a one-stop gateway to online bioinformatics databases and software tools. Nucleic Acids Research 2007, (35 Database):D780-D785.
ExPASy Life Science Directory[http://expasy.org/links.html]
Babu PA, Udyama J, Kumar RK, Boddepalli R, Mangala DS, Rao GN: DoD2007: 1082 Molecular Biology Databases. Bioinformation 2007, 2(2):64–67.
Stevens RD, Robinson AJ, Goble CA: myGrid: Personalised Bioinformatics on the Information Grid. Bioinformatics 2003, 19(Suppl 1):i302-i304.
Lord P, Alper P, Wroe C, Goble C: Feta: a Light-Weight Architecture for User Oriented Semantic Service Discovery. In Proceedings of the Second European Semantic Web Conference (ESWC): 29 May - 1 June 2005; Heraklion (Greece). Volume 3532. Springer Berlin/Heidelberg; 2005:17–31.
Wolstencroft K, Oinn T, Goble C, Ferris J, Wroe C, Lord P, Glover K, Stevens R: Panoply of Utilities in Taverna. In Proceedings of the First International Conference on e-Science and Grid Computing (E-SCIENCE): 5 - 8 December 2005; Washington (USA). IEEE Computer Society; 2005:156–162.
Saltz J, Oster S, Hastings S, Langella S, Kurc T, Sanchez W, Kher M, Manisundaram A, Shanbhag K, Covitz P: caGrid: Design and Implementation of the Core Architecture of the Cancer Biomedical Informatics Grid. Bioinformatics 2006, 22(15):1910–1916.
The present work has been funded by the Spanish Ministry of Science and Innovation (OntoMineBase project, reference TSI2006-13021-C02-01, and COMBIOMED- RETICS), the European Commission through the ACGT integrated project (FP6-2005-IST-026996), the ACTION-Grid support action (FP7-ICT-2007-2-224176) and the Comunidad de Madrid, Spain.
Dept Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660, Boadilla del Monte, Madrid, Spain
Guillermo de la Calle, Miguel García-Remesal, Stefano Chiesa, Diana de la Iglesia & Victor Maojo
Guillermo de la Calle
Miguel García-Remesal
Stefano Chiesa
Diana de la Iglesia
Victor Maojo
Correspondence to Guillermo de la Calle.
GDLC conceived and participated in the design of the study and drafted the manuscript. MGR conceived and participated in the design of the study and drafted the manuscript. SC participated in the design of the study and the evaluation of the results. DDLI participated in the design of the study and implemented the system. VM conceived the study and helped to draft the manuscript. All authors read and approved the final manuscript.
Guillermo de la Calle, Miguel García-Remesal, Stefano Chiesa, Diana de la Iglesia and Victor Maojo contributed equally to this work.
12859_2009_3050_MOESM1_ESM.TXT
Additional file 1: PubMed identifiers list. This file contains the list of the 392 PubMed identifiers selected to create the test set. (TXT 4 KB)
de la Calle, G., García-Remesal, M., Chiesa, S. et al. BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature. BMC Bioinformatics 10, 320 (2009). https://doi.org/10.1186/1471-2105-10-320
Received: 12 February 2009
DOI: https://doi.org/10.1186/1471-2105-10-320
Target Domain
Bioinformatics Resource
Biomedical Ontology
Submission enquiries: bmcbioinformatics@biomedcentral.com


Jul. 13, 2018 4:00 p.m.
Proud sponsor Black Press Media is honoured to present our special documentary on the 16th annual Miss B.C. pageant held in Fort Langley.
Check out all of the highlights from a magical evening that recognized the personal achievements and community involvement of nearly 50 girls and women vying for the Miss, Mrs., Miss Teen and Jr. Miss B.C. crowns.
Read our full story, photos and videos on the 2018 pageant here.


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PRESS RELEASES AND MEDIA COVERAGE
www.workability.one New Job Board for Neurodiverse, Autistic and Disabled Job Seekers
Hopes To Help Ease Labor Shortage and Connect Forward-Thinking Companies With Uniquely Talented Workers
News outlets ranging from The Wall Street Journal to National Public Radio have quoted dozens of business leaders across the country bemoaning the fact that they aren't able to find anyone to fill their open positions. Millions of Americans lost their jobs during the COVID-19 pandemic and with the national unemployment rate hovering at six percent, "Help Wanted," signs with generous signing bonuses continue to dot the business landscape.
Given the tight job market and lack of available workers, small businesses and large employers alike can develop cost-effective recruiting strategies by tapping into a hidden market of talent -- the estimated 5,437,988 (2.21%) adults in the United States who have ASD (https://www.cdc.gov/ncbddd/autism/features/adults-living-with-autism-spectrum-disorder.html).
Over 80 percent of college-educated, autistic adults remain unemployed, according to advocacy organization Autism Speaks. A new employment website www.workability.one hopes to bring job opportunities to the forefront by connecting neurodiverse, Autism Spectrum Disorder (ASD), and disabled job seekers with forward-thinking companies seeking uniquely talented workers.
The prevalence of ASD (about one to two percent globally, and higher—and increasing—in the U.S.) has made an impression on forward-thinking organizations keen to understand the spectrum of the disorder and its impact on work and training.
"Employers need to rethink their recruiting, interviewing, onboarding and training strategies," said www.workability.one. "It's no longer about jobs being filled by the right people -- it's about companies adjusting the jobs, work hours, and goals to meet needs of workers. It's a neurodiverse world, and a one-size-fits-all approach no longer works. Job flexibility can be defined in multiple ways."
Characteristics and skills common among the neurodiverse -- pattern seeking and recognition, singular focus on a specific task until its completion, honest and direct communication -- are desirable attributes for new employees -- and can be beneficial in fields such as in cybersecurity, data analytics and software testing. "Neurodiversity can be an important advantage in highly competitive technical fields," workability.one added.
www.workability.one aspires to be the go-to career portal for neurodiverse, ASD and differently abled job seekers, and hopes its reasonably priced job posting fees will entice companies across the country. Employers can post one job at a time, or enlist the help of www.workability.one to upload multiple jobs on the site. "We're happy to help, and encourage employers to post a wide range of job types," said workability.one.
www.workability.one is cultivating an audience and site visitors among neurodiverse, ASD and disabled influencers and local and regional advocacy groups. workability.one is registered as a supplier on the Ariba Business Network under Ariba Network ID AN01695504703.
To learn more, visit www.workabiity.one.

redpajama_set_name: RedPajamaCommonCrawl


This entry was posted in social media and tagged #RunChat, Barack Obama, Cate Elefante, Katy Perry, Midlife Road Trip, Rubem Robierb, Sway Group, Twitter, Twitter Chats, Twitter Parties, Waitress, Wired by Paula Kiger. Bookmark the permalink.
I love Twitter for the ability to follow breaking news in real time. Reputable news outlets, journalists and people experiencing the event provide incredible insight.
I LOVE twitter and for some reason Lucy Lawless follows me. About every 6 months, we have a conversation. Life is weird.
Let me know if you discover any new good things!
I hope the tips make it more pleasant!
I still like Twitter, though I do spend far less time there than I have in the past. Like you, I have meet a fair amount (actually I think my percentage is higher than yours considering I have less followers) of IRL friends and colleagues there. My favorite remains the Twitter chats, mostly for professional development and networking but sometimes just for fun.
I love twitter chats too, so much! Thanks for dropping by (and for sharing the post).


If you've been unemployed recently, you'll notice that the job hunt has changed drastically from what it used to be. You used to submit applications to job postings, sit home and wait for someone to call you for an interview. People don't do this anymore. We're still in a recession and unemployment rates are still high. Because of this, it's likely that someone more qualified than you had applied to these open competitions anyway. If you're sitting home waiting for the standard process to work, you're going to be sitting there for a long time.
I've spoken at length about how to use social media to network and get yourself noticed outside the traditional channels. In addition to doing this, you need something about you that stands out from the other candidates, and then you need to effectively convey that to potential employers. You basically need a personal marketing plan.
First you need to identify what you want to be doing.
What are the skills someone doing that job needs to have?
What are that person's daily tasks?
What behavioural competencies are required for this position?
What type of experience does that person need?
What does this person's career path look like?
You should be able to answer most of these questions before even starting to think about applying. Then you need to identify the gaps between you and the ideal candidate for this position. Before applying for this position, you may need to upgrade some skills or gain some experience. Don't sit there defeated saying "I can't get experience because no one will hire me." There are internship opportunities or not-for-profits, and small businesses who would gladly accept a volunteer to do whatever it is you do. Then when you introduce yourself, you can replace the word "unemployed bum" with "freelancer" or "professional consultant" even if you're not getting paid.
Do you have accomplishments that other candidates may not have (i.e. awards, publications, relevant memberships, etc.)?
Get to know your own personality. Ask some friends how they would describe you. You need recruiters to look at your resume and online profiles, and really feel like they know you. Unless you're just a miserable person… then hide your personality at all costs.
Once you've done this, you need to be able to describe yourself as the ideal candidate. Seamlessly link your description of yourself to the description of the ideal candidate. When you introduce yourself to people, they will often ask what you do. You need a brief description of yourself that highlights your key differentiators. You should also tailor it to your audience. There may be more than one career path to your ideal job, or more than one job that interests you. Know your audience, and make sure you're telling them what they want to hear from you. Companies wouldn't use the same marketing collateral across all clients. They tailor it to highlight the products or services most valued by different target markets. You should do the same for your target companies.
Once you've created the outline of your candidate brand, update all of your social networking sites well as your resume, business card, etc. to reflect this brand. Then you can start to develop an application process. Much like a sales process, you're going to be generating leads and identifying the most effective methods of reaching out to those companies, that is consistent with your personal brand. I have an entire series of "How To" posts about using social networking for the job hunt called "Dude Where's My Job?" Take a look back through for more tips on interacting with these companies online.
Sitting at home, filling out applications and applying to open ads is old-school. That job search model is not productive in this type of economy, so why would you sit home and do the same thing over and over again with no results? Stand out and be productive.
Undergrad Success thanks Scott Keenan for this awesome post!


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Appeal of DIONE GOLDIN from action of John G. Marmillo, Superintendent of the Wappingers Central School District and Ronald L. Warman, president of the Wappingers Congress of Teachers, relating to an employment agreement.
(December 10, 1998)
Wasserman & Steen, attorneys for respondent Marmillo, Lewis M. Wasserman, Esq., of counsel
James R. Sandner, Esq., attorney for respondent Warman, Kevin H. Harren, Esq., of counsel
MILLS, Commissioner.--Petitioner appeals from an agreement entered into by respondents Marmillo and Warman, respectively, on behalf of the Board of Education of the Wappingers Central School District and the Wappingers Congress of Teachers which released respondent Warman from his teaching duties to conduct union business full-time. The appeal must be dismissed.
The Collective Bargaining Agreement for the period July 1, 1988 through June 30, 1992 between the Wappingers Central School District and the Wappingers Congress of Teachers provides that the Congress president will teach three periods per day if he/she is a secondary teacher and will act as a substitute three days per week if he/she is an elementary teacher. By memorandum of agreement dated February 14, 1995, the parties extended the agreement through June 30, 1998. The agreement was ratified by the Wappingers Congress of Teachers on March 1, 1995 and approved by the board of education on March 7, 1995. By memorandum dated March 8, 1995, entitled "side letter," Superintendent of Schools John G. Marmillo and Wappingers Congress of Teachers President Ronald L. Warman agreed to relieve the president of all teaching duties to conduct Congress business and that the Congress would reimburse the district in accordance with a formula based on 60 percent of the substitute pay rate. The Collective Bargaining Agreement was first published and made available to the public in December 1995. At its February 26, 1996 meeting, the board of education discussed the "side letter" agreement and directed respondent Marmillo to terminate it, which he did on February 27, 1996. The district's local newspaper published an article reporting the board's action on February 28, 1996. This appeal ensued.
Petitioner alleges that the "side letter" is an illegal document, that respondent Marmillo improperly relieved respondent Warman of his full-time teaching duties in contravention of the district's collective bargaining agreement, that district funds were improperly paid to respondent Warman and that respondent Marmillo failed to perform his duties under the Education Law. She seeks annulment of the "side letter", repayment of respondent Warman's salary for the period covered under the letter, and an order directing his return to his teaching duties pursuant to the collective bargaining agreement.
Respondents raise several procedural defenses. They contend that the appeal is untimely, that petitioner has failed to join the Wappingers Board of Education and Congress of Teachers as necessary parties, and that the matter is moot. Respondents' assertions are correct.
First, the appeal must be dismissed as untimely. An appeal to the Commissioner of Education must be brought within 30 days of the making of the decision or the performance of the act complained of, except for good cause shown. Such cause must be set forth in the petition (8 NYCRR "275.16). Petitioner acknowledges in her petition that she "first discovered the existence of the side letter by accident in June 1995". Petitioner states that she spoke to a State Education Department representative in June 1995 about the side-letter and also spoke to Wappingers' school board members about the side-letter agreement at approximately the same time. Moreover, the districts' collective bargaining agreement, which petitioner asserts precludes such "side letter", was available in December 1995. Petitioner did not appeal to the Commissioner about the conduct of which she complains until May 28, 1996, when she served respondent Marmillo. She did not serve respondent Warman until June 18. Accordingly, the petition is time barred (Appeal of T.B., 35 Ed Dept Rep 408). Petitioner's claim that she filed the appeal within thirty days of receiving instructions to do so from the State Education Department does not constitute good cause for delay. Ignorance of the appeal process is not a sufficient basis to excuse a delay in commencing an appeal (Appeal of Klein, 35 Ed Dept Rep 91).
Petitioner has also failed to join a necessary party to the appeal, warranting its dismissal. A party whose rights would be adversely affected by a determination of an appeal in favor of petitioner is a necessary party and must be joined as same (Appeal of Kurlans, 37 Ed Dept Rep 293; Appeal of Bartling, 35 id. 324). Herein, petitioner failed to name or serve the Board of Education of the Wappingers Central School District, a party in interest to the agreement she attacks. Since petitioner seeks a determination that the side-letter agreement was illegally negotiated and is therefore null and void, an order directing respondent Warman to return to his teaching responsibilities as mandated by the collective bargaining agreement, and an order directing reimbursement to the Wappingers Central School District for illegal payments made under the side-letter agreement, it is apparent that the rights and responsibilities of the Wappingers Central School District would be affected if the Commissioner granted the relief requested by petitioner. Accordingly, the petition must be dismissed for petitioner's failure to name and serve the Wappingers Central School District as a party to this proceeding. In addition, to the extent that petitioner seeks an order directing the Wappingers Congress of Teachers to reimburse the school district, union organizations are not subject to the jurisdiction of the Commissioner of Education under Education Law "310. Thus, even if petitioner had properly joined the Wappingers Congress of Teachers as a party, I would be unable to grant the relief she seeks against the Congress.
Respondents are also correct in their contention that the appeal is moot. It is well settled that the Commissioner of Education will not render a decision upon a statement of facts which no longer exists or which subsequent events have laid to rest (Appeal of Goldin, 35 Ed Dept Rep 446; Appeal of Scarrone, 35 id. 443; Application of Ciffone, et al., 35 id. 243). In this case, the Wappingers Board of Education already terminated the side-letter and respondent Warman returned to the teaching duties required under the collective bargaining agreement. With respect to monies that petitioner claims are owed by the Wappingers Congress of Teachers to the Wappingers Board of Education on behalf of the school district, petitioner's failure to join the board of education and the jurisdictional limitation respecting the teachers' association, as discussed above, would preclude payment if, in fact, such monies were owed. Consequently, no further relief can be granted, and the matter is moot.
Finally, in her reply, petitioner raises allegations that the Wappingers Board of Education violated the Open Meetings Law relating to the conduct of executive sessions. Initially, I note that the purpose of a reply is to respond to procedural defenses or respond to new material contained in an answer (8 NYCRR ""275.3 and 275.14). A reply is not meant to buttress allegations in the petition or raise new allegations that should have been in the petition (Appeal of Chechek, 37 Ed Dept Rep 624; Appeal of Lambert, 37 id. 599). Thus, petitioner's new claims are improperly raised in her reply. Moreover, alleged violations of the Open Meetings Law must be pursued in a judicial proceeding in State Supreme Court pursuant to Article 78 of the Civil Practice Law and Rules (Public Officers Law "107; Appeal of Lambert and MacDonald, 37 Ed Dept Rep 588; Appeal of Gwinner, 37 id. 262; Appeal of Marek, 35 id. 314). Therefore, I am without jurisdiction to decide those claims.
In view of the disposition of the procedural issues, I need not address petitioner's other claims.


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Data Protection Officer Outsourcing
EU AI Ethics Assessments
Tag: Cyber Security
Aphaia: Leading experts in ICT regulation and policyCyber Security
Data Protection ePrivacy Zandilli Lucien October 19, 2020
Amazon facing lawsuit in Germany, accused of breaking EU's privacy laws.
Amazon facing lawsuit in Germany after being accused of breaking EU's privacy laws against the EU-US Privacy Shield.
The global giant Amazon is currently facing a lawsuit and has been accused of breaking the privacy laws in Europe, according to this recent article from Politico. The company has been accused of using the infamous Privacy Shield despite its previous invalidation in Europe which has led to this lawsuit. The basis is that the Court of Justice of the European Union made clear that transferring data through the Privacy Shield was no longer allowed following July's Schrems II judgment. This ruling invalidated the EU-US privacy shield. The reason for the invalidation was that the CJEU decided that shipping data outside of the EU put it at risk. According to the CJEU, US surveillance customs are more intrusive than they should be and go beyond what is acceptable for privacy. While Amazon understands that the Privacy Shield is invalid, it appears that they have continued to use this invalidated transfer mechanism.
Standard Contractual Clauses are still a viable option for companies needing to transfer data.
Standard Contractual Clauses (SCCs) are another option for the technological giants and are used by the likes of Google and Facebook. The difference is that exporting data from the EU using the SCC requires more supervision, and better ensures the safety of the data. While the SCC gives these companies an alternative, the clauses come with caveats, and are not entirely free of problems. Right now, the giant Facebook stands against the Irish data regulators regarding their use of the clauses.
EuGD takes legal action against Amazon.
EuGD (Europäische Gesellschaft für Datenschutz) decided to take action putting forth the formal legal complaint that escalated the conflict. The recent article by Vincent Manancourt, features a statement from Johann Hermann, the current head of EuGD, the group behind the legal complaint. "The [Court of Justice of the European Union] has made it clear that data transfers to the U.S. on the basis of the Privacy Shield are no longer permitted. If the world's leading cloud company and largest e-commerce provider remains inactive for more than two months and ignores consumer rights, that is unacceptable," said Mr Hermann, head of Europäische Gesellschaft für Datenschutz (EuGD). Moreover, the founder of EuGD, Thomas Bindl, said that taking the legal route was a decision made taking into consideration similar conflicts.
Despite the noise and controversy surrounding the conflict and impending lawsuit, it is still necessary to wait and see the developments in court. However, regardless of the result in the ruling, this will likely inspire greater vigilance and compliance on the part of companies who may also be transferring data out of Europe.
Do you make international data transfers to third countries? Are you affected by Schrems II decision? We can help you. Aphaia provides both GDPR and Data Protection Act 2018 consultancy services, including data protection impact assessments, and Data Protection Officer outsourcing. We also offer CCPA compliance services. Contact us today.
AI Data Protection Zandilli Lucien September 30, 2020
Amazon launches new technology which scans palms for identification and payment.
Amazon launches new technology in two of its physical stores, which allow for contact free identification and payment, by scanning an individual's palm.
Amazon is on the verge of launching a new biometric payment system which scans an image of customers' palms, according to this new BBC article. This new methodology is an attempt at a contactless replacement of traditional membership and physical loyalty cards. The accuracy and unique identifiers lie within the vein patterns in the hands of individuals, which still remain fairly inconspicuous to the naked eye. These scanners would require the customer to wave their palm a few inches away from a scanner making it a viable contactless form of ID/payment simultaneously. The system is currently being tested at two Amazon stores. Physical bills and data will be stored locally at the stores, but will not be sent to Amazon data centers, and clients will be allowed to delete the data from their website.
Amazon developers think this technology is safer and more secure than other methods of biometric identification.
The application seems to be as accurate and effective as fingerprints, but not as easily identifiable by human vision, and therefore presumably more difficult to replicate. Amazon developers claim it is more secure than other forms of biometrics, which is especially relevant after issues with racial bias have been shown in the company's facial recognition software that has currently been suspended by officials. Recently, we published an article on The National Biometric Information Privacy Act, which was introduced into US congress. Bills like these are an attempt to curtail any negative effects or security breaches that may arise in using biometric scanners and similar technology.
While this technology is convenient, some point to possible data security risks.
In the midst of the pandemic, the introduction of a new payment method requiring less human interaction, and no physical contact seems like a much needed innovation, however some groups are advocating against biometric forms of ID and payments due to the possible privacy issues associated with biometric data being stored by governments or large corporations. Director of the privacy rights groups Big Brother Watch, Silkie Carlo says that this new technology is invasive, unnecessary and provides just another outlet for Amazon to cultivate personal data freely despite privacy laws and agreements.
The convenience of biometrics is not overshadowed by the possible invasion of privacy it risks, as a direct consequence. The implementation of these scanners in many different buildings is being discussed if this initial trial in Seattle locations goes well. This technology is a part of Amazon's vision of a non human staffed supermarket, where everything is tracked by AI and machines in the store and payment can be completed using this new palm scanner for a full contactless experience.
What does the GDPR say about this type of data processing?
The scans being picked up by these machines fall under biometric data, the processing of which is prohibited, under the GDPR, unless certain conditions are met. When processing biometric data, unless at least one of those conditions are met, the processing is deemed unlawful. Article 9 of the GDPR dictates that one of the following criteria must be met in order for the processing of biometric data;
Explicit consent to process that personal data has been given by the data subject for one or more specified purposes, except in instances where union on member state laws prevent the prohibition from being lifted by the data subject.
Processing the biometric data is necessary for the purposes of fulfilling obligations or exercising specific rights of the controller or the data subject in the field of employment, social security or social protection law.
The processing is necessary to protect the vital interests of the data subject or another natural person if the data subject is physically or legally incapable of giving consent.
The processing of biometric data is carried out in the course of its legitimate activities with appropriate safeguards by a foundation, association or any other not-for-profit body, on condition that the processing relates only to members or former members of the body, or with a person's in regular contact with the body, in connection with its aim or purposes related to political philosophical religious or trade unionism.
The processing is relating to personal data which is manifestly made public by the data subject.
The processing is necessary for the establishment, exercise or defence of legal claims.
The processing is necessary for reasons of substantial public interest, including in the area of public health.
The processing is necessary for the purposes of private or occupational medicine.
The processing is necessary for archiving purposes in the public interest, whether scientific, historical or statistical purposes.
For more clarity on what is classified as biometric data as well as other aspects of this technology, check out our post; 14 common misconceptions on biometric identification and authentication debunked.
Does your company process biometric identification data? Aphaia provides a number of services in relation to compliance with regard to data protection, including regarding biometric data: data protection impact assessments, Data Protection Officer outsourcing, and EU AI ethics assessments. Get in touch today to find out more.
AI ePrivacy Zandilli Lucien August 28, 2020
Lincolnshire Police Trial CCTV: this technology can even detect moods!
Lincolnshire police trial CCTV technology which can detect moods, eyewear and headgear, but not before a human rights and privacy assessment is carried out.
Lincolnshire police will soon debut their trial of CCTV cameras in Gainsborough. This is a new, more complicated and potentially controversial type of Surveillance technology. Although the funding for this project has been approved and received, due to privacy concerns surrounding the use of this technology, the implementation of the new equipment is at a standstill. Key legal considerations need to be made before this could be released and used in the general public, as this technology has the ability to search for persons using parameters surrounding their mood, or their apparel such as hats or sunglasses. Due to the fact that the police have full control of the search parameters; the technology is inherently problematic, as was in case of court rulings as recently as 2018.
A Welsh national had brought a legal case against the authorities for their use of a very similar facial recognition technology, and this has raised the specter of many ideological and privacy concerns when it comes to the Police having unquestionable access to intrusive means of surveillance, and monitoring persons who may not be suspected or involved in any crimes. Although Mr. Bridges did not have instant success with his claim, as his first petition to the High Court was denied, in his subsequent Court of Appeal claim; three out of five of the unconstitutional breaches of privacy Mr. Bridges presented were ratified as legally valid in the court.
The police have acknowledged, and made attempts at addressing the public's privacy concerns regarding the use of this technology.
Privacy concerns are a very important consideration prior to the establishment of this new technology for everyday use. The police have tried to give some assurance to the public that their rights are of paramount importance in the means and the protocols surrounding this technology and how it is used. The local police have also released some preliminary information which may ease public anxiety around the implementation of this technology; the scans are not being done in live time and also, all footage is deleted after 31 days.
Legislation continues to be introduced regarding privacy and surveillance.
There are also larger debates surrounding the appropriate search terms allowed and under what circumstances they can be implemented in a situation where this new surveillance technology is to be in use. Legislation around government surveillance also has seen changes in recent years since the Ed Bridges case, and it continues to be reformed, in an attempt to encompass everyone's well-being without stripping them of the fundamental privacies and rights allotted to them.
According to Cristina Contero Almagro, partner at Aphaia, 'The risk is twofold: first, the police using the technology without the appropriate safeguards and second, the information being compromised and used maliciously by third-parties which may access it unlawfully. Considering the nature of the data involved, it is essential to put in place strong security measures which ensure the data will be adequately protected. It is important to note that once that biometric information has been exposed, the damage to the rights and freedoms of the affected data subjects is incalculable, as it is not something that can be changed like a password'.
'Any facial recognition that includes profiling should be viewed with suspicion,' comments Dr Bostjan Makarovic, Aphaia's Managing Partner. 'The challenge is that there is no way to object to such profiling because it takes place in real time as one enters a certain area. Law enforcement and public safety are important reasons but should not be used as a blanket justification without further impact assessment.'
Does your company utilize facial recognition or process other biometric data? If yes, failure to adhere fully to the guidelines and rules of the GDPR and Data Protection Act 2018 could result in a hefty financial penalty. Aphaia provides both GDPR adaptation consultancy services and CCPA compliance, including data protection impact assessments, EU AI Ethics assessments and Data Protection Officer outsourcing. Contact us today.
Data Protection GDPR IT law Zandilli Lucien August 26, 2020
Twitter Data Case Dispute: European Union privacy regulators conflicted over how much, or whether—to fine, over last year's data breach.
Twitter Data Case Dispute between European Union privacy regulators, causing delay in the progress of the most advanced cross-border privacy case involving a U.S. tech company under the GDPR.
The Twitter data case dispute, disclosed in a statement from Ireland's Data Protection Commission, is one of the first major tests for enforcement of the GDPR. This has raised concern over other possible disagreements and delays in nearly two dozen other investigations into Facebook, Google, and other U.S. tech companies. This particular case concerns a security hole that Twitter claimed to have fixed in January 2019, which exposed the private tweets of some users, over a period of over four years.
This Twitter case dispute will be an early indication of how similar situations of power sharing among EU regulators.
The outcome of this Twitter case will be an early indication of how the EU's power-sharing system among regulators will work in practice. Because Twitter has regional headquarters in Ireland, the investigation is led by Ireland's data commission. However, cases can be objected to, by regulators in any of the 26 other EU countries involved. Under the GDPR, in cases that involve multiple countries, the lead regulator (in this case Ireland's data commission), sends its draft decision to counterparts. They then have four weeks to submit objections, then there is additional time left to approve revisions based on those objections. Any disagreements the regulators can't resolve can be referred to the European Data Protection Board, which decides by way of a vote. Once the board approves a decision, the lead regulator will inform the company of that decision within a month. The voting process can take from a month to two, or two and a half months, depending on whether extensions are granted.
After consultations, with other EU authorities, there remained a number of objections, triggering the first ever dispute resolution.
The Irish privacy regulator mentioned that it had triggered a dispute-resolution mechanism among the bloc's privacy regulators due to a failure to resolve disagreements over its draft decision in the Twitter case. This is the first time that process has been triggered. Ireland's data commission forwarded a draft decision to its counterparts for comments in May.
The commission engaged in consultations with other regulators to resolve their complaints. Graham Doyle, a deputy commissioner said that despite the consultation, a number of objections remained and the matter has now been referred to the European Data Protection Board, by the Data Protection Commission.
Under the GDPR, companies can be charged a sliding scale up to 2% of their annual revenue, considering various factors for this type of violation.
Ireland's data commission said that the focus of this case is on whether Twitter met its obligation for a timely notification of the data breach. Under the GDPR, regulators can fine companies up to 2% of their world-wide annual revenue for failing to notify them of a data breach within 72 hours. This could amount to up to $69 million, based on Twitter's 2019 revenue. However, this legislation also directs regulators to take into account the gravity and duration of the violation, the type of personal information involved, and also other factors, like whether the violation was intentional or not. This opens up lots of room for disagreement between regulators on how much should be charged for violations.
Does your company have all of the mandated safeguards in place to ensure compliance with the GDPR and Data Protection Act 2018? Aphaia provides both GDPR and Data Protection Act 2018 consultancy services, including data protection impact assessments, and Data Protection Officer outsourcing. We can help your company get on track towards full compliance. Contact us today.
Aphaia Newsletter: start your month informed
Spanish DPA AEPD publishes Guidelines on AI audits
Doctors fined by CNIL: The French DPA has sanctioned two health professionals over poor data protection.
UK treaty with EU: This agreement will allow an extended period for personal data flows.
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