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111 results for “gold standards”
Hackathon - TF-TG literature triage Gold Standard corpus
<p>This link contains the gold standard corpus for training the systems aiming at classifying or triaging the documents (PubMed abstracts) in relevant or not relevant for TF-TG relations. Note that this is not purely a curation triage, but we are primarily looking whether the abstracts do describe/characterize TF-TG interactions (positive training set labeled as “1” or they don't (negative training set labeled as “0”). </p> <p>This dataset was manually classified by domain experts into these to classes.</p> <p>Note that in PubMed there is a very considerable class imbalance, implying that there is only a very small fraction of TF-TG relations relevant literature subset. The resulting classifier should be useful to triage the entire PubMed database.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562939/files/greekc_triage_training_v02.tsv?download=1">greekc_triage_training_v02.tsv</a></p> </li> <li> <p>Example:</p> </li> </ul> <p>11731616 [{"sourceid":"11731616","sourcedb":"PubMed","text":"Homeobox protein Gsh-1-dependent regulation of the rat GHRH gene promoter. Although GHRH is known to play a pivotal role in the regulation of the GHRH-GH-IGF-I axis, the molecular mechanism of GHRH gene expression has not yet been examined. Here we studied the transcriptional regulation of the GHRH gene 5'promoter using an in vitro experimental model system. We especially focused on the role of homeobox transcriptional factor Gsh-1, because a dwarf phenotype and abolished GHRH expression was observed in Gsh-1 knockout mice. First, we cloned human Gsh-1, which showed 87.3% homology with mouse Gsh-1 at the nucleotide level. When the 5'-promoter region of the rat GHRH gene was introduced into the human placental cell line JEG-3, in which we found the endogenous expression of Gsh-1 as well as GHRH mRNA, substantial transcriptional activity of the promoter was recognized. Promoter activity was further enhanced by overexpression of Gsh-1 protein, whereas it was substantially reduced by elimination of Gsh-1 binding sites. EMSA confirmed the actual binding of Gsh-1 on the multiple binding sites of GHRH gene promoter. Finally, coexpression of CREB-binding protein significantly enhanced the Gsh-1-induced GHRH gene expression, suggesting the cooperative role of the coactivator protein. Because Gsh-1 is found to be expressed in the hypothalamus of the adult rat, our data provide evidence that the Gsh-1 homeobox protein plays a key role in the expression of the GHRH gene.","denotations":[{"obj":"Gene:29446","span":{"begin":84,"end":88}},{"obj":"Gene:288457","span":{"begin":17,"end":22}},{"obj":"Gene:29446","span":{"begin":55,"end":59}},{"obj":"Gene:29446","span":{"begin":146,"end":150}},{"obj":"Gene:29446","span":{"begin":193,"end":197}},{"obj":"Gene:29446","span":{"begin":295,"end":299}},{"obj":"Gene:14842","span":{"begin":430,"end":435}},{"obj":"Gene:14601","span":{"begin":477,"end":481}},{"obj":"Gene:14842","span":{"begin":509,"end":514}},{"obj":"Gene:219409","span":{"begin":553,"end":558}},{"obj":"Gene:14842","span":{"begin":599,"end":604}},{"obj":"Gene:29446","span":{"begin":669,"end":673}},{"obj":"Gene:219409","span":{"begin":783,"end":788}},{"obj":"Gene:2691","span":{"begin":800,"end":804}},{"obj":"Gene:288457","span":{"begin":940,"end":945}},{"obj":"Gene:288457","span":{"begin":1010,"end":1015}},{"obj":"Gene:288457","span":{"begin":1068,"end":1073}},{"obj":"Gene:29446","span":{"begin":1107,"end":1111}},{"obj":"Gene:54244","span":{"begin":1152,"end":1172}},{"obj":"Gene:288457","span":{"begin":1200,"end":1205}},{"obj":"Gene:29446","span":{"begin":1214,"end":1218}},{"obj":"Gene:288457","span":{"begin":1304,"end":1309}},{"obj":"Gene:288457","span":{"begin":1408,"end":1413}},{"obj":"Gene:29446","span":{"begin":1473,"end":1477}}]}] 1</p> <ul> <li> <p>Format: tsv-separated columns (PMID, PubAnnotation JSON formated results of Pubtator for this record together with the automatically detected gene mentions using GnormPlus providing the Entrez Gene Identifiers together with the mention offsets, i.e. start and end character positions, corresponding class label of the records (1= relevant, 0=nonrelevant))</p> </li> <li> <p>PubAnnotation format description: <a href="http://www.pubannotation.org/docs/annotation-format/">http://www.pubannotation.org/docs/annotation-format/</a></p> </li> <li> <p>PubTator record retrieval description:</p> </li> </ul> <p>https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/curl.html</p> <p><strong>Remember:</strong> One must notice that this dataset is high imbalanced, with very few cases of the positive class. The additional files could also be explored to build the model or even to build a classifier working at the level of sentences instead of abstracts,</p> <p> </p>
Small example Eiger 2X 16M data set from Diamond Light Source I04 revised for HDRMX Gold Standard Discussion
<p>Revised useful small (488 frame) Eiger data set recorded during routine testing, useful for software testing as it is small. Data recorded is from a thaumatin crystal by Graeme Winter, The original dataset is <a href="https://zenodo.org/record/3385862">https://zenodo.org/record/3385862</a> which contains two Eiger MX datasets, Therm_6_1 and Them_6_2, each with a data file and two versions each of the metadata -- a "..._master.h5" file and a "....nxs" file. The former are the usual Eiger metadata files using exposed external links to connect the metadata to the date, and the latter are HDF5-1.10 VDS files. This revision has the same data as the original Therm_6_2 data, but now includes with the "master.h5" file a "..._master_rev.h5" file and with the ".nxs" file a "..._rev.nxs" VDS file.</p> <p>The purpose to the changes in the "..._rev" files is to provide a supporting example for the HDRMX discusssion of a new proposed Eiger "gold standard" to improve the ability to process Eiger MX data collected at one facility at other facilties, by ensuring that sufficient metadata is stored with all datasets.</p> <p>The changes were made by the following script</p> <pre>cp Therm_6_2.nxs Therm_6_2_rev.nxs cp Therm_6_2_master.h5 Therm_6_2_master_rev.h5 export LD_LIBRARY_PATH=$HOME/lib export HDF5_PLUGIN_PATH=$HOME/lib export PATH=$HOME/bin:$PATH h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/name -d /entry/instrument/name -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/source -d /entry/source -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_rev.nxs -s /entry/instrument/source -d /entry/source -f ref export end_time=`h5dump -d "/entry/end_time" Therm_6_2_master.h5 | grep ":" | sed 's/^.........//'|sed 's/.\$//'` echo "end_time: $end_time" python << 'EOL' import h5py import numpy as np import os end_time=os.environ['end_time'] fvds = h5py.File('Therm_6_2_rev.nxs','r+') fmaster = h5py.File('Therm_6_2_master_rev.h5','r+') fvds_keys=fvds.keys() fmaster_keys=fmaster.keys() fvds_entry=fvds['entry'] fmaster_entry=fmaster['entry'] fvds_entry_keys=fvds_entry.keys() fmaster_entry_keys=fmaster_entry.keys() fvds_entry_instrument=fvds['entry']['instrument'] fmaster_entry_instrument=fmaster['entry']['instrument'] fvds_entry_instrument_keys=fvds_entry_instrument.keys() fmaster_entry_instrument_keys=fmaster_entry_instrument.keys() fvds_entry_instrument_name=(fvds['entry']['instrument']['name']) fmaster_entry_instrument_name=(fmaster['entry']['instrument']['name']) fvds_entry_instrument_short_name=fvds_entry_instrument.attrs['short_name'] fmaster_entry_instrument_short_name=fmaster_entry_instrument.attrs['short_name'] zero_offset=fmaster_entry_instrument['detector']['module']['fast_pixel_direction'].attrs['offset'] fmaster_det_z=fmaster_entry_instrument['transformations']['det_z'] fvds_det_z=fvds_entry_instrument['transformations']['det_z'] print('fvds_keys: ',fvds_keys) print('fmaster_keys: ',fmaster_keys) print('fvds_entry_keys: ',fvds_entry_keys) print('fmaster_entry_keys: ',fmaster_entry_keys) print('fvds_entry_instrument_keys: ',fvds_entry_instrument_keys) print('fmaster_entry_instrument_keys: ',fmaster_entry_instrument_keys) print('fvds_entry_instrument_name: ',fvds_entry_instrument_name) print('fmaster_entry_instrument_name: ',fmaster_entry_instrument_name) print('fvds_entry_instrument_short_name: ',fvds_entry_instrument_short_name) print('fmaster_entry_instrument_short_name: ',fmaster_entry_instrument_short_name) print('fmaster_entry_instrument_detector_module_fast_pixel_direction_offset: ',zero_offset) print('fmaster_entry_instrument_detector_detector_z_det_z: ',fmaster_det_z) print('fmaster_entry_end_time: ',end_time) fmaster.attrs.modify('file_time',np.string_(end_time)) fmaster.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fmaster.attrs.modify('HDF5_Version',np.string_('hdf5-1.8.18')) fvds.attrs.modify('file_time',np.string_(end_time)) fvds.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fvds.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fmaster_entry_instrument_name.attrs.modify('short_name',np.string_(fmaster_entry_instrument.attrs['short_name'])) fmaster_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fmaster_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fvds_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fvds_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fmaster_det_z.attrs.modify('offset',zero_offset) fvds_det_z.attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) print(fmaster['entry']['instrument']['name'].attrs['short_name']) print(fmaster['entry']['instrument']['name'].attrs['short_name'].shape) print(fmaster['entry']['instrument']['name'].attrs['short_name'].dtype) del fvds_entry_instrument.attrs['short_name'] del fmaster_entry_instrument.attrs['short_name'] del fmaster_entry_instrument['source'] fvds.close() fmaster.close() quit() EOL $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_master_rev.h5 $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_rev.nxs </pre> <p>The revised cnxvalidate and definitions are available on github</p> <p><a href="https://github.com/HDRMX/cnxvalidate.git">https://github.com/HDRMX/cnxvalidate.git</a></p> <p><a href="https://github.com/HDRMX/definitions.git">https://github.com/HDRMX/definitions.git</a></p> <p> </p> <p> </p>
Gold, Lanthanum, Gallium, Cobalt and Tantalum quantification performed with relative standardization-NAA in candidate electronic waste (LED) reference material within the METROCYCLEEU project
<p>Datasets containing uncertainty budgets of measurements performed with Neutron Activation Analysis (NAA) on electronic waste materials (specifically light-emitting diodes, LED).</p> <p>Results reported in the datasets are part of the characterization of CRM candidate material LED.</p> <p>Data are elaborated and resulting dataset files are obtained with INAA-INRIM 3.1 software, pre-release development version.</p>
Export-Led Decay: The Trade Channel in the Gold Standard Era
<p>This package contains the data, programs and instructions to replicate manuscript "Export-Led Decay: The Trade Channel in the Gold Standard Era"by Bernardo Candia and Mathieu Pedemonte forthcoming at JEEA</p>
NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition
<p>The automatic recognition of gene names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. The NLM-Gene corpus is a high-quality manually annotated corpus for genes, covering ambiguous gene names, with an average of 29 gene mentions (10 unique identifiers) per article, and a broader representation of different species (including <i>Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio,</i> etc.) when compared to previous gene annotation corpora. NLM-Gene consists of 550 PubMed articles from 156 biomedical journals, doubly annotated by six experienced NLM indexers, randomly paired for each article to control for bias. The annotators worked in three annotation rounds until they reached a complete agreement. Using the new resource, we developed a new gene finding algorithm based on deep learning which improved both on precision and recall from existing tools. The NLM-Gene annotated corpus is freely available at Dryad and at <a href="https://www.ncbi.nlm.nih.gov/research/bionlp/">https://www.ncbi.nlm.nih.gov/research/bionlp/</a>. The gene finding results of applying this tool to the entire PubMed/PMC are freely accessible through our web-based tool PubTator.</p>
MedProcNER Corpus: Gold Standard annotations for Clinical Procedures Information Extraction
<p><strong>MedProcNER</strong> stands for MEDical PROCedure Named Entity Recognition. It is a shared task and set of resources focused on the <strong>detection, normalization and indexing of clinical procedures in medical documents</strong> in <strong>Spanish</strong>. MedProcNER is complementary to the DisTEMIST corpus (https://temu.bsc.es/distemist) as they both use the same document collection.</p><p><strong>Please cite if you use this dataset:</strong></p><p>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</p><p>@article{lima2023overview, title={Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023}, author={Lima-L{\'o}pez, Salvador and Farr{\'e}-Maduell, Eul{\`a}lia and Gasc{\'o}, Luis and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, journal={Working Notes of CLEF}, year={2023} }</p><p>This repository includes the Train Set of the task, which includes a total of 750 documents, plus the annotated Test Set's 250 documents. A gazetteer of possible <strong>SNOMED CT codes</strong> for the normalization and indexing tasks is also part of the bundle as a lexical resource. </p><p>In addition, a cross-mapping file of all SNOMED CT codes to <strong>MeSH</strong> is also included.</p><p>Finally, we release an experimental <strong>multilingual Silver Standard</strong> version derived from the Spanish Gold Standard in <strong>9 languages</strong>: <i><strong>English</strong></i>, <i><strong>Catalan</strong></i>, <i><strong>Italian</strong></i>, <i><strong>French</strong></i>, <i><strong>Portuguese</strong></i>, <i><strong>Romanian</strong></i>, <i><strong>Czech</strong></i>, <i><strong>Dutch</strong></i> and <i><strong>Swedish</strong></i>. </p><p>These documents have been generated using an automatic annotation transfer process that works as follows:</p><ol><li>The text files were translated with a neural machine translation system.</li><li>The annotations were translated with the same neural machine translation system.</li><li>The translated annotations were transferred to the translated text files using a lexical approach and custom dictionaries.</li></ol><p>MedProcNER was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ @ CLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/medprocner">https://temu.bsc.es/medprocner</a>.</p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/medprocner"><strong>Web</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Citation</strong></a><strong>: </strong>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</li><li><a href="https://doi.org/10.5281/zenodo.7817666"><strong>Annotation guidelines</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/"><strong>Proceedings and participant papers</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Overview paper</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/medprocnerproctemist-shared-task-on-clinical-procedure-detection-and-normalization-task-overview-presentation-at-bioasq-workshop-of-clef-2023-conference"><strong>Overview talk slides at BioASQ/CLEF</strong></a></li></ul><p> </p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in MedProcNER, 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/8413866">SympTEMIST </a>(Corpus of symptoms, signs and findings mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine 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 place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, 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><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><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p>
NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition
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Differentiating causes of febrile illness in Burkina Faso: data from an accuracy study comparing gold standard culture techniques with a haemocytometry based algorithm (IMS), procalcitonin (PCT) and C-reactive protein (CRP)
Open the record for dataset details and reuse information.
DAS Tool 1.1.2 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. Refinement of the binning output of MaxBin 2.2.7, MetaBAT 2.12.1, CONCOCT 1.0.0, and DAS Tool 1.1.2.<br><strong>Software: </strong>DAS Tool<br><strong>SoftwareVersion: </strong>1.1.2<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/cmks/DAS_Tool<br><strong>DockerImage:</strong> cami/das_tool:1.1.2<br><strong>IsBiobox:</strong> No<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> DAS_Tool -i binning_concoct1.0.0,binning_maxbin2.2.7,binning_metabat2.12.1 -c anonymous_gsa_pooled.fasta -o output --search_engine diamond
CONCOCT 1.0.0 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>CONCOCT<br><strong>SoftwareVersion: </strong>1.0.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/BinPro/CONCOCT<br><strong>DockerImage:</strong> quay.io/biocontainers/concoct:1.0.0--py37h88e4a8a_5<br><strong>IsBiobox:</strong> No<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> for i in {0..63}; do bowtie2 -q --threads 30 --fr -x anonymous_gsa_pooled.fasta --interleaved sample_${i}/anonymous_reads.fq -S anonymous_reads_sample_${i}.sam ; done<br>for i in {0..63}; do samtools view -b sample_${i}.sam -o anonymous_reads_sample_${i}.bam & done<br>for i in {0..63}; do samtools sort anonymous_reads_sample_${i}.bam -o anonymous_reads_sample_${i}.sorted.bam ; done<br>for i in {0..63}; do samtools index anonymous_reads_sample_${i}.sorted.bam ; done<br>cut_up_fasta.py anonymous_gsa_pooled.fasta -c 10000 -o 0 --merge_last -b contigs_10K.bed > contigs_10K.fa<br>concoct_coverage_table.py contigs_10K.bed /host/benchmarking/fmeyer/output/bowtie2/mouse_gut/sorted_bam/anonymous_reads_sample_*.sorted.bam > coverage_table.tsv<br>concoct --composition_file contigs_10K.fa --coverage_file coverage_table.tsv -b<br>merge_cutup_clustering.py clustering_gt1000.csv > clustering_merged.csv
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
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>
Gold Standard and Annotation Dataset for CO2 Emissions Annotation
<p>This repository contains the results of a research project which provides a benchmark dataset for extracting greenhouse gas emissions from corporate annual and sustainability reports. </p> <p>The zipped <code>datasets</code> file contains two datasets, <code>gold_standard</code> and <code>annotation_dataset</code>(inside the outer zip file there is a password-protected zip file containing the two datasets. To unpack, use the password is provided in the outer zip file).</p> <h3>Data collection</h3> <ol> <li>A Large Language Model (LLM) based pipeline was used to extract the greenhouse gas emissions from the reports (see columns prefixed with <code>llm_</code> in <code>annotation_dataset</code>). The extracted emissions follow the categories Scope 1, 2 (market-based) and 2 (location-based) and 3, as defined in the GHGP protocol (see variables <code>scope</code>).</li> <li>Annotation of the pipeline output was done in 3 phases: first by non-experts (see columns prefixed with <code>non_expert_</code> in <code>annotation_dataset</code>), then by expert groups (columns prefixed with <code>exp_group_</code> in <code>annotation_dataset</code>) in case of disagreement of non-experts and finally in a discussion of all experts (columns prefixed with <code>exp__disc</code> in <code>annotation_dataset</code>) in case of disagreement between expert groups. The annotation guidelines for the <a href="https://zenodo.org/api/records/15124118/draft/files/Non-Expert%20Annotation%20Guidelines.pdf/content">non-experts</a> and <a href="https://zenodo.org/api/records/15124118/draft/files/Expert%20Annotation%20Guidelines.pdf/content">experts</a> are also included in this repository.</li> <li>The annotation results from all three phases are combined to form the final benchmark dataset: <code>gold_standard</code>. Codebooks detailing each variable of each of the two datasets are also provided. More details about the annotation template or the data wrangling scripts can be found in the <a href="https://github.com/soda-lmu/gist-data-descriptor/" target="_blank" rel="noopener">GitHub repository</a>. </li> </ol> <h3>Merging of datasets</h3> <p>Users can match the two datasets (<code>gold_standard</code> and <code>annotation_dataset</code>) using the variable combination of <code>company_name</code>, <code>report_year</code> and <code>merge_id</code> (index column). The <code>merge_id</code> already includes the company name and report year implicitly, but to avoid column duplication in the join operation, it should be included as join variables. For example this is useful when comparing LLM extractions to gold standard data.</p>
SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction
<p><strong>SympTEMIST</strong> stands for Symptoms TExt MIning Shared Task. It is a shared task and set of resources focused on the <strong>detection of mentions, normalization and indexing of symptoms, signs and findings in medical documents</strong> in <strong>Spanish</strong>. SympTEMIST is complementary to the DisTEMIST (https://temu.bsc.es/distemist) and MedProcNER/ProcTEMIST (https://temu.bsc.es/medprocner) corpora as they all use the same document collection.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>"Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>."</p> <p> </p> <p>This repository includes the:</p> <ul> <li><strong>Train </strong>and<strong> Test Set </strong>for the three subtasks</li> <li><strong>SYMPTEMIST gazetteer </strong>of <strong>SNOMED symptoms, signs & findings </strong></li> <li><strong>Multilingual </strong>Silver Standard<strong> </strong>in <strong>9 languages:</strong> <ul> <li><em><strong>English</strong></em></li> <li><em><strong>Portuguese</strong></em></li> <li><em><strong>French</strong></em></li> <li><em><strong>Italian</strong></em></li> <li><em><strong>Romanian</strong></em></li> <li><em><strong>Catalan</strong></em></li> <li><em><strong>Swedish</strong></em></li> <li><em><strong>Dutch</strong></em></li> <li><em><strong>Czech</strong></em></li> </ul> </li> <li><strong>Background set</strong> of over 15,000 clinical cases.</li> <li><strong>Spanish</strong> Silver Standard (predictions by participants over the background set)</li> </ul> <p> </p> <p>Please read the README file attached for more information on folder structure and file format.</p> <p> </p> <p>SympTEMIST was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioCREATIVE 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/symptemist.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><a href="https://temu.bsc.es/symptemist/"><strong>Task web</strong></a></li> <li><a href="https://biocreative.bioinformatics.udel.edu/"><strong>BioCreative web</strong></a></li> <li><strong>Citation: </strong>Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>.</li> <li><a href="../records/8246440"><strong>Annotation guidelines</strong></a></li> <li><a href="../records/10103191"><strong>BioCreative/AMIA workshop proceedings</strong></a></li> <li><a href="../doi/10.5281/zenodo.10104546"><strong>Overview paper</strong></a></li> <li><a href="https://www.youtube.com/playlist?list=PLyLDDulunoofv79Pci0-y2rGP5GOBoZhp"><strong>Youtube videos (overview & teams)</strong></a></li> <li><a href="https://www.slideshare.net/MartinKrallinger/symptemist-shared-task-on-symptoms-signs-and-findings-detection-and-normalization-task-overview-talk-at-biocreative-viii-workshop-amia-2023"><strong>SympTEMIST overview talk slides at BioCreative/AMIA workshop</strong></a></li> </ul> <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><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<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 SympTEMIST, you might want to check out these corpora and resources:</p> <ul> <li><a href="../records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li> <li><a href="../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="../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="../records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li> <li><a href="../records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li> <li><a href="../records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li> <li><a href="../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="../records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li> <li><a href="../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="../records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li> <li><a href="../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> <p> </p>
Hackathon - TF-TG relation annotation Gold Standard Corpus
<p>TF-TG relation annotation Gold Standard Corpus.</p> <p>The file contains 130 PMIDs annotated in the following way:</p> <ul> <li>PMID.txt: the original TXT file with the abstract.</li> <li>PMID.ann: the file with the BRAT annotations. Each annotation file contains terms (e.g. T1, T2, ...) and relations (e.g. R1, R2, ...), where each relation is from one term (e.g. T1) to another term (e.g. T2).</li> </ul>
Dataset: Gold standard dataset for explainability need detection in app reviews.
<p>We crawled 90,000 app reviews from both Google Play Store and Apple App Store, including reviews from both free and paid apps. These reviews were filtered for explainability needs, and after this process, 4,495 reviews remained. Among them, 2,185 reviews indicated an explanation need, while 2,310 did not. This resulting gold standard dataset was used to train and evaluate several machine learning models and rule-based approaches for detecting explanation needs in app reviews.</p> <p>The dataset includes both balanced and unbalanced evaluation sets, as well as the original crawled data from October 2023. In addition to machine learning approaches, rule-based methods optimized for F1 score, precision, and recall are also included.</p> <p>We provide several pre-trained machine learning models (including BERT, SetFit, AdaBoost, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Random Forest, and SVM) along with training scripts and evaluation notebooks. These models can be applied directly or retrained using the included datasets.</p> <p>For further details on the structure and usage of the dataset, please refer to the README.md file within the provided ZIP archive.</p>
HR Measures From Four Consumer-Grade Optical Heart Rate Sensors with Gold Standard ECG Reference
<p>Accompanying paper that explains data collection, research methods, cleaning etc. is under review as:</p> <p>"Validity of Four Consumer-Grade Optical Heart Rate Sensors for Assessing Volume and Intensity Distribution of Physical Activity"</p> <p>Currently with: Scandinavian Journal of Medicine & Science in Sports TBD, 2024-12-31</p> <p>Four files contain HR data at 1 Hz for each of the devices (Garmin Venu2S and Polar Vantage M2, and the upper arm-worn Polar Verity Sense and Scosche Rhythm24) [device column] paired with medical-grade ECG [ecg column], Type: EP830, Amedtec ECGpro, Aue, Germany</p>
Diagnostic Accuracy of Foot Length in Predicting Preterm and Low Birth Weight Using Ultrasound Dating as The Gold Standard in a Rural District of Pakistan
ClinicalTrials.gov study NCT05515211. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
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.