Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,738
datasets available to search
ShareScore release 0.9.0
Dataset results
5,738 results for “Standards”
MEGAN 6.15.2 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>MEGAN<br> <strong>SoftwareVersion: </strong>6.15.2<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/husonlab/megan-ce<br> <strong>ReferenceDatabase:</strong> NCBI nr 2019-04-17<br> <strong>Taxonomy:</strong> NCBI 2019-04-17<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> diamond makedb --in nr.faa.gz --db nr_diamond0.9.24 --taxonmap prot.accession2taxid.gz --taxonnodes nodes.dmp<br> diamond blastx -d nr_diamond0.9.24 -q anonymous_gsa_pooled.fasta -a result -t tmp --index-chunks 1<br> megan/tools/daa2rma -i result.daa -o 19122017_mousegut_scaffolds.rma --acc2taxa megan/prot_acc2tax-Nov2018X1.abin<br> #final bins extracted with MEGAN's graphical interface tool</p>
DIAMOND 0.9.24 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>DIAMOND<br> <strong>SoftwareVersion: </strong>0.9.24<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/bbuchfink/diamond<br> <strong>ReferenceDatabase:</strong> NCBI nr 2019-04-17<br> <strong>Taxonomy:</strong> NCBI 2019-04-17<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> diamond makedb --in nr.faa.gz --db nr --taxonmap prot.accession2taxid.gz --taxonnodes nodes.dmp<br> diamond blastx --query anonymous_gsa_pooled.fasta --db nr --outfmt 102 --out 19122017_mousegut_scaffolds.out --block-size 40 --index-chunks 1 --tmpdir tmp</p>
MaxBin 2.2.7 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly
Genome binning of the gold standard pooled assembly <br><strong>Software: </strong>MaxBin<br><strong>SoftwareVersion: </strong>2.2.7<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://sourceforge.net/projects/maxbin/<br><strong>DockerImage:</strong> cami/maxbin:2.2.7<br><strong>IsBiobox:</strong> No<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> run_MaxBin.pl -thread 16 -contig anonymous_gsa_pooled.fasta -out output -reads sample_0/reads/anonymous_reads.fq -reads2 sample_1/reads/anonymous_reads.fq -reads3 sample_2/reads/anonymous_reads.fq -reads4 sample_3/reads/anonymous_reads.fq -reads5 sample_4/reads/anonymous_reads.fq -reads6 sample_5/reads/anonymous_reads.fq -reads7 sample_6/reads/anonymous_reads.fq -reads8 sample_7/reads/anonymous_reads.fq -reads9 sample_8/reads/anonymous_reads.fq -reads10 sample_9/reads/anonymous_reads.fq -reads11 sample_10/reads/anonymous_reads.fq -reads12 sample_11/reads/anonymous_reads.fq -reads13 sample_12/reads/anonymous_reads.fq -reads14 sample_13/reads/anonymous_reads.fq -reads15 sample_14/reads/anonymous_reads.fq -reads16 sample_15/reads/anonymous_reads.fq -reads17 sample_16/reads/anonymous_reads.fq -reads18 sample_17/reads/anonymous_reads.fq -reads19 sample_18/reads/anonymous_reads.fq -reads20 sample_19/reads/anonymous_reads.fq -reads21 sample_20/reads/anonymous_reads.fq -reads22 sample_21/reads/anonymous_reads.fq -reads23 sample_22/reads/anonymous_reads.fq -reads24 sample_23/reads/anonymous_reads.fq -reads25 sample_24/reads/anonymous_reads.fq -reads26 sample_25/reads/anonymous_reads.fq -reads27 sample_26/reads/anonymous_reads.fq -reads28 sample_27/reads/anonymous_reads.fq -reads29 sample_28/reads/anonymous_reads.fq -reads30 sample_29/reads/anonymous_reads.fq -reads31 sample_30/reads/anonymous_reads.fq -reads32 sample_31/reads/anonymous_reads.fq -reads33 sample_32/reads/anonymous_reads.fq -reads34 sample_33/reads/anonymous_reads.fq -reads35 sample_34/reads/anonymous_reads.fq -reads36 sample_35/reads/anonymous_reads.fq -reads37 sample_36/reads/anonymous_reads.fq -reads38 sample_37/reads/anonymous_reads.fq -reads39 sample_38/reads/anonymous_reads.fq -reads40 sample_39/reads/anonymous_reads.fq -reads41 sample_40/reads/anonymous_reads.fq -reads42 sample_41/reads/anonymous_reads.fq -reads43 sample_42/reads/anonymous_reads.fq -reads44 sample_43/reads/anonymous_reads.fq -reads45 sample_44/reads/anonymous_reads.fq -reads46 sample_45/reads/anonymous_reads.fq -reads47 sample_46/reads/anonymous_reads.fq -reads48 sample_47/reads/anonymous_reads.fq -reads49 sample_48/reads/anonymous_reads.fq -reads50 sample_49/reads/anonymous_reads.fq -reads51 sample_50/reads/anonymous_reads.fq -reads52 sample_51/reads/anonymous_reads.fq -reads53 sample_52/reads/anonymous_reads.fq -reads54 sample_53/reads/anonymous_reads.fq -reads55 sample_54/reads/anonymous_reads.fq -reads56 sample_55/reads/anonymous_reads.fq -reads57 sample_56/reads/anonymous_reads.fq -reads58 sample_57/reads/anonymous_reads.fq -reads59 sample_58/reads/anonymous_reads.fq -reads60 sample_59/reads/anonymous_reads.fq -reads61 sample_60/reads/anonymous_reads.fq -reads62 sample_61/reads/anonymous_reads.fq -reads63 sample_62/reads/anonymous_reads.fq -reads64 sample_63/reads/anonymous_reads.fq
Neutron powder diffraction from NIST 660c LaB6 standard for calibration of Wombat high-intensity neutron powder diffractometer
<p>Calibration data for the Wombat high intensity powder diffractometer, ANSTO, collected at a nominal wavelength of 1.64A from NIST 660c LaB<sub>6</sub> standard using the [224] reflection from a single crystal Ge monochromator, [335] cut. Note that B in this standard is enriched to 99% with isotope <sup>11</sup>B. The sample was loaded in a 6mm diameter vanadium cylindrical sample can.</p>
FIGURE 1. Analogy between a in The need for standard protocols in bioregionalisation: Comments on "The spectre of biogeographical regionalization" by Morrone (2018)
FIGURE 1. Analogy between a systematic (A-C) and biogeographic analysis (C-E) from the data to knowledge levels. A: homology hypothesis grouping morpho-anatomical features. B: a character (mixed struture) grouping taxa bearing the features. C: a phylogeny: (result of the congruence analysis). C: a phylogeny standing for a biogeographic homology hypothesis about distributions of taxa. D. a biogeographic character grouping the areas of endemism (X, Y, Z) in which the taxa occur. E: an areagram. (figure modified after Grand, 2013).
Supplementary material 2 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Use cases were collected using a number of methods to maximise responses. Lead authors of papers published using data accessed via the Atlas of Living Australia (ALA) were contacted and asked to contribute their research data use cases, and a number of papers describing fitness for use determination were sent to the ALA Data Quality group. Fitness for use and quality check information from these papers were extracted and transferred to the use case library. These are the results of those surveys.
Supplementary material 4 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Description and specifications for the tests following the conventions of the Fitness For Use Framework. This supplement is a copy of https://github.com/tdwg/bdq/blob/master/tg2/core/TG2_tests.csv as of commit 941e774 2019-Aug-20.
Supplementary material 3 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Counts of occurrence records in 2019-04-15 snapshot of GBIF-mediated data that fit the three categories of expected responses for each of the event date-related validation tests.
Supplementary material 1 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Vocabulary of Terms used for the TDWG Task Group on Data Quality Tests and Assertions, plus key additional terms from the Use Case Study. Ther terms are consistent with the terms used in the Fitness for Use Framework (Veiga et al. 2017)
An Analysis of the OASIS OSLC Integration Standard - A Survey
<p>OASIS OSLC is a standard that targets the integration of engineering software applications. Its approach promotes loose coupling, in which each application autonomously manages its own product data, while providing RESTful web services through which other applications can interact.</p> <p>To achieve this, a review of the current state of the OASIS OSLC integration standard is provided in terms of its market penetration in commercial applications, its capabilities, and the architectural qualities of OSLC-based solutions. This review is based on a survey of commercial software applications that provide some support for OSLC capabilities.</p>
CodiEsp Silver Standard: Participant predictions in eHealth CLEF2020 - Spanish clinical cases coded in ICD10 (CIE10)
<p><strong>Introduction</strong></p> <p>Predictions in the background set of <a href="https://temu.bsc.es/codiesp/">eHealth CLEF 2020 Task 1</a> participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per CodiEsp subtask. Within each CodiEsp subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong><br> The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks CodiEsp-Diagnostic and CodiEsp-Procedure, the file files have the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X (explainability) contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://zenodo.org/record/3837305#.X7T9KVlKg5k">Gold Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to CodiEsp participants</p>
DETERMINANTS OF ADHERENCE TO STANDARD PRECAUTIONS FOR THE PREVENTION OF BLOODBORNE INFECTIONS AMONG MEDICAL LABORATORY SCIENTISTS IN LAGOS STATE, NIGERIA
<p>DETERMINANTS OF ADHERENCEL TO STANDARD PRECAUTIONS FOR THE PREVENTIONOF BOODBORNE INFECTIONS AMONG MEDICAL LABORATORY SCIENTISTS IN LAGOS STATE, NIGERIA</p>
standard with together) corner left bottom (analysis genetic the in included species among bold gene in shown oxidase-I are SE cytochrome and species the within at) % (divergence divergence sequence average The . pairwise) corner showing right upper (Matrix) %;. SE 4 ABLE (T error in Description of a new species of the Rhinolophus trifoliatus-group (Chiroptera: Rhinolophidae) from Southeast Asia
standard with together) corner left bottom (analysis genetic the in included species among bold gene in shown oxidase-I are SE cytochrome and species the within at) % (divergence divergence sequence average The . pairwise) corner showing right upper (Matrix) %;. SE 4 ABLE (T error
MESINESP: Post-workshop datasets. Silver Standard and annotator records
<p><em><strong>Please use the <a href="https://doi.org/10.5281/zenodo.4612274">MESINESP2 corpus (the second edition of the shared-task)</a> since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials).</strong></em></p> <p>The MESINESP (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) Challenge was held in May-June 2020, and as a result of a strong participation and the manual annotation of an evaluation dataset, two additional datasets are released now:</p> <p>1) "all_annotations_withIDsv3.tsv" contains a tab-separated file with all manual annotations (both validated and non-validated) of the evaluation dataset prepared for the competition. It contains the following fields:</p> <ul> <li>annotatorName: Human annotator id</li> <li>documentId: Document ID in the source database</li> <li> decsCode: A DeCS code added to it or validated</li> <li>timestamp: When it was added</li> <li>validated: if it was validated at that point by another annotator, or not yet</li> <li>SpanishTerm: The Spanish descriptor corresponding to the DeCS code</li> <li>mesinespId: The internal document id in the distributed evaluation file</li> <li>dataset: if part of the evaluation or the test sets</li> <li>source: which database it was taken from</li> </ul> <p><em><strong>Example:</strong></em></p> <p><strong>annotatorName documentId decsCode timestamp validated SpanishTerm mesinespId dataset source</strong><br> A7 biblio-1001069 6893 2020-01-17T11:27:07.000Z false caballos mesinesp-dev-671 dev LILACS<br> A7 biblio-1001069 4345 2020-01-17T11:27:12.000Z false perros mesinesp-dev-671 dev LILACS</p> <p> </p> <p>2) A "Silver Standard" created from the 24 system runs submitted by 6 participating teams. It contains each of the submitted DeCS code for each document in the test set, as well as other information that can help ascertain reliability and source for anyone that wants to use this dataset to enrich their training data. It contains more that 5.8 million datapoints, and is structured as follows</p> <ul> <li>SubmissionName: Alias of the team that submitted the run</li> <li>REALdocumentId: The real id of the document</li> <li>mesinespId: The mesinesp assigned id in the evaluation dataset</li> <li>docSource: The source database</li> <li>decsCode: the DeCS code assigned to it by the team's system</li> <li>SpanishTerm: The Spanish descriptor of the DeCS code</li> <li>MiF: The Micro-f1 scored by that system's run</li> <li>MiR: The Micro-Recall scored by that system's run</li> <li>MiP: The Micro-Precision scored by that system's run </li> <li>Acc: The Accuracy scored by that system's run</li> <li>consensus: The number of runs where that DeCS code was assigned to this document by the participating teams (max. is 24)</li> </ul> <p><strong><em>Example:</em></strong></p> <p><strong>SubmissionName REALdocumentId mesinespId docSource decsCode SpanishTerm MiF MiR MiP Acc consensus</strong><br> AN ibc-177565 mesinesp-evaluation-00001 IBECS 28567 riesgo 0.2054 0.1930 0.2196 0.1198 4<br> AN ibc-177565 mesinesp-evaluation-00001 IBECS 15335 trabajo 0.2054 0.1930 0.2196 0.1198 4<br> AN ibc-177565 mesinesp-evaluation-00001 IBECS 33182 conocimiento 0.2054 0.1930 0.2196 0.1198 7<br> </p> <p><strong>For citation and a detailed description of the Challenge, please cite:</strong><br> <em>Anastasios, Nentidis and Anastasia, Krithara and Konstantinos, Bougiatiotis and Martin, Krallinger and Carlos, Rodriguez-Penagos and Marta, Villegas and Georgios, Paliouras.</em> <strong>Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering</strong> (2020). Proceedings of the Eleventh International Conference of the CLEF Association (CLEF 2020). Thessaloniki, Greece, September 22--25</p> <p><strong>Citation</strong></p> <p>@inproceedings{durusan2019overview,<br> title={Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering},<br> author={Anastasios, Nentidis and Anastasia, Krithara and Konstantinos, Bougiatiotis and Martin, Krallinger and Carlos, Rodriguez-Penagos and Marta, Villegas and Georgios, Paliouras},<br> booktitle={Experimental IR Meets Multilinguality, Multimodality, and Interaction Proceedings of the Eleventh International Conference of the CLEF Association (CLEF 2020), Thessaloniki, Greece, September 22--25, 2020, Proceedings},<br> volume={12260},<br> year={2020},<br> organization={Springer}<br> }</p> <p> </p> <p>Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology
<p><strong>Intro:</strong></p> <p>Cantemist shared task dataset (divided in train, dev1, dev2 and test). In addition, we include here the Cantemist background set.</p> <p>It contains the train, development and test sets of the three subtasks: cantemist-ner, cantemist-norm and cantemist-coding with Gold Standard annotations.</p> <p>In addition, it contains the documents of the background set, without annotations.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</p> <pre><code>@inproceedings{miranda2020named, title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results}, author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M}, booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>Format:</strong></p> <p>For subtasks cantemist-norm and cantemist-ner, annotations are distributed in Brat format. See <a href="https://brat.nlplab.org/standoff.html">Brat webpage</a> for more information</p> <p>For subtask cantemist-coding, codes are grouped in a TSV file with the following columns (this follows the format used in <a href="https://temu.bsc.es/codiesp/">CodiEsp shared task</a>): </p> <blockquote> <p>filename code</p> </blockquote> <p> </p> <p><strong>Shared task goal:</strong></p> <p>In the three subtasks, the goal will be to predict the annotations (either the ANN files or the TSV with the codes) given only the plain text files. </p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/cantemist/">Web</a></strong></li> <li><strong><a href="http://ceur-ws.org/Vol-2664/cantemist_overview.pdf">Citation</a>: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://doi.org/10.5281/zenodo.4010899">Silver Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>For further information, please visit <a href="https://temu.bsc.es/cantemist/">https://temu.bsc.es/cantemist/</a> or email us at encargo-pln-life@bsc.es</p>
Data from: The standard metabolic rate of a land snail (Cepaea hortensis) is a repeatable trait and influences winter survival
<p>Phenotypic selection on physiological parameters is an underrepresented topic in studies of evolutionary biology. There is especially a lack of studies involving invertebrate organisms. We studied the repeatability of the standard metabolic rate (SMR) and the effect of individual variation in SMR on the subsequent winter survival in a terrestrial shell-bearing mollusc, the white-lipped snail (<i>Cepaea hortensis</i>) in mid-Norway. SMR was measured twice during the autumn and – after an experimental overwintering at controlled conditions – twice during the following spring. We found a significant repeatability of SMR over all three time periods tested, with a clear effect of time, with a high repeatability of 0.56 over 4 days during spring, 0.44 over 12 days in the autumn and 0.17 over 194 days from autumn to spring. That SMR is a repeatable physiological trait across the winter period during which a possible selection might occur, suggests that SMR could be a potential target of natural selection. We indeed found that the autumn SMR significantly influenced the probability of survival during the winter period, with a combination of a positive linear (P = 0.011) and a quadratic stabilizing (P = 0.001) effect on SMR. Our results hence support the view that metabolic rate is an important physiological component influencing the fitness of an organism.</p>
Connectivity-based localization of human hypothalamic nuclei in functional images of standard voxel size
<p>Despite their critical roles in autonomic functions, individual hypothalamic nuclei have not been extensively investigated in humans using functional magnetic resonance imaging, partly due to the difficulty in resolving individual nuclei contained in the small structure of the hypothalamus. Areal parcellation analyses enable discrimination of individual hypothalamic nuclei but require a higher spatial resolution, which necessitates long scanning time or large amounts of data to compensate for the low signal-to-noise ratio in 3T or 1.5T scanners. In this study, we present analytic procedures to estimate likely locations of individual nuclei in the standard 2-mm resolution based on our higher resolution dataset. The spatial profiles of functional connectivity with the cerebral cortex for each nucleus in the medial hypothalamus were calculated using our higher resolution dataset. Voxels in the hypothalamus in standard resolution images from the Human Connectome Project (HCP) database that predominantly shared connectivity profiles with the same nucleus were subsequently identified. Voxels representing individual nuclei, as identified with the analytic procedures, were reproducible across 20 HCP datasets of 20 subjects each. Furthermore, the identified voxels were spatially separate. These results suggest that these analytic procedures are capable of refining voxels that represent individual hypothalamic nuclei in standard resolution. Our results highlight the potential utility of these procedures in various settings such as patient studies, where lengthy scans are infeasible.</p>
Metadata standards and tools practice at EPFL School of Life Sciences 2020 Survey
<p>In 2020, EPFL Library conducted a study about Tools and Metadata Standards practice in EPFL School of Life Sciences.</p> <p>By standard, we mean:<br> - terminological resources (vocabularies, terminologies, classifications, thesauri),<br> - formats and data models / schemas,<br> - structured knowledge bases (databases, reference databases, ontologies).<br> And by tools, we mean:<br> - bioinformatics software (i.e. for sequence or molecular structure analysis of proteins and genes)<br> - databases from the Life Sciences field (i.e. genome databases).</p> <p>Our goal was twofold: on the one hand, to gain new knowledge and insights, and on the other hand, to develop a reproducible survey methodology resolutely based on liaison librarian-data librarian collaboration.</p> <p>This dataset reflects the results collected during the second phase of the study: "Survey in EPFL Life Sciences Community".</p> <p> </p>
Cantemist Silver Standard: Participant predictions in SEPLN IberLEF2020 - Spanish oncology clinical cases coded in ICD-O
<p><strong>Introduction</strong></p> <p>Predictions in the background set of Cantemist participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per Cantemist subtask. Within each Cantemist subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong></p> <p>The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks Cantemist-NER and Cantemist-Norm, the files are in Brat format.</p> <p>For the sub-track Cantemist-Coding files have the following fields:</p> <pre>articleID ICDO-code </pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/cantemist/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><a href="https://doi.org/10.5281/zenodo.3773228"><strong>Gold Standard corpus</strong></a></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to Cantemist participants. </p> <p> </p> <p>For more information, visit the track webpage: <a href="http://temu.bsc.es/cantemist/">http://temu.bsc.es/cantemist/</a> or email us at encargo-pln-life@bsc.es</p>
Data from: Standardization and validation of a panel of cross-species microsatellites to individually identify the Asiatic wild dog (Cuon alpinus)
The Asiatic wild dog or dhole (Cuon alpinus) is a highly elusive, monophyletic, forest dwelling, social canid distributed across south and Southeast Asia. Severe pressures from habitat loss, prey depletion, disease, human persecution and interspecific competition resulted in global population decline in dholes. Despite a declining population trend, detailed information on population size, ecology, demography and genetics is lacking. Generating reliable information at landscape level for dholes is challenging due to their secretive behaviour and monomorphic physical features. Recent advances in non-invasive DNA-based tools can be used to monitor populations and individuals across large landscapes. In this paper, we describe standardization and validation of faecal DNA-based methods for individual identification of dholes. We tested this method on 249 field-collected dhole faeces from five protected areas of the central Indian landscape in the state of Maharashtra, India. Results We tested a total of 18 cross-species markers and developed a panel of 12 markers for unambiguous individual identification of dholes. This marker panel identified 101 unique individuals from faecal samples collected across our pilot field study area. These loci showed varied level of amplification success (57-88%), polymorphism (3-9 alleles), heterozygosity (0.23-0.63) and produced a cumulative misidentification rate or PID(unbiased) and PID(sibs) value of 4.7x10-10 and 1.5x10-4, respectively, indicating a high statistical power in individual discrimination from poor quality samples. Conclusion Our results demonstrated that the selected panel of 12 microsatellite loci can conclusively identify dholes from poor quality, non-invasive biological samples and help in exploring various population parameters. This genetic approach would be useful in dhole population estimation across its range and will help in assessing population trends and other genetic parameters for this elusive, social carnivore.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.