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Fig. 2.20 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.20. Overview of the digitised specimen with the ZooSphere setup, visible at Zoosphere.net. Image copyright MfN.
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized. in A new species of freshwater Chaetonotidae (Gastrotricha, Chaetonotida) from Obodska Cave (Montenegro) based on morphological and molecular characters
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized.
Standard Distributions of Blood Tests for Public Use
<p>This dataset is the distribution parameters obtained by approximating the distribution of blood test values taken from inpatients in a Japanese hospital by a lognormal distribution or an exponential distribution. </p> <p>The parameters P1, P2 and P3 of the lognormal distribution are used in the following equation.<br> P1*exp(-(ln(x/P2)/P3)^2)</p> <p>The parameters P1 and P2 of the exponential distribution are used in the following.<br> P1*exp(-P2*x)</p> <p>This file is CSV with UTF-8 charset, and the columns are as follows.</p> <p>---<br> Test name<br> Test name in Japanese<br> Unit of the value<br> Distribution type (LogNormal or Exp)<br> P1<br> P2<br> P3<br> P1 Error(1sigma)<br> P2 Error(1sigma)<br> P3 Error(1sigma)<br> Number of data points used to make this distribution<br> Bin width used to make the distribution<br> ---</p> <p>Any researchers can use this dataset without privacy issues.<br> CC BY-NC 4.0<br> </p>
Data for Measuring the Market Impact of New Auditing Standards in China
<p>Data used in a study of the market impact of new auditing standards in China. The data includes 76 companies listed on the A+H share markets that were subject to the new auditing standards in 2017. It also includes 76 companies listed on the A share market which were matched with the experimental sample using the Propensity-Score Matching method. Files include 2016 & 2017 cumulative abnormal returns (CAR) and volatility (Vol) for the experimental group (A+H shares) and the control group (A shares) and control variables for both groups.</p> <p> </p> <p> </p>
DATASET OF "Impact of short-acting vs. standard anaesthetic agents on obstructive sleep apnoea: a randomised, controlled, triple-blind trial"
<p>Sleep apnoea is associated with negative outcomes following general anaesthesia. Current recommendations suggest using short-acting anaesthetic agents in preference to standard agents to reduce this risk, but there is currently no evidence to support this. This randomised controlled triple-blind trial tested the hypothesis that a combination of short-acting agents (desflurane-remifentanil) would reduce the postoperative impact of general anaesthesia on sleep apnoea severity compared with standard agents (sevoflurane-fentanyl). Sixty patients undergoing hip arthroplasty under general anaesthesia were randomised to anaesthesia with desflurane-remifentanil or sevoflurane-fentanyl. Respiratory polygraphy was performed before surgery and on the first and third postoperative nights. The primary outcome was the supine apnoea-hypopnoea index on the first postoperative night. Secondary outcomes were the supine apnoea-hypopnoea index on the third postoperative night, and the oxygen desaturation index on the first and third postoperative nights. Additional outcomes included intravenous morphine equivalent consumption and pain scores on postoperative days 1, 2 and 3. Pre-operative sleep study data were similar between groups. Mean (95%CI) values for the supine apnoea-hypopnoea index on the first postoperative night were 18.9 (12.7–25.0) and 21.4 (14.2–28.7) events.h<sup>-1</sup>, respectively, in the short-acting and standard anaesthesia groups (p=0.64). Corresponding values on the third postoperative night were 28.1 (15.8–40.3) and 38.0 (18.3–57.6) events.h<sup>-1</sup> (p=0.34). Secondary sleep- and pain-related outcomes were generally similar in the two groups. In conclusion, short-acting anaesthetic agents did not reduce the impact of general anaesthesia on sleep apnoea severity compared with standard agents. These data should prompt an update of current recommendations.</p>
Data release - A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr
<p>Data release accompanying the manuscript</p> <p> "<strong>A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr</strong>" - <a href="https://arxiv.org/abs/2009.14057">Chen et al. (2020)</a></p> <p>assuming an association between the LIGO-Virgo gravitational wave signal <a href="https://www.gw-openscience.org/eventapi/html/O3_Discovery_Papers/GW190521/">GW190521</a> and the electromagnetic signal ZTF19abanrhr as identified by <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.251102">Graham et al 2020</a>.</p> <p>The posterior samples for the GW analyses are available from <a href="https://doi.org/10.5281/zenodo.4057130">Isi (2020)</a> and <a href="https://dcc.ligo.org/LIGO-P2000158/public">LVC (2020)</a> respectively.</p>
How Software Organizations are using the ISO/IEC 29110 Standard's Processes: A Survey of the State of the Art and Practice
<p>ISO/IEC 29110 was developed containing a set of industrially validated practices that can potentially be adopted by software Very Small Entities (VSE). VSEs usually have characteristics that differentiate them from organizations of different sizes, such as extremely limited resources and informal project management processes, tending to adopt Agile methods and having an historical resistance to the adoption of standards, that are in general developed for large organizations. In this sense, our research question arises: "How are software organizations using the ISO/IEC 29110 practices?". To answer this question, a Systematic Mapping Study (SM), and a Survey with software organizations were carried out in order to identify the state of the art and the state of the practice in relation to the use of the standard’s practices. The SM identified 21 primary studies reporting the use of the standard in hundreds of software organizations with positive results such as organizational learning, process improvement, improved communication, and also some negative results, such as deployment difficulties in technical areas and the need for additional time and resources. The Survey carried out with 23 software companies identified that, in general, companies do not explicitly know the content of the standard, but partially carry out, in accordance with the standard, practices related to planning, monitoring, control and execution of a project plan, and do not carry out requirements analysis or architecture and detailed design as defined in the standard. </p>
PharmaCoNER corpus: gold standard annotations of Pharmacological Substances, Compounds and proteins in Spanish clinical case reports
<p><strong>Intro:</strong></p><p>The PharmaCoNER corpus (divided into train, dev and test) is a Gold Standard manually annotated dataset used for the the PharmaCoNER shared task posed at BIONLP-ST (at EMNLP). In addition, we include here the PharmaCoNER background set. It contains the train, development and test sets of the two subtasks (subtask-1 and subtask-2) with Gold Standard annotations. In addition, it contains the documents of the background set, without annotations.</p><p>The PharmaCoNER corpus consists of:</p><ul><li>Manually classified clinical case sections derived from Open access Spanish medical publications, named the Spanish Clinical Case Corpus (SPACCC).</li><li>It was manually selected by a practicing oncologist and revised by a clinical documentalist to assure that records were relevant/representative and resembled structure and content relevant to process clinical records.</li><li>The final corpus: 1000 clinical cases 16,504 sentences</li><li>The corpus contains a total of 396,988 words, with an average of 396.2 words per clinical case.</li><li>It covers a range of medical disciplines including oncology, urology, cardiology, pneumology or infections diseases, etc.</li><li>The corpus has been annotated at the mention level by experts in medicinal chemistry and pharmacology following a granular annotation scheme covering four mention types:<ul><li><i>Entity type 1 (NORMALIZABLES)</i>: mentions of chemicals that can be manually normalized to a unique concept identifier (primarily SNOMED-CT).</li><li><i>Entity type 2 (NO_NORMALIZABLES)</i>: mentions of chemicals that could not be normalized manually to a unique concept identifier.</li><li><i>Entity type 3 (PROTEINAS)</i>: mentions of proteins/genes following an adaptation of the BioCreative GPRO track annotation guidelines (includes peptides, peptide hormones & antibodies).</li><li><i>Entity type 4 (UNCLEAR )</i>: cases of general substance class mentions of clinical relevance, including certain pharmaceutical formulations, general treatments, chemotherapy programs, and vaccines.</li><li>Mentions class "<i>UNCLEAR</i>" (not evaluated for the PharmaCoNER track)</li></ul></li></ul><p> </p><p> </p><p><strong>Please, cite: </strong></p><p>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</p><p> </p><p><strong>Annotation quality</strong></p><p>Inter-annotator agreement: 93% for annotation, 73% for mapping.</p><p>For more information, see the <a href="https://paperswithcode.com/paper/pharmaconer-pharmacological-substances">paper</a>.</p><p> </p><p><strong>Format</strong></p><p>For subtask 1 annotations are distributed in <i>Brat</i> format. (More info at Brat webpage https://brat.nlplab.org/standoff.html)</p><p>For subtask-2, codes are associated with each document are given in a <i>TSV</i> file with the following columns: </p><blockquote><p>filename code</p></blockquote><p> </p><p><strong>Shared task goal:</strong></p><p>In the two subtasks, the goal is to predict the annotations of the test files (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><a href="https://temu.bsc.es/pharmaconer/"><strong>Web</strong></a></li><li><a href="https://www.aclweb.org/anthology/D19-5701.pdf"><strong>Citation</strong></a><strong>: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li><li><a href="https://doi.org/10.5281/zenodo.4271908"><strong>Silver Standard corpus</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li><li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li><li><a href="https://www.youtube.com/watch?v=B3ZzJl5OMkY"><strong>Youtube video(general setting)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/pharmaconer-pharmacological-substances-compounds-and-proteins-named-entity-recognition-track-at-bionlpost-workshop-november-4-skycity-rm-2-hong-kong-emnlp2019"><strong>Slides PharmacoNER overview talk at BIONLP-ST / EMNLP </strong></a></li></ul><p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p><p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p><p> </p><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p> </p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p><br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in PharmaCoNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST</a> (Corpus of symptoms, signs and findings mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul>
PharmaCoNER Silver Standard: Participant predictions in PharmaCoNER shared task
<p><strong>Introduction</strong></p> <p>Predictions in the test and background set of PharmaCoNER participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per PharmaCoNER participant. Within each participant directory, there is one directory per run.</p> <p> </p> <p><strong>Corpus format description</strong></p> <p>For subtask 1 annotations are distributed in Brat format. See Brat webpage for more information <a href="https://brat.nlplab.org/standoff.html">https://brat.nlplab.org/standoff.html</a></p> <p>For subtask-2, codes are associated with each document are given in a TSV file with the following columns: </p> <pre>articleID code </pre> <p><br> <strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/pharmaconer/">Web</a></strong></li> <li><strong>Citation: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li> <li><a href="https://doi.org/10.5281/zenodo.4270157"><strong>Gold Standard corpus</strong></a></li> <li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li> <li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li> </ul> <p> </p> <p>All credit to PharmaCoNER participants. </p> <p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p> <p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p>
MEDDOCAN corpus: gold standard annotations for Medical Document Anonymization on Spanish clinical case reports
<p><strong>Intro:</strong></p> <p>Meddocan shared task dataset (divided in train, dev and test). In addition, we include here the Meddocan background set.</p> <p>It contains the training, development and test sets of the Meddocan shared task with Gold Standard annotations.</p> <p>In addition, it contains the documents of the background set, without annotations.</p> <p> </p> <p><strong>Annotation quality</strong></p> <p>Inter-annotator agreement: 98% </p> <p>For more information, see the <a href="http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf">paper</a>. </p> <p> </p> <p><strong>Format:</strong></p> <p>Annotations are distributed in Brat format. See <a href="https://brat.nlplab.org/standoff.html">Brat webpage</a> for more information.</p> <p>In addition, annotations are also distributed in XML format (based on i2b2 XML format).</p> <p>In the <a href="https://temu.bsc.es/meddocan/index.php/resources/">Meddocan webpage</a>, there is a script to convert between MEDDOCAN-Brat, MEDDOCAN-XML, and i2b2 formats.</p> <p> </p> <p><strong>Shared task goal:</strong></p> <p>In the three subtasks, the goal will be to predict the annotations given only the plain text files. </p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/meddocan/">Web</a></strong></li> <li><strong>Citation: </strong>Montserrat Marimon et al. “Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.” In: IberLEF@ SEPLN. 2019, pp. 618–638.</li> <li><strong>Silver Standard corpus</strong></li> <li><a href="https://doi.org/10.5281/zenodo.4279337"><strong>Annotation guidelines</strong></a></li> </ul> <p> </p> <p>For further information, please visit <a href="https://temu.bsc.es/meddocan/">https://temu.bsc.es/meddocan/</a> or email us at encargo-pln-life@bsc.es</p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital (SEAD)</p>
Profile Data for Stylus Device Calibration using Ball Standard
<p>X and Z profile data for the simulation of Stylus device calibration using Ball standard</p> <p>M1000: Number of profile points -20 means: 20 points at the left of the peak</p>
A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox
<p>Data set used in "A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox"</p>
silverCID — a silver standard corpus for Chemical-induced Diseases relation extraction — Edit
<p>silverCID — a silver standard corpus for Chemical-induced Diseases relation extraction — Edit</p>
Interaction data for standard tournament for the Axelrod Python project.
<p>This is the data from the tournament described here: http://axelrod-tournament.readthedocs.org/en/latest/standard/strategies.html</p> <p>A description of the format is available here: http://axelrod.readthedocs.org/en/latest/tutorials/further_topics/reading_and_writing_interactions.html</p>
Medical Relation Extraction Gold Standard with CrowdTruth
<p>The lack of annotated datasets for training and benchmarking is one of the main challenges of Clinical Natural Language Processing. In addition, current methods for collecting annotations attempt to minimize disagreement between annotators, and therefore fail to model the ambiguity inherent in language. We propose the <strong>CrowdTruth</strong> method for collecting medical ground truth through crowdsourcing, based on the observation that disagreement between annotators can signal ambiguity in the text, target semantics, or the worker's interpretation.</p> <p>This repository contains a dataset of 3,984 English sentences for medical relation extraction, centering on the cause and treat medical relations, that have been processed with CrowdTruth disagreement analytics to capture ambiguity. In addition, we provide the raw crowdsourcing data used to compile this ground truth, as well as the task templates used to collect the data on CrowdFlower.</p>
Standard Model effective field theory global fit using electroweak data
<p>These are files that can be used to reproduce the fit results in 10.1007/JHEP09(2016)157 and arXiv:1610.01783<strong>.</strong></p> <p> </p>
A rule based Tibetan part-of-speech (POS) tagger for the creation of gold standard training data
<p>This rule based Tibetan part-of-speech (POS) tagger was prepared in the course of the research project 'Tibetan in Digital Communication' (2012-2015) hosted at SOAS, University of London and funded by the UK's Arts and Humanities Research Council (grant code: AH/J00152X/1). For a description of the tag set see Garrett et al. 2014. and Garrett et al. 2015. For a description of the tagger itself see Garrett et al. 2014. Note that the tagger must be used together with a lexicon (for example Hill & Garrett 2017a). One must use one's own script to tag all words with all tags in the lexicon and then apply the tagger to remove incorrect tags.</p> <p>On the associated corpus of 318,230 words (Hill & Garrett 2017b) the lexical tagger (i.e. simply applying all available tags to all words) tags 141,911 words with the correct unique tag, achieves as accuracy of 1.000 (by definition getting the right tag among others for each word) with an ambiguity of 2.73111. In contrast, the Rule Tagger tags 241,256 words with the correct unique tag, achieves an accuracy of 0.99893 and an ambiguity of 1.38577.</p> <p>Because this tagger does not achieve ambiguity 1.000 it is not suitable for tagging large scale corpora, but instead is useful for the creation of gold standard training data.</p> <p>N.B. In some rare cases the tagger removes all POS-tags.</p>
Files and code for English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19
<p><span lang="EN-GB">Automated information extraction with natural language processing (NLP) tools is required to gain systematic insights from the large number of COVID-19 publications, reports and social media posts, which far exceed human processing capabilities. </span></p> <p><span lang="EN-GB">Here we present an NLP toolbox comprising COVID-19-related dictionaries and annotated corpora in English as well as useful code and workflows for their update and use. The dictionaries contain terms referring to the COVID-19 disease, the SARS-CoV-2 virus, its variants and common mutations, respectively. They were used together with the EasyNER NLP tool to extract and annotate all 764 398 abstracts in the CORD-19 dataset, creating a very large silver standard corpus (named Lund-Annotated-CORD-19 corpus). This was complemented with a small gold standard corpus consisting of PubMed abstracts manually annotated for key entity classes such as disease, virus, symptom, protein/gene, cell type, chemical and species terms. </span></p> <p><span lang="EN-GB">The toolbox can support various text analysis tasks related to COVID-19 such as named entity recognition and co-mention analysis. A preliminary version of the toolbox, which was released early in the pandemic, was</span><span lang="EN-GB"> for example already used to create a COVID-19 knowledge graph and study the evolution and variation of COVID-19-related terminology. In addition, the toolbox can be applied in the development of other NLP tools, for example to train and evaluate large language models.</span></p> <p><span lang="EN-GB">When using the toolbox, please cite this record and the associated article.</span></p> <p> </p> <p> </p>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
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.