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.
34,778
datasets available to search
ShareScore release 0.9.0
Dataset results
34,778 results for “diseases”
Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease
<p>Age is an important ecological tool in wildlife conservation. However, it is difficult to estimate in most animals, including felines — most of whom are endangered. Here, we developed the first DNA methylation-based age-estimation technique — as an alternative to current age-estimation methods — for two feline species that share a relatively long genetic distance with each other: domestic cat (<i>Felis catus</i>; 79 blood samples) and an endangered <i>Panthera</i>, the snow leopard (<i>Panthera uncia</i>; 11 blood samples). We measured the methylation rates of two gene regions <span>using </span>methylation-sensitive high-resolution melting (MS-HRM). Domestic cat age was estimated with a mean absolute deviation (MAD) of 3.83 years. Health conditions influenced accuracy of the model. Specifically, the models built on cats with chronic kidney disease (CKD) had lower accuracy than those built on healthy cats. The snow leopard-specific model (i.e. the model that resets the model settings for snow leopards) had a better accuracy (MAD = 2.10 years) than that obtained on using the domestic cat model directly. This implies that our markers could be utilised across species, although changing the model settings when targeting different species could lead to better estimation accuracy. The snow leopard-specific model also successfully distinguished between sexually immature and mature individuals.</p>
Innovation in chronic respiratory diseases: biodiversity and commons – WHO GARD workshop [video]
<p>00:00 Introduction by Letitia Harding.</p> <p>00:15 Embedding biodiversity in digitally-enabled, patient-centered care pathways for airway diseases – Dr Jean Bousquet.</p> <p>09:35 Open-source respiratory health commons. 15 projects communities can adapt, repair, reproduce for low cost medical care (libre and open-source tech) [<a href="https://doi.org/10.5281/zenodo.5515632">download presentation</a>].</p> <p>18:13 Open discussion – Facilitation by Letitia Harding.</p> <p>28:56 End.</p>
"Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.
<p> "Decision support systems halve fungicide compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.https://doi.org/10.1038/s43247-021-00291-8 | www.nature.com/commsenv</p> <p>Dataset includes the results of 80 independent experiments reported in 22 articles and it has a dimension of 329 rows x 42 columns. Further information in "Description Supplementary Data 1.pdf" file. </p> <p>This dataset was assembled including also the data from the publication Agronomy 2020, 10(4), 560; https://doi.org/10.3390/agronomy10040560.</p>
Ecological dataset from: 'Rats and the city: implications of urbanization on zoonotic disease risk in Southeast Asia'
<p>This dataset includes the ecological and environmental data, a description of the analysis steps, and the related R code associated with the manuscript: "Rats and the city: implications of urbanization on zoonotic disease risk in Southeast Asia" by Kim R. Blasdell, Serge Morand, Susan G.W. Laurance, Stephen L Doggett, Amy Hahs, David Perera, and Cadhla Firth, available at: https://www.pnas.org/doi/abs/10.1073/pnas.2112341119</p> <p>This dataset also relates to the preprint: "Rats in the city: implications for zoonotic disease risk in an urbanizing world" available at: https://www.biorxiv.org/content/10.1101/2021.03.18.436089v1</p> <p>A detailed description of the files can be found in README.txt</p>
A single-cell atlas of multiple myeloma disease evolution
<p>A collection of computationally integrated (batch corrected) scRNA-seq and scTCR-seq data from a large cohort of bone marrow and peripheral blood samples from patients with multiple myeloma, myeloma precursor disease (MGUS and SMM) and non-cancer controls.</p> <h3>Datasets</h3> <ul> <li><strong>dataDescription.xlsx</strong> - Definition of dataset components.</li> <li><strong>panImmune.h5ad</strong> - All cells types integrated gene expression object.</li> <li><strong>Tcell.h5ad</strong> - T cells integrated object.</li> <li><strong>Tcell-scTCR.csv</strong> - Single-cell TCR data.</li> <li><strong>Botta_2023-predicted.h5ad</strong> - Single-cell gene expression data from Botta et al. analysed by label transfer.</li> <li><strong>metadata.donor.csv</strong> - Donor metadata.</li> </ul> <h3>Published data acquisition</h3> <p>Data shared through the gene expression omnibus (GEO) can be accessed for Maura et al. under accession GSE161195, Bailur et al. GSE163278, Oetjen et al. GSE120221, Granja et al. GSE139369, Zavidij et al. GSE124310, Kfoury et al. GSE143791, Botta et al. GSE205393, and Zheng et al. GSE156728. Data shared via dbGaP for Sklavenitis-Pistofidis et al. can be accessed under accession phs002476.v1.p1. Data shared online can be accessed for Stephenson et al. (via <a href="https://covid19cellatlas.org/">https://covid19cellatlas.org/</a>), Conde et al. (via <a href="https://www.tissueimmunecellatlas.org/">https://www.tissueimmunecellatlas.org/</a>), and Liu et al. (via <a href="https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81">https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81</a>). Single-cell data from <a href="https://doi.org/10.1016/j.ccell.2022.10.017" target="_blank" rel="noopener">Sklavenitis-Pistofidis et al.</a> and extended clinical data from <a href="https://doi.org/10.1038/s43018-023-00657-1">Maura et al.</a> (i.e. paraprotein) are omitted and must be acquired through direct contact with these authors.</p> <h3>Contact information </h3> <ul> <li>Kane Foster - kane.foster.web@gmail.com</li> <li>Kwee Yong - kwee.yong@ucl.ac.uk</li> </ul> <p>Please cite <a href="https://www.medrxiv.org/content/10.1101/2024.06.22.24309250v1">our pre-print</a> if you use this resource in your research.</p>
Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping
<p>Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping</p>
Dihydrosphingolipids are associated with steatosis and increased fibrosis damage in human and animal models of non-alcoholic fatty liver disease
<p>Data sets used for the article entitled: "Accumulation of dihydrosphingolipids and neutral lipids is related to steatosis and fibrosis damage in human and animal models of non-alcoholic fatty liver disease"</p> <p>- Patient data: DATA Patients JLR.xlsx</p> <p>- Mouse data: DATA Mice.xlsx</p>
Wheat Disease Dataset - Small
<p>999 wheat disease images, including yellow rust, brown rust, septoria, mildew and healthy leaves taken in realistic growth conditions. This is a small subset of the full dataset containing 19,172 images over the five categories.</p> <p>The full dataset is available on request from James Brown, james.brown@jic.ac.uk</p> <p> </p>
Data for: Multi-omics analysis identifies symbionts and pathogens of blacklegged ticks (Ixodes scapularis) from a Lyme disease hotspot in southeastern Ontario, Canada
<p>Ticks in the family Ixodidae are recognized as important vectors of zoonoses including Lyme disease (LD), which is caused by spirochete bacteria from the <em>Borreliella</em> (<em>Borrelia</em>) <em>burgdorferi</em> sensu lato (<em>Bbsl</em>) complex. The blacklegged tick (<em>Ixodes scapulars</em>) continues to expand across Canada, creating hotspots of elevated LD risk at the leading edge of its expansion range. Current efforts to understand the risk of pathogen transmission associated with <em>I. scapularis</em> in Canada focus primarily on targeted screens, while variation in the tick microbiome remains poorly understood. Using multi-omics consisting of 16S metabarcoding and ribosome-depleted, whole-shotgun RNA transcriptome sequencing, we examined the microbial communities associated with adult <em>I. scapularis</em> (N = 32), sampled from four tissue types (whole tick, salivary glands, midgut, and viscera) and three geographical locations within an LD hotspot near Kingston, Ontario. The communities consisted of both endosymbiotic and known or potentially pathogenic microbes, including RNA viruses, bacteria, and a <em>Babesia</em> sp. intracellular parasite. We show that β-diversity is significantly higher between individual tick salivary gland and midgut bacterial communities, compared to whole ticks; while linear discriminant analysis (LDA) effect size (LEfSe) determined that the three potentially pathogenic bacteria detected by V4 16S rDNA sequencing were also discriminatory for dissected tissues only, including a <em>Borrelia</em> from the <em>Bbsl</em> complex, <em>Borrelia miyamotoi</em>, and <em>Anaplasma phagocytophilum. </em>Importantly, we find co-infection of <em>I. scapularis</em> by multiple microbes, in contrast to diagnostic protocols for LD, which typically focus on infection from a single pathogen of interest (<em>B. burgdorferi</em> sensu stricto).</p>
Full dataset for 'RNA-binding proteins in human genetic disease'
<p>This extended version of the Supplementary Table S1 of the publicaiton "RNA-binding proteins in human genetic disease" (Gebauer, F., Schwarzl, T., Valcárcel, J. <em>et al.</em> <em>Nat Rev Genet</em> <strong>22</strong>, 185–198 (2021). https://doi.org/10.1038/s41576-020-00302-y) contains all human RNA-binding proteins (RBPs) found in at least one high-throughput RNA interactome caputure study with all their annotation along with known RBP.</p> <p>The whole set is available as download on RBPbase with additional annotation and additional studies. This table contains the version used in the original publication</p> <p> </p>
iDPP@CLEF 2022 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge
<p><a href="https://brainteaser.health/open-evaluation-challenges/idpp-2022/">iDPP@CLEF 2022</a> (Intelligent Disease Progression Prediction at CLEF) is a challenge organised by the <a href="https://brainteaser.health/">BRAINTEASER</a> Horizon 2020 project and co-located with <a href="https://clef2022.clef-initiative.eu/">CLEF 2022</a> (Conference and Labs of the Evaluation Forum). </p> <p>BRAINTEASER is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.</p> <p>The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to:</p> <ul> <li> <p>Better describe<strong> disease mechanisms</strong>.</p> </li> <li> <p><strong>Stratify patients </strong>according to their phenotype assessed all over the disease evolution.</p> </li> <li> <p><strong>Predict disease progression</strong> in a probabilistic, time dependent fashion.</p> </li> </ul> <p>iDPP@CLEF 2022 offered the following tasks:</p> <ul> <li> <p><strong>Pilot Task 1 – Ranking Risk of Impairment</strong>: It focuses on ranking of patients based on the risk of impairment in specific domains. More in detail, we will use the ALSFRS-R scale to monitor speech, swallowing, handwriting, dressing/hygiene, walking and respiratory ability in time and will ask participants to rank patients based on time to event risk of experiencing impairment in each specific domain.</p> </li> <li> <p><strong>Pilot Task 2 – Predicting Time of Impairment</strong>: It refines Task 1 asking participants to predict when specific impairments will occur (i.e. in the correct time-window). In this regard, we assess model calibration in terms of the ability of the proposed algorithms to estimate a probability of an event close to the true probability within a specified time-window.</p> </li> <li> <p><strong>Position Papers Task 3 – Explainability of AI algorithms</strong>: We call for proposals of different visualization frameworks able to show the multivariate nature of the data and the model predictions in an explainable, possibly interactive, way.</p> </li> </ul> <p> </p> <p>This dataset contains the repositories of the participants to iDPP@CLEF 2022. These repositories contain the output, i.e. the predictions, produced by the participating systems as well as the performance scores for those systems.</p> <p>For additional information about iDPP@CLEF 2022, please see:</p> <ul> <li> <p>Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. In Barr ́on-Cedeno, A., Da San Martino, G., Degli Es- posti, M., Sebastiani, F., Macdonald, C., Pasi, G., Hanbury, A., Potthast, M., Faggioli, G., and Ferro, N., editors, <em>Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Thirteenth International Conference of the CLEF Association (CLEF 2022)</em>, pages 395–422. Lecture Notes in Computer Science (LNCS) 13390, Springer, Heidelberg, Germany.</p> </li> <li> <p>Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, <em>CLEF 2022 Working Notes</em>, pages 1130– 1210. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. <a href="https://ceur-ws.org/Vol-3180/paper-88.pdf">http://ceur-ws.org/Vol-3180/</a>.</p> </li> </ul>
The contribution of Neanderthal introgression and natural selection to neurodegenerative diseases
<p>Files used to create binary annotations for LDSC in the following repository: https://github.com/RHReynolds/als-neanderthal-analysis.</p> <p>See the following link for details: https://github.com/RHReynolds/als-neanderthal-analysis/tree/main/raw_data/01_annotations</p>
Dataset for accumulation of pTau231 at the post synaptic density in Alzheimer's disease
<p>Dataset included from Journal of Alzheimer's disease publication "Accumulation of pTau231 at postsynaptic density in Alzheimer's Disease. This includes validation of synaptosomal fraction richness in Figure 5, tTau and pTau231 Simoa Quanterix synaptosomal fraction data for Supplementary figure 2, entorhinal cortex PSD95 P4 adjusted total tau and pTau231 values from Simoa Quanterix for generation of model shown in figure 4, quantitative AMPA immunohistochemical staining data used for generating figure 2, NanoString protein panel data used to generate heatmap in figure 7, quantitative immunofluorescence staining data for pTau214 and PSD95 colocalization studies in figure 3, Braak stage 2 case NanoString protein data for intra-case regional comparisons of disease progression, and synaptosome Simoa total tau and pTau231 data in addition to western blot P4 PSD95 data that was used for generation of figures 4 and 6.</p>
Dataset related to: Sirtuin 3 Deficiency Aggravates Kidney Disease in Response to High-Fat Diet through Lipotoxicity-Induced Mitochondrial Damage
<p>The files contain all the dataset included in the manuscript divided by figures.</p> <p> </p> <p>Abstract: Sirtuin 3 (SIRT3) is the primary mitochondrial deacetylase that controls the antioxidant<br>pathway and energy metabolism. We previously found that renal Sirt3 expression and activity were<br>reduced in mice with type 2 diabetic nephropathy associated with oxidative stress and mitochondrial<br>abnormalities and that a specific SIRT3 activator improved renal damage. SIRT3 is modulated by<br>diet, and to assess whether Sirt3 deficiency aggravates mitochondrial damage and accelerates kidney<br>disease in response to nutrient overloads, wild-type (WT) and Sirt3-/- mice were fed a high-fat-diet<br>(HFD) or standard diet for 8 months. Sirt3-/- mice on HFD exhibited earlier and more severe<br>albuminuria compared to WT mice, accompanied by podocyte dysfunction and glomerular capillary<br>rarefaction. Mesangial matrix expansion, tubular vacuolization and inflammation, associated with<br>enhanced lipid accumulation, were more evident in Sirt3-/- mice. After HFD, kidneys from Sirt3-/-<br>mice showed more oxidative stress than WT mice, mitochondria ultrastructural damage in tubular<br>cells, and a reduction in mitochondrial mass and energy production. Our data demonstrate that Sirt3<br>deficiency renders mice more prone to developing oxidative stress and mitochondrial abnormalities<br>in response to HFD, resulting in more severe kidney diseases, and this suggests that mitochondria<br>protection may be a method to prevent HFD-induced renal injury.</p>
Data and code for: Local infectious disease experience influences vaccine refusal rates: a natural experiment
<p>Vaccination has been critical to the decline in infectious disease prevalence in recent centuries. Nonetheless, vaccine refusal has increased in recent years, with complacency associated with reductions in disease prevalence highlighted as an important contributor. We exploit a natural experiment in Glasgow at the beginning of the 20th century to investigate whether prior local experience of an infectious disease matters for vaccination decisions. Our study is based on smallpox surveillance data and administrative records of parental refusal to vaccinate their infants. We analyse variation between administrative units of Glasgow in cases and deaths from smallpox during two epidemics over the period 1900–1904, and vaccine refusal following its legalisation in Scotland in 1907 after a long period of compulsory vaccination. We find that lower local disease incidence and mortality during the epidemics were associated with higher rates of subsequent vaccine refusal. This finding indicates that complacency influenced vaccination decisions in periods of higher infectious disease risk, responding to local prior experience of the relevant disease, and has not emerged solely in the context of the generally low levels of infectious disease risk of recent decades. These results suggest that vaccine delivery strategies may benefit from information on local variation in incidence.</p>
Dataset related to: Add-On Cyclic Angiotensin-(1-7) with Cyclophosphamide Arrests Progressive Kidney Disease in Rats with ANCA Associated Glomerulonephritis
<p>The files contain all the dataset included in the manuscript divided by figures.</p> <p> </p> <p>Abstract: Rapidly progressive crescentic glomerulonephritis associated with anti-neutrophil cytoplasmic antibodies (ANCA-GN) is a major cause of renal failure. Current immunosuppressive therapies are associated with severe side effects, intensifying the need for new therapeutic strategies. The activation of Mas receptor/Angiotensin-(1-7) axis exerted renoprotection in chronic kidney disease.<br> Here, we investigated the effect of adding the lanthionine-stabilized cyclic form of angiotensin-1-7 [cAng-(1-7)] to cyclophosphamide in a rat model of ANCA-GN. At the onset of proteinuria,Wistar Kyoto rats with ANCA-GN received vehicle or a single bolus of cyclophosphamide, with or without daily cAng-(1-7). Treatment with cAng-(1-7) plus cyclophosphamide reduced proteinuria by 85% vs. vehicle, and by 60% vs. cyclophosphamide, and dramatically limited glomerular crescents to less than 10%. The addition of cAng-(1-7) to cyclophosphamide protected against glomerular inflammation and endothelial rarefaction and restored the normal distribution of parietal epithelial cells. Ultrastructural analysis revealed a preserved GBM, glomerular endothelium and podocyte structure, demonstrating that combination therapy provided an additional layer of renoprotection. This study demonstrates that adding cAng-(1-7) to a partially effective dose of cyclophosphamide arrests the progression of renal disease in rats with ANCA-GN, suggesting that cAng-(1-7) could be a novel clinical approach for sparing immunosuppressants.</p>
Wheat disease images (small dataset)
<p>999 wheat disease images, including yellow rust, brown rust, septoria, mildew and healthy leaves taken in realistic growth conditions. This is a small subset of the full dataset containing 19,172 images over the five categories.</p> <p>The full dataset is available on request from James Brown, james.brown@jic.ac.uk.</p>
DisTEMIST corpus: detection and normalization of disease mentions in spanish clinical cases
<p><strong>DisTEMIST corpus: training set + MULTILINGUAL RESOURCES + CROSSMAPPINGS + test set + background set</strong></p><p> </p><p><strong>Please cite if you use this dataset:</strong></p><p>Miranda-Escalada, A., Gascó, L., Lima-López, S., Farré-Maduell, E., Estrada, D., Nentidis, A., Krithara, A., Katsimpras, G., Paliouras, G., & Krallinger, M. (2022). Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. <i>Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings</i></p><p>@article{miranda2022overview, title={Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources}, author={Miranda-Escalada, Antonio and Gascó, Luis and Lima-López, Salvador and Farré-Maduell, Eulàlia and Estrada, Darryl and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2022} }</p><p> </p><p><strong>Introduction</strong></p><p>The DisTEMIST corpus is a collection of 1000 clinical cases with <strong>disease mention annotations manually mapped to</strong> <strong>Snomed-CT concepts</strong>. All documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022. For more information about the track and its schedule, please <a href="https://temu.bsc.es/distemist/">visit the website</a>.</p><p> </p><p><strong>File structure:</strong></p><p>The DisTEMIST corpus has been randomly divided into a training set, containing 750 clinical cases, and a test set (584 in the case of subtrack2), consisting of 250 additional cases. Participants must train their systems using the train set and submit predictions for the test set, on which they will be evaluated. The file structure of the corpus is as follows:</p><ul><li><strong>train_set:</strong><ul><li><strong>text_files</strong>: Folder with plain text files of the clinical cases</li><li><strong>subtrack1_entities</strong>: It contains annotations in a tab-separated file (TSV) with the following columns:<ul><li><i>filename</i>: document name</li><li><i>mark</i>: identifier mention id</li><li><i>label</i>: mentions type (ENFERMEDAD)</li><li><i>off0</i>: starting position of the mention in the document</li><li><i>off1</i>: ending position of the mention in the document</li><li><i>span</i>: text span</li></ul></li><li><strong>subtrack2_linking: </strong>It contains annotations in a tab-separated file (TSV) with the following columns:<ul><li><i>filename</i>: document name</li><li><i>mark</i>: identifier mention id</li><li><i>label</i>: mentions type (ENFERMEDAD)</li><li><i>off0</i>: starting position of the mention in the document</li><li><i>off1</i>: ending position of the mention in the document</li><li><i>span</i>: text span</li><li><i>codes</i>: List of Snomed-CT concept codes linked to the mention. If there is more than one code associated with a mention, they will be concatenated by the symbol "+".</li><li><i>semantic relation</i>: the relationship between the assigned code and the mention. It can be EXACT, when the code corresponds exactly with the mention, or NARROW, when the mention corresponds to a narrower concept than the Snomed-CT code. For instance, the concept "Chorioretinal lacunae" does not exist in Snomed-CT. Then, it is normalized to the Snomed-CT ID 302893000 ("Chorioretinal disorder").</li><li>(Note: the training for entity linking were released in two parts, you must join them)</li></ul></li></ul></li></ul><p> </p><ul><li><strong>test_annotated:</strong><ul><li><strong>text_files</strong>: Folder with plain text files of the clinical cases</li><li><strong>brat</strong>: Folder with text files and their annotations in brat's .ann format</li><li><strong>subtrack1_entities: </strong>It contains annotations in a tab-separated file (TSV) with the same columns as the train set</li><li><strong>subtrack2_linking: </strong>It contains annotations in a tab-separated file (TSV) with the same columns as the train set</li></ul></li></ul><p> </p><ul><li><strong>test_background_unannotated/text_files: </strong>3000 clinical cases (test + background). In the the original task, participants had to make predictions for these 3000 files and they were evaluated on a subset of them.</li></ul><p> </p><ul><li><strong>multilingual-resources</strong>: we have generated the annotated training and validation sets in <strong>6 languages</strong>: <i><strong>English</strong></i>, <i><strong>Portuguese</strong></i>, <i><strong>Catalan</strong></i>, <i><strong>Italian</strong></i>, <i><strong>French</strong></i> and <i><strong>Romanian</strong></i>. The process was:<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 an annotation transfer technology.</li><li>The text files are stored in the multilingual_resources/training-text-files subfolder.</li><li>The annotated TSV files are stored in the multilingual_resources/lang subfolders.</li><li>If you want to visualize the multilingual resources, check out this Brat server: <a href="https://temu.bsc.es/mDistemist/#/translations/">https://temu.bsc.es/mDistemist/#/translations/</a><br>For instance, you can see the parallel annotations in <a href="http://temu.bsc.es/mDistemist/diff.xhtml#/translations/fr/train/es-S0004-06142008000100011-1?diff=/translations/en/train/">English vs in French</a>, or in <a href="https://temu.bsc.es/mDistemist/diff.xhtml#/translations/cat/train/S0004-06142005000500011-1?diff=/gold-standard/train/">Spanish (the gold standard) vs in Catalan</a>.</li></ol></li></ul><p> </p><ul><li><strong>cross-mappings. </strong>We include the same entities as in DISTEMIST-linking but mapped to Snomed-CT, <a href="https://www.ncbi.nlm.nih.gov/mesh/"><strong>MeSH</strong></a>, <a href="https://icd.who.int/browse10/2019/en#/"><strong>ICD-10</strong></a>, <a href="https://hpo.jax.org/"><strong>HPO</strong></a>, and <a href="https://www.omim.org/"><strong>OMIM</strong></a>. The original mappings are manual and to Snomed-CT. The mapping to the other terminologies was done through the UMLS Metathesaurus.</li></ul><p> </p><p><strong>Resources</strong> </p><ul><li><a href="https://temu.bsc.es/distemist/">Task Web</a></li><li><a href="https://github.com/TeMU-BSC/distemist_evaluation_library">Evaluation Library</a></li><li><a href="https://doi.org/10.5281/zenodo.6458114"><strong>DISTEMIST gazetteer</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.6458078"><strong>DISTEMIST guidelines</strong></a></li><li><strong>Citation: </strong>Miranda-Escalada, A., Gascó, L., Lima-López, S., Farré-Maduell, E., Estrada, D., Nentidis, A., Krithara, A., Katsimpras, G., Paliouras, G., & Krallinger, M. (2022). Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. <i>Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings</i></li><li><a href="https://ceur-ws.org/Vol-3180/paper-11.pdf"><strong>Overview paper</strong></a></li><li><a href="https://ceur-ws.org/Vol-3180/"><strong>CLEF/BioASQ Workshop proceedings</strong></a></li><li><a href="https://www.youtube.com/watch?v=KvvhogUMjl8&list=PL5uSCzf1azhC_Sg9leXavmcA4nJ4HQa_n"><strong>Youtube videos (overview & teams)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/overview-of-distemist-at-bioasq-automatic-detection-and-normalization-of-diseases-from-clinical-texts-results-methods-evaluation-and-multilingual-resources-at-bioasq-workshop-of-clef-conference"><strong>DISTEMIST overview talk slides</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> </p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in DisTEMIST, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/8413866">SymTEMIST</a> (Corpus of symptoms, signs and findings 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/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 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><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>
Fig. 5 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e.g., Biodiversity repositories and biological general source). Health-related sources are represented in purple (Health gov: governmental, init-program: initiative or programs). Using this broad categorization, most of the sources contribute with data related to the three epidemiological levels, although with an unpaired flow. For example, scientific literature has a lower contribution for hosts/ reservoirs, and biodiversity-biological sources have a minor contribution for pathogens. Most data sources have a global scale meanwhile governmental sources have a relevant contribution to pathogen data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen. In the right panel: positive studies (i.e., those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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.