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1,630 results for “Occupations”
Data from: Human activities influence the occupancy probability of mammalian carnivores in the Brazilian Caatinga
The Caatinga is a semi-arid domain, characterized by reduced humidity and high rates of anthropogenic impact. In addition to the low availability of water, carnivorous mammals are still exposed to a number of threats related to landscape modifications. We used data from camera traps and occupancy models to investigate the habitat use by carnivores in an area of Caatinga in northeastern Brazil. We found a negative correlation between the presence of wind farms and the occupancy probability of the jaguar, and a positive relationship with the presence of the jaguarundi. Puma and jaguarundi occupied primarily sites near watercourses, whereas the occupancy of the crab-eating fox was correlated positively with the presence of poachers. The ocelot was detected more frequently at sites distant from human settlements, whereas the jaguar was detected more often in areas far from wind farms. We found a negative correlation between the distance of water and the detection of the ocelot. The detection of the crab-eating fox was influenced positively by the detection of cattle. In addition to the negative influence of some anthropic activities, our results indicate that water is a very important resource for species, and the few permanent sources of this resource available in the area must be preserved. The replication of our research in other systems, worldwide, that are experiencing similar pressures, should permit a systematic evaluation of the management and conservation strategies needed to rebuild or maintain populations, restore ecosystems, and support conservation policies in human-altered landscapes.
Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community
Emerging conservation paradigms have shifted from single to multi-species approaches focused on sustaining biodiversity. Multi-species hierarchical occupancy modelling provides a method for assessing biodiversity while accounting for multiple sources of uncertainty. We analysed camera trapping data with multi-species models using a Bayesian approach to estimate the distributions of a terrestrial mammal community in northern Botswana and evaluate community, group, and species-specific responses to human disturbance and environmental variables. Groupings were based on two life-history traits: body size (small, medium, large and extra-large) and diet (carnivore, omnivore and herbivore). We photographed 44 species of mammals over 6607 trap nights. Camera station-specific estimates of species richness ranged from 8 to 27 unique species, and species had a mean occurrence probability of 0·32 (95% credible interval = 0·21–0·45). At the community level, our model revealed species richness was generally greatest in floodplains and grasslands and with increasing distances into protected wildlife areas. Variation among species' responses was explained in part by our species groupings. The positive influence of protected areas was strongest for extra-large species and herbivores, while medium-sized species actually increased in the non-protected areas. The positive effect of grassland/floodplain cover, alternatively, was strongest for large species and carnivores and weakest for small species and herbivores, suggesting herbivore diversity is promoted by habitat heterogeneity. Synthesis and applications. Our results highlight the importance of protected areas and grasslands in maintaining biodiversity in southern Africa. We demonstrate the utility of hierarchical Bayesian models for assessing community, group and individual species' responses to anthropogenic and environmental variables. This framework can be used to map areas of high conservation value and predict impacts of land-use change. Our approach is particularly applicable to the growing number of camera trap studies world-wide, and we suggest broader application globally will likely result in reduced costs, improved efficiency and increased knowledge of wildlife communities.
Data from: Intraspecific niche variation drives abundance-occupancy relationships in freshwater fish communities
A positive relationship between occupancy and average local abundance of species is found in a variety of taxa, yet the mechanisms driving this association between abundance and occupancy are still enigmatic. Here we show that freshwater fishes exhibit a positive abundance-occupancy relationship across 125 Swedish lakes. For a subset of 9 species from 11 lakes, we estimated species-specific diet breadth from stable isotopes, within-lake habitat breadth from catch data for littoral and pelagic nets, adaptive potential from genetic diversity, abiotic niche position, and dispersal capacity. Average local abundance was mainly positively associated with both within-lake habitat and diet breadth, that is, species with larger intraspecific variation in niche space had higher abundances. No measure was a good predictor of occupancy, indicating that occupancy may be more directly related to abundance or abiotic conditions than to niche breadth per se. This study suggests a link between intraspecific niche variation and a positive abundance-occupancy relationship and implies that management of freshwater fish communities, whether to conserve threatened or control invasive species, should initially be aimed at niche processes.
Data from: Novel application of explicit dynamics occupancy models to ongoing aquatic invasions
1. Identification of suitable habitat where invasive species can establish is an important step towards controlling their spread. Accurate identification is difficult for new or slow invaders because unoccupied habitats may be suitable given enough time for dispersal and occupied habitats may prove to be unsuitable for establishment. 2. To identify suitable habitat of a recent invader, I used an explicit dynamics occupancy modeling framework to evaluate habitat covariates related to successful and failed establishments of American bullfrogs (Lithobates catesbeianus) within the Yellowstone River floodplain of Montana, USA from 2012-2016. 3. During this 5-year period, bullfrogs failed to establish at most sites they colonized. Bullfrog establishment was most likely to occur and least likely to fail at sites closest to human-modified ponds and lakes and those with emergent vegetation. These habitat covariates were generally associated with the presence of permanent water. 4. Suitable habitat for bullfrog establishment is abundant in the Yellowstone River floodplain, though many sites with suitable habitat remain uncolonized. Thus, the maximum distribution of bullfrogs is much greater than their current distribution. 5. Synthesis and applications. Focused control efforts on habitats with or proximate to permanent waters are most likely to reduce the potential for bullfrog establishment and spread. The novel application of explicit dynamics occupancy models is a useful and widely applicable tool for guiding management efforts towards those habitats where new or slow invaders are most likely to establish and persist.07-Aug-2017
Data from: Identifying drivers of spatial variation in occupancy with limited replication camera trap data
Occupancy models are widely used in camera trap studies to analyze species presence, abundance, and geographic distribution, among other important ecological quantities. These models account for imperfect detection using a latent variable to distinguish between true presence/absence and observed detection of a species. Under certain experimental setups, parameter estimation in a latent variable framework can be challenging. Several studies have issued guidelines on the number of independent replicated observations (surveys) needed for each unchanging occupancy field (season) to ensure reliable estimation. In this paper we present a spatio-temporal occupancy model, and show through a simulation study that it can be fit to data obtained from a \textit{single} survey per season, so long as the number of seasons is sufficiently large. We include an application using camera-trap data on the Thomson's gazelle in the Serengeti in Tanzania.
Data from: Not all surveillance data are created equal – a multi‐method dynamic occupancy approach to determine rabies elimination from wildlife
1. A necessary component of elimination programs for wildlife disease is effective surveillance. The ability to distinguish between disease freedom and non‐detection can mean the difference between a successful elimination campaign and new epizootics. Understanding the contribution of different surveillance methods helps to optimize and better allocate effort and develop more effective surveillance programs. 2.We evaluated the probability of rabies virus elimination (disease freedom) in an enzootic area with active management using dynamic occupancy modeling of 10 years of raccoon rabies virus (RABV) surveillance data (2006‐2015) collected from three states in the eastern USA. We estimated detection probability of RABV cases for each surveillance method (e.g., strange acting reports, road‐kill, surveillance‐trapped animals, nuisance animals, and public health samples) used by the USDA National Rabies Management Program. 3.Strange acting, found dead, and public health animals were the most likely to detect RABV when it was present, and generally detectability was higher in the fall‐winter compared to the spring‐summer. Found dead animals in the fall‐winter had the highest detection at 0.33 (95% CI: 0.20, 0.48). Nuisance animals had the lowest detection probabilities (~0.02). 4.Areas with oral rabies vaccination (ORV) management had reduced occurrence probability compared to enzootic areas without ORV management. RABV occurrence was positively associated with deciduous and mixed forests and medium to high developed areas, which are also areas with higher raccoon (Procyon lotor) densities. By combining occupancy and detection estimates we can create a probability of elimination surface that can be updated seasonally to provide guidance on areas managed for wildlife disease. 5.Synthesis and applications. Wildlife disease surveillance is often comprised of a combination of targeted and convenience‐based methods. Using a multi‐method analytical approach allows us to compare the relative strengths of these methods, providing guidance on resource allocation for surveillance actions. Applying this multi‐method approach in conjunction with dynamic occupancy analyses better informs management decisions by understanding ecological drivers of disease occurrence.
Data from: Assessing changes in distribution of the endangered snow leopard Panthera uncia and its wild prey over 2 decades in the Indian Himalaya through interview-based occupancy surveys
Understanding species distributions, patterns of change and threats can form the basis for assessing the conservation status of elusive species that are difficult to survey. The snow leopard Panthera uncia is the top predator of the Central and South Asian mountains. Knowledge of the distribution and status of this elusive felid and its wild prey is limited. Using recall-based key-informant interviews we estimated site use by snow leopards and their primary wild prey, blue sheep Pseudois nayaur and Asiatic ibex Capra sibirica, across two time periods (past: 1985–1992; recent: 2008–2012) in the state of Himachal Pradesh, India. We also conducted a threat assessment for the recent period. Probability of site use was similar across the two time periods for snow leopards, blue sheep and ibex, whereas for wild prey (blue sheep and ibex combined) overall there was an 8% contraction. Although our surveys were conducted in areas within the presumed distribution range of the snow leopard, we found snow leopards were using only 75% of the area (14,616 km2). Blue sheep and ibex had distinct distribution ranges. Snow leopards and their wild prey were not restricted to protected areas, which encompassed only 17% of their distribution within the study area. Migratory livestock grazing was pervasive across ibex distribution range and was the most widespread and serious conservation threat. Depredation by free-ranging dogs, and illegal hunting and wildlife trade were the other severe threats. Our results underscore the importance of community-based, landscape-scale conservation approaches and caution against reliance on geophysical and opinion-based distribution maps that have been used to estimate national and global snow leopard ranges.
Data from: Impact of prey occupancy and other ecological and anthropogenic factors on tiger distribution in Thailand's Western Forest Complex
1. Despite conservation efforts, large mammals such as tigers and their main prey, gaur, banteng, and sambar, are highly threatened and declining across their entire range. The only large viable source population of tigers in mainland Southeast Asia occurs in Thailand's Western Forest Complex (WEFCOM), an approximately 19,000 km2 landscape of 17 contiguous protected areas. 2. We used an occupancy modeling framework, which accounts for imperfect detection, to identify the factors that affect tiger distribution at the approximate scale of a female tiger's home range, 64 km2, and site use at a scale of 1 km2 in WEFCOM. At the larger scale, we estimated the proportion of sites occupied by tigers; at the finer scale, we identified the key variables that influence site-use and developed a predictive distribution map. At both scales, we examined key ecological and anthropogenic factors that help explain distribution and preferred habitat use. 3. WEFCOM is virtually only "half full" of tigers, it occupied 37% or 5,858 km2 of the landscape which was largely influenced by the combined presence of all three large prey species; in contrast, site use was most strongly influenced by presence of sambar. 4. By modeling occupancy while accounting for imperfect probability of detection, we established reliable benchmark data on the distribution of tigers. This study also identified factors that limit tiger distributions; which managers can then target to expand tiger distribution in WEFCOM and guide recovery elsewhere in Southeast Asia.
Nicaragua Occupancy Modeling
<p>The data and code provided here were used to produce the results contained in Jordan <em>et al</em>. (2016), submitted to PLOS ONE in August 2015.</p>
FIGURES 11–14 in Burrowing crickets endemic to summits in Mauritius (Orthoptera, Gryllidae): occupation of similar niches by species possibly derived from Australasian and African colonists
FIGURES 11–14. Gialaia (Eugialaia) strasbergi n. sp. 11, dorsal view of male holotype; 12, frontal view of face; 13, right hind tibia, outer view; 14 right hind tibia, inner view. Scale bars: 11: 10 mm; 2–4: 1 mm.
FIGURES 5–10. Taciturna baiderae n in Burrowing crickets endemic to summits in Mauritius (Orthoptera, Gryllidae): occupation of similar niches by species possibly derived from Australasian and African colonists
FIGURES 5–10. Taciturna baiderae n. sp. 5–7, male holotype genitalia in dorsal view; 5, right side view; 6, ventral view; 7. 8–10 female genitalia; 8–9, copulatory papilla in left side view; 8, and dorsal view; 9. 10, female last abdominal tergites (cerci removed). Scale bars: 1 mm.
FIGURES 1–4. Taciturna baiderae n in Burrowing crickets endemic to summits in Mauritius (Orthoptera, Gryllidae): occupation of similar niches by species possibly derived from Australasian and African colonists
FIGURES 1–4. Taciturna baiderae n. sp. 1, dorsal view of male; 2, frontal view of face; 3, right hind tibia, outer view; 4 right hind tibia, inner view. Scale bars: 1: 10 mm; 2–4: 1 mm.
FIGURES 15–20 in Burrowing crickets endemic to summits in Mauritius (Orthoptera, Gryllidae): occupation of similar niches by species possibly derived from Australasian and African colonists
FIGURES 15–20. Gialaia (Eugialaia) strasbergi n. sp. 15–17, male holotype genitalia in dorsal view; 15, right side view; 16, ventral view; 17. 18–20 female genitalia; 18–19 copulatory papilla in left side view; 18 and ventral view; 19. 20, female last abdominal tergites in side view. Scale bars: 1 mm.
The Teachers' Occupational Wellbeing Study
<p><em><span lang="EN-US">The Teachers’ Occupational Wellbeing Study</span></em><span lang="EN-US"> examines Finnish teachers’ occupational wellbeing, along with its predictors and outcomes, using a comprehensive survey questionnaire. Attention is also given to demographic aspects, such as regional differences and variation across educational levels and institutions. The aim is to use the findings to inform interventions, teacher training, and policy development.</span></p> <p><span lang="EN-US">The study began in spring 2020, at the onset of the COVID-19 pandemic and was started by Professor Katariina Salmela-Aro (1961-2025). Since then, data have been collected biannually every spring and autumn. In addition, the collection of longitudinal data began in spring 2024. This design enables the examination of both general trends (cross-sectional data) and individual-level changes over time (longitudinal study).</span></p> <p><span lang="EN-US">The survey data have been gathered in close collaboration with the <a href="https://www.oaj.fi/en/" target="_blank" rel="noopener">Trade Union of Education in Finland</a> (OAJ), consisting of responses from its members. The participants include teachers from across Finland and all educational levels.</span></p> <p><span lang="EN-US">The study is currently part of and funded by the EDUCA Flagship. More information about the study and its main results can be found on the <strong><a href="https://educaflagship.fi/en/research/study-of-teachers-work-related-well-being">EDUCA Flagship website </a></strong>and in </span>the supporting materials provided on this page. </p> <p>The project is currently led by University Lecturer Dr. Lauri Hietajärvi (<a href="mailto:lauri.hietajarvi@helsinki.fi" target="_self">lauri.hietajarvi@helsinki.fi</a>) from University of Helsinki, Finland, and managed by Dr. Olli-Pekka Heinimäki (<a href="mailto:lauri.hietajarvi@helsinki.fi" target="_self">olli-pekka.heinimaki@helsinki.fi</a>) from the same university. </p> <p>****</p> <p>Selected sections of datasets from 2020 to 2024 have been made openly available for anyone to use. Please read the relevant documentation carefully before working with the data. Should you have any questions, please contact the project management.</p> <p>The attached documents includes:</p> <ul> <li><strong>Teacher occupational wellbeing study data collection report – openly distributed version</strong>: Details about the project and data collection.</li> <li> <p><strong>Time Series 2020–2025</strong>: Key wellbeing trends across the full dataset (each time point).</p> </li> <li> <p><strong>Representativeness of the Data</strong>: Estimates of representativity at each measurement point.</p> </li> <li> <p><strong>Teacher_occupational_wellbeing_measures_overall</strong>: Summary of all measures included in the survey across timepoints.</p> </li> <li> <p><strong>README</strong>: Information about the open-access datasets and how they were generated from the original data.</p> </li> <li> <p><strong>2020_Spring.zip – 2024_Spring.zip</strong>: Nine open-access datasets collected at different time points (each provided in both .sav and .csv formats).</p> </li> <li> <p><strong>Scale documentation 2020–2024 open</strong>: Scale documents for each of the published nine datasets.</p> </li> </ul> <p> </p>
ANALYSIS OF the risk factors of MUSCULOSKELETAL DISORDERS: A dataset on most challenging operations in the garments industry in Bangladesh and reported occupational discomforts by the workers.
<p>This dataset comprises the perceived most challenging operations in the garment industry and the occupational discomfort faced by the workers. Collected through participant surveys, the dataset encompasses demographic information and work related parameters.</p><p>The primary objective of this dataset is to identify and analyse the prevalence of MSDs in the most challenging operation in the garment industry. Researchers, ergonomists, and occupational health professionals can leverage this dataset to explore further for mitigating MSD risks in the industry.</p><p>This dataset can be used for finding the Ergonomic interventions and workplace improvements. It can facilitate specific interventions for solving work related diseases among the garment workers.</p><p>Anyone can use this dataset with proper citation.</p><p>Note: This dataset adheres to ethical considerations, and participant information is anonymized to ensure confidentiality.</p>
Variability and Temporality of Lithic Production in Epipaleolithic to Early Neolithic Occupations at Cova del Vidre (Catalonia, Spain)
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SITE OCCUPANCY AND ABUNDANCE MODELS FOR ANALYZING MULTIPLE-VISIT DETECTION/NONDETECTION DATA
<p>Data and R codes for the paper: Site occupancy and abundance models for analyzing multiple-visit detection/nondetection data.</p>
Deaths and work related occupational injuries detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019
<p>Ensuring social data's reliability is essential in accurately evaluating social and economic impacts across geographical locations, economic sectors and stakeholder categories. Yet, the MRIO model utilized in our research (EXIOBASE) was hindered by out-of-date or significantly proxy fatality statistics, causing potential inaccuracies in our findings. We have comprehensively revised EXIOBASE fatality data to address this shortcoming, incorporating detailed, nation-specific, and up-to-date data. The update includes work-related fatal occupational injuries as well as fatalities associated with occupational exposure to a variety of 17 hazardous substances and conditions such as asbestos, arsenic, benzene, beryllium, cadmium, chromium, diesel engine exhaust, formaldehyde, nickel, polycyclic aromatic hydrocarbons, silica, sulfuric acid, trichloroethylene, asthmagens, particulate matter, gases and fumes, noise and ergonomic factors. Our methodological process is built on three pillars: data acquisition, raw data processing, and computation of fatal injuries by country, gender, year, and EXIOBASE economic sector.</p> <p>Data were sourced from the World Health Organization (WHO) (Pega et al., 2021) and Eurostat databases (Publications Office of the European Union, 2013). The WHO data was carefully screened based on specific criteria such as age above 15 years, gender, and fatal injuries only. Eurostat data provided granular information on work-related fatalities, classified by economic activities in the European Community (or NACE Rev.2 (Eurostat, 2008)). The WHO provided aggregate fatality data for 2010 and 2016. The strategy for allocating these deaths across Eurostat categories depended on the countries' geographical location, with different methods applied to European and non-European nations.</p> <p>For European nations, fluctuations in fatality numbers within a NACE Rev.2 sector mirrored the changes registered by Eurostat. For non-European countries, fatality figures were proportionally allocated across economic sectors split according to the NACE Rev.2 classification, reflecting the workforce size associated with each economic sector. Due to the scarcity of data for nations within Asia, America, or Africa, we adopted a regional approach, computing fatality ratios over each NACE Rev.2 category for each region by integrating data for available countries over a reference year. For 2010 and 2016, the aggregate fatality figures for nations within these three zones were established. Due to the temporal proximity of both reference years, we postulated a linear trend in the fatality count between these two years. The number of fatalities for a specific country, year, and per NACE Rev.2 activity was then calculated by applying the previously mentioned fatality ratio to the total number of deaths for that nation. Last, we applied the European annual ratios to their total mortality figures for the few countries that could not be classified as European or belonging to one of the aforementioned zones.</p> <p>The result is a comprehensive database that includes the number of fatalities (expressed in the number of deaths for work-related fatal occupational injuries and in Disability-adjusted life years (DALYs), for fatalities associated with occupational exposure to a specific risk factor), detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019, providing insights into work-related fatal injuries across different health effects and geographical regions.</p> <p> </p> <p><strong>Nomenclature</strong></p> <p>Archives:</p> <ul> <li><em>Concordance_ISIC_Exiobase.xlsx : </em>Concordance between the International Standard Industrial Classification (ISIC) and the exiobase sectors</li> <li><em>Concordance_ISO3_EXIO3.xlsx - </em>Concordance between the ISO3 code and the Exiobase regions</li> <li><em>Workforce_by_ISO3.csv - </em>Number of active persons per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)</li> <li><em>Workforce_by_EXIO3.csv - </em>Number of active persons per Exiobase region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)</li> <li><em>Death_ISO3.csv - </em>Number of death per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li><em>Death_EXIO3.csv - </em>Number of death per Exiobase Region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li><em>Death_EXIO3_region_exiobase_sector.csv - </em>Number of death per Exiobase Region (EXIO3 code), exiobase sector, Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li>Injuries_ISO3.zip - Archive of DALY per Country (ISO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)</li> <li>Injuries_EXIO3.zip - Archive of DALY per Exiobase Region (EXIO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)</li> <li><em>Workforce_EXIO3_sector_exiobase.xlsx - </em>Number of active persons per Exiobase region (EXIO3 code), per exiobase sector, Sex, Year (from 1991 to 2021).<br>This file is available here : Berthet, Etienne and Lavalley, Julien and Anquetil-Deck, Candy and Ballesteros, Fernanda and Stadler, Konstantin and Soytas, Ugur and Hauschild, Michael and Laurent, Alexis, Assessing the Social and Environmental Impacts of Critical Mineral Supply Chains for the Energy Transition in Europe. Available at SSRN: <a href="https://ssrn.com/abstract=4610350" target="_blank" rel="noopener noreferrer">https://ssrn.com/abstract=4610350</a> or <a href="http://dx.doi.org/10.2139/ssrn.4610350" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.2139/ssrn.4610350</a> </li> </ul> <p><strong>Content of Injuries_*.zip:</strong></p> <ul> <li>arsenic_*.csv</li> <li>asbestos_*.csv</li> <li>asthmagens_*.csv</li> <li>benzene_*.csv</li> <li>beryllium_*.csv</li> <li>cadmium_*.csv</li> <li>chromium_*.csv</li> <li>diesel_*.csv</li> <li>ergonomic_*.csv</li> <li>formaldehyde_*.csv</li> <li>gases_*.csv</li> <li>nickel_*.csv</li> <li>noise_*.csv</li> <li>polycyclic_*.csv</li> <li>silica_*.csv</li> <li>sulfuric_*.csv</li> <li>trichloro_*.csv</li> </ul>
AN EXTENSION OF N-MIXTURE OCCUPANCY MODELS FOR COUNT DATA
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AI4PROFHEALTH - Automatic Occupations Gazetteer and Occupations Co-occurrence with Clinical Concepts
<div> <div>This dataset comprises an occupations gazetteer generated with automatically extracted terminology from the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. In addition, this dataset also includes the co-occurrences among occupations, and of professions with other clinical concepts that have been extracted automatically, including diseases, procedures, symptoms, species, drugs, and neoplasia morphologies. </div> <br> <div>The repository contains a .zip file for all the results obtained from Mesinesp2 and another for the clinical cases, both containing a .tsv file for the professions gazetteer, one for the professions internal co-occurrence, and one for the professions co-occurrence with other semantic classes:</div> </div> <ul> <li><strong>mesinesp2_profession_gazetteer_and_cooccurrence.zip (Mesinesp2)</strong> <ul> <li>mesinesp2_professions_gazetteer.tsv</li> <li>mesinesp2_professions_cooccurrences.tsv</li> <li>mesinesp2_professions_cooccurrences_with_other_classes.tsv</li> </ul> </li> </ul> <ul> <li><strong>clinicalcases_profession_gazetteer_and_cooccurrence.zip (clinical cases)</strong> <ul> <li>clinicalcases_professions_gazetteer.tsv</li> <li>clinicalcases_professions_cooccurrences.tsv</li> <li>clinicalcases_professions_cooccurrences_with_other_classes.tsv</li> </ul> </li> </ul> <p>The gazetteer is divided into two columns, one with the name of the extracted terms and the other one with their total count.</p> <p>The professions co-occurrences .tsv file is divided into three columns. The first two columns contain the professions that co-occur, and the third column, named "count," indicates the number of co-occurrences calculated at the document level for each pair of detected entities. The professions co-occurrences with other classes .tsv file is structured in a similar manner, where the first column contains professions, the second one the clinical concepts that they co-occur with, the third one the counts of co-occurrences, and the fourth one the class to which the clinical concept belongs.</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>- Sergi Marsol Torrent (<sergi [dot] marsol [at] bsc [dot] es>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested, you might want to check out these corpora and resources:</p> <ul> <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> <li><a href="10.5281/zenodo.5070540" target="_blank" rel="noopener">MEDDOPROF corpus </a></li> <li><a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></li> </ul> <p><strong>Acknowledgements</strong></p> <p>This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (<strong>PID2020-119266RA-I0/AEI/10.13039/501100011033).</strong></p>
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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.