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12,662 results for “linking”
Linking diet switching to reproductive performance across populations of two Critically Endangered mammalian herbivores
<p>Data associated with Harvey Sky, N., Britnell, J., Antwis, R. <em>et al.</em> Linking diet switching to reproductive performance across populations of two critically endangered mammalian herbivores. <em>Commun Biol</em> <strong>7</strong>, 333 (2024). https://doi.org/10.1038/s42003-024-05983-3</p> <p>The data deposited here includes raw metabarcoding output fasta files and some processed metabarcoding and sample data in xslx files. We include a more detailed description of each file below.</p> <p>Data regarding Kenyan black rhino and Grevy’s zebra are treated as sensitive and confidential. There are therefore restrictions on the data that we can make available. Due to these confidentiality considerations, the sample data stored here does not include locations of sample collection within each reserve for either species, or the identity or breeding data for black rhino. It also only includes the final processed values for NDVI and rainfall. The remote sensing data is available from the repositories cited in the methods, but we cannot provide the shapefiles or other data used to calculate the final values for each sample. </p> <p><em><strong>Raw fasta files_plants.zip</strong></em></p> <p>A zipped folder containing the raw fasta files which were the output from the MiSeq sequencing of dietary plants in the faecal samples for both black rhino and Grevy's zebra. Within the zipped folder, the first part of the title of each fasta.gz file is the sample code (S1, S2, S3 etc), which allows you to cross reference these files with the sample data and processed sequencing data in the xslx files. Files with R1 in the title are foward reads, and R2 are reverse reads. </p> <p><em><strong>Raw fasta files_bacteria.zip</strong></em></p> <p>A zipped folder containing the raw fasta files which were the output from the MiSeq sequencing of microbiome bateria in the faecal samples for both black rhino and Grevy's zebra. Within the zipped folder, the first part of the title of each fasta.gz file is the sample code (S1, S2, S3 etc), which allows you to cross reference these files with the sample data and processed sequencing data in the xslx files. Files with R1 in the title are foward reads, and R2 are reverse reads. </p> <p><em><strong>Sample data and processed metabarcoding data_Black rhino.xlsx</strong></em></p> <p><em>Sample data tab</em></p> <p>The data that we are able to share that is associated with each black rhino sample.</p> <p>SampleID - The code used to identiy each sample which allows it be cross-referenced with other tabs and the fasta files. </p> <p>IndividualID - We are not able to share rhino names or other identifiers, but we have given each individual a unique number so that it can be seen which samples came from the same individuals. </p> <p>NDVI - Mean NDVI of each individual's area of utilisation in the 10-day period within which the sample was collected. The method used to calculate this is described in the methods of the article. </p> <p>Rainfall - Cumulative rainfall over the 30 days previous to sample collection for the 0.05 degree pixel under the sample. The method used to calculate this is described in the methods of the article. </p> <p>Season - Post is the post-rain sampling season June-July 2018. Pre is the pre-rain sampling season January-March 2019. </p> <p>Reserve - The reserve that the sample was collected on. </p> <p>Date - The date of sample collection. </p> <p>Dietary breadth - Shannon-Wiener index of dietary alpha diversity. The method used to calculate this is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p>Poaceae, Fabaceae, Ebenaceae - The relative abundance of each of these three dietary plant families that were the focus of our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p><em>Bacteria numbers of reads</em></p> <p>The number of reads assigned to each bacterial ASV found in each sample. </p> <p><em>Bacteria sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the microbiome metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p><em>Plant numbers of reads</em></p> <p>The number of reads assigned to each dietary plant ASV found in each sample. </p> <p><em>Plant sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the dietary plant metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p> </p> <p><em><strong>Sample data and processed metabarcoding data_Grevy's zebra.xlsx</strong></em></p> <p><em>Sample data tab</em></p> <p>The data that we are able to share that is associated with each Grevy's zebra sample.</p> <p>Sample ID - The code used to identiy each sample which allows it be cross-referenced with other tabs and the fasta files. </p> <p>NDVI - Mean NDVI of each individual's area of utilisation in the 10-day period within which the sample was collected. The method used to calculate this is described in the methods of the article. </p> <p>Rainfall - Cumulative rainfall over the 30 days previous to sample collection for the 0.05 degree pixel under the sample. The method used to calculate this is described in the methods of the article. </p> <p>Reserve - The reserve that the sample was collected on. </p> <p>Season - Post is the post-rain sampling season July-August 2018. Pre is the pre-rain sampling season January-February 2019. </p> <p>Date - The date of sample collection. </p> <p>Dietary breadth - Shannon-Wiener index of dietary alpha diversity. The method used to calculate this is described in the methods of the article. </p> <p>Poaceae, Fabaceae - The relative abundance of each of these two dietary plant families that were the focus of our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p>Indigofera - The relative abundance of each of this Fabaceae genus was included in our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p><em>Bacteria numbers of reads</em></p> <p>The number of reads assigned to each bacterial ASV found in each sample. </p> <p><em>Bacteria sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the microbiome metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p><em>Plant numbers of reads</em></p> <p>The number of reads assigned to each dietary plant ASV found in each sample. </p> <p><em>Plant sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the dietary plant metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p> </p>
Dataset for "Structural complexity and benthic metabolism: resolving the links between carbon cycling and biodiversity in restored seagrass meadows"
<p>This dataset accompanies the article "Structural complexity and benthic metabolism: resolving the links between carbon cycling and biodiversity in restored seagrass meadows" accepted for publication in Biogeosciences (https://doi.org/10.5194/bg-2023-173). The dataset includes benthic fluxes and biodiversity data in from bare sediments, restored <em>Zostera marina</em> and a natural <em>Z. marina</em> meadow collected in Gåsö, Sweden (58.2325, 11.3984) between July 05 - July 20, 2022. </p>
Data from: Seed preference is only weakly linked to seed-type-specific feeding performance in a songbird
<p>The dehusking of seeds by granivorous songbirds is a complex process that requires fast, coordinated and sensory-feedback-controlled movements of beak and tongue. Hence, efficient seed handling requires a high degree of sensorimotoric skill and behavioural flexibility, since seeds vary considerably in size, shape and husk structure. To deal with this variability, individuals might specialise on specific seed types, which could result in greater seed handling efficiency of the preferred seed type, but lower efficiency for other seed types. To test this, we assessed seed preferences of canaries (Serinus canaria) through food choice experiments and related these to data of feeding performance, seed handling skills and beak kinematics during feeding on small, spindle-shaped canary seeds and larger, spheroid-shaped hemp seeds. We found great variety in seed preferences among individuals: some had no clear preference, while others almost exclusively fed on hemp seeds, or even prioritized novel seed types (millet seed). Surprisingly, we only observed few and weak effects of seed preference on feeding efficiency. This suggests that either the ability to handle seeds efficiently can be readily applied across various seed types, or alternatively, it may indicate that achieving high levels of seed-specific handling skills does not require extensive practice.</p>
Linked collectors and determiners for: Collection of various insect orders, Natural History Museum, University of Oslo.
Natural history specimen data linked to collectors and determiners held within, "Collection of various insect orders, Natural History Museum, University of Oslo". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/4cfd80a7-d630-4d7f-89fb-d2d1e749a418">https://bionomia.net/dataset/4cfd80a7-d630-4d7f-89fb-d2d1e749a418</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4cfd80a7-d630-4d7f-89fb-d2d1e749a418">https://gbif.org/dataset/4cfd80a7-d630-4d7f-89fb-d2d1e749a418</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: NHM, Heteroptera.
Natural history specimen data linked to collectors and determiners held within, "NHM, Heteroptera". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/1acdfdd0-e43d-4d5d-92bc-7a2a60a03782">https://bionomia.net/dataset/1acdfdd0-e43d-4d5d-92bc-7a2a60a03782</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1acdfdd0-e43d-4d5d-92bc-7a2a60a03782">https://gbif.org/dataset/1acdfdd0-e43d-4d5d-92bc-7a2a60a03782</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Hymenoptera collection, Natural History Museum, University of Oslo.
Natural history specimen data linked to collectors and determiners held within, "Hymenoptera collection, Natural History Museum, University of Oslo". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/a4e7ff0a-f9c0-481a-88ed-5986ec86b24a">https://bionomia.net/dataset/a4e7ff0a-f9c0-481a-88ed-5986ec86b24a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a4e7ff0a-f9c0-481a-88ed-5986ec86b24a">https://gbif.org/dataset/a4e7ff0a-f9c0-481a-88ed-5986ec86b24a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Diptera collection, Natural History Museum, University of Oslo.
Natural history specimen data linked to collectors and determiners held within, "Diptera collection, Natural History Museum, University of Oslo". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/bb39604c-3731-49ce-a9dc-f3617d3d0d15">https://bionomia.net/dataset/bb39604c-3731-49ce-a9dc-f3617d3d0d15</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/bb39604c-3731-49ce-a9dc-f3617d3d0d15">https://gbif.org/dataset/bb39604c-3731-49ce-a9dc-f3617d3d0d15</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Realización de fichas de 12 especies de anfibios y 2 de reptiles.
Natural history specimen data linked to collectors and determiners held within, "Realización de fichas de 12 especies de anfibios y 2 de reptiles". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/45347233-6a4f-4843-b4f2-9573eef8dea0">https://bionomia.net/dataset/45347233-6a4f-4843-b4f2-9573eef8dea0</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/45347233-6a4f-4843-b4f2-9573eef8dea0">https://gbif.org/dataset/45347233-6a4f-4843-b4f2-9573eef8dea0</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Botanical Museum, Copenhagen. Database of type specimens.
Natural history specimen data linked to collectors and determiners held within, "Botanical Museum, Copenhagen. Database of type specimens". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/84d3829c-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/84d3829c-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/84d3829c-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/84d3829c-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: NMSA: Arthropod Collections (1900-2012).
Natural history specimen data linked to collectors and determiners held within, "NMSA: Arthropod Collections (1900-2012)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/aa4916c3-3dc7-4c90-9f8e-b81d986df153">https://bionomia.net/dataset/aa4916c3-3dc7-4c90-9f8e-b81d986df153</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/aa4916c3-3dc7-4c90-9f8e-b81d986df153">https://gbif.org/dataset/aa4916c3-3dc7-4c90-9f8e-b81d986df153</a>. Formatted as a Frictionless Data package.
Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard
<p>Species of the family Felidae (a group represented by cats) are thought to be obligate carnivores, specialized for hunting and consuming other animals. However, the detection of plants in the feces of felids raises questions about the role of plants in their diet. This is particularly true for the snow leopard (Panthera uncia), a big cat native to central and South Asia's high mountains. Our study aimed to comprehensively identify the prey and plants consumed by snow leopards as well as six other sympatric mammals. We applied DNA metabarcoding methods on 126 fecal samples collected from the Sarychat-Ertash Nature Reserve in Kyrgyzstan. We found that among the three most common plant families in snow leopard feces, Tamaricaceae (genus Myricaraia) was consumed often by snow leopards. The genus Myricaria frequently appeared in samples lacking any animal prey DNA, indicating that snow leopards might have consumed this plant especially when their digestive tracts were empty. We also observed a significant difference in plant composition between male and female snow leopards, and potentially between sampling seasons. We provide a comprehensive overview of the prey and plants detected in the feces of snow leopards and sympatric mammals. We believe our findings will help in formulating hypotheses and guiding future research to understand the adaptive significance of plant-eating behavior in felids and animal-plant relationships in the ecosystem.</p>
Cross-language Wikipedia link graph
<p>Wikipedia articles use Wikidata to list the links to the same article in other language versions. Therefore, each Wikipedia language edition stores the Wikidata Q-id for each article.</p> <p>This dataset constitutes a Wikipedia link graph where all the article identifiers are normalized to Wikidata Q-ids. It contains the normalized links from all Wikipedia language versions. Detailed link count statistics are attached. Note that articles that have no incoming nor outgoing links are not part of this graph.</p> <p>The format is as follows:</p> <p>Q-id of linking page (outgoing) <tab> Q-id of linked page (incoming) <tab> language version - dump date (20241101)</p> <p>This dataset was used to compute <a href="https://danker.s3.amazonaws.com/index.html">Wikidata PageRank</a>. More information can be found on the <a href="https://github.com/athalhammer/danker">danker</a> repository, where the source code of the link extraction as well as the PageRank computation is hosted.</p> <p>Example entries:<br><br>$ bzcat 2024-11-06.allwiki.links.bz2 | head</p> <p>1 107 ckbwiki-20241101<br>1 107 lawiki-20241101<br>1 107 ltwiki-20241101<br>1 107 tewiki-20241101<br>1 107 wuuwiki-20241101<br>1 111 hywwiki-20241101<br>1 11379 bat_smgwiki-20241101<br>1 11471 cdowiki-20241101<br>1 150 ckbwiki-20241101<br>1 150 lowiki-20241101</p> <p> </p> <p> </p>
Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids - Datasets
<p>This repository contains data from work "Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids"<br><br></p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/BFA.fasta/content" target="_blank" rel="noopener noreferrer">BFA.fasta</a> - fasta file containing sequences of bacterial functional amyloids used as a query for identification of novel bacterial functional amyloids in UHGP dataset</p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/UHGPAmyloids.fasta/content" target="_blank" rel="noopener noreferrer">UHGPAmyloids.fasta </a>- fasta file containing sequences of amyloids identified in UHGP dataset</p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/UHGPAmyloids.csv/content" target="_blank" rel="noopener noreferrer">UHGPAmyloids.csv</a> - csv file containing information about amyloids identified in UHGP</p> <p>Columns:<br>query_id - Uniprot id of the protein from BFA<br>query_gene_name - gene name of the protein from BFA<br>target_id - UHGP id of the found homolog<br>ProbabilityAMYPred-FRL - score obtained for the target_id sequence according to AMYPred-FRL<br>Archcandy - Prediction of beta arch motif for identified amyloid<br>Genome - UHGP genome id of the target_id<br>Localization - predicted subcellular localization with BUSCA for target_id sequence<br>Lineage - full taxonomy of the target_id sequence (which bacteria produced this specific sequence) </p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/PPIPositivePredictionsBetween_UHGPAmyloids_And_HPAIntestine_filtered.csv/content" target="_blank" rel="noopener noreferrer">PPIPositivePredictionsBetween_UHGPAmyloids_And_HPAIntestine_filtered.csv</a> - csv file contining inforamtions about predicted protein-protein interactions between UHGPAmyloids and human proteins expressed in guts</p> <p>Columns:<br>UHGPAmyloids_id - UHGPAmyloids id (same as target_id in UHGPAmyloids.csv)<br>hp_uniprot_name - Uniprot name of a human protein<br>negative and score - scores returned by ProteinPrompt softwawre for prediction of PPI<br>hp_uniprot_id - Uniprot id of a human protein<br>BFA_sp_uniprot_id - Uniprot id of a BFA source protein<br>BFA_sp_uniprot_name - gene name of a BFA source protein<br>UHGPAmyloids_localization - predicted subcellular localization with BUSCA for UHGPAmyloids_id sequence<br>UHGPAmyloids_lineage - full taxonomy of the UHGPAmyloids_id sequence (which bacteria produced this specific sequence) </p>
Data associated with the publication "Multi-decadal increase of forest burned area in Australia is linked to climate change"
<p>Data from various sources related to fires in Australian forests, associated with the publication "Multi-decadal increase of forest burned area in Australia is linked to climate change"</p>
Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance
<p>The below data are associated with our paper entitled "Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance."</p> <p>1) SNP-gene link predictions generated by pgBoost and existing methods SCENT (Sakaue et al. 2024 <em>Nat Genet</em>), Signac (Stuart et al. 2021 <em>Nat Methods</em>), ArchR (Granja et al. 2021 <em>Nat Genet</em>), and Cicero (Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><strong>pgBoost_scores.tsv.gz </strong>contains linking predictions made by pgBoost.</p> <p><strong>constituent_method_scores.tsv.gz</strong> contains linking predictions made by constituent methods.</p> <p><em><span>**NOTE: promoters (+/- 1kb from TSS) and candidate links >500kb are excluded from linking predictions (see manuscript)**</span></em></p> <p>Linking scores and percentiles are reported for each method (pgBoost score, SCENT FDR, Signac correlation, ArchR correlation, Cicero co-accessibility). Rank percentiles are computed as: 1 - (rank / n). When multiple links receive the same score, they are assigned the percentile of the top rank. Links unscored by each method (denoted by zeros* in the linking score column) are assigned a percentile equivalent to the percent of links unscored by the focal method. See the Methods section of the paper for further details on computing linking scores and summarizing scores across cell types and data sets.</p> <p>*Candidate links tested and assigned a co-accessibility of zero by the Cicero method are given a score of 1e-100 in the "Cicero" column to distinguish between unscored candidate links and candidate links assigned a partial correlation of zero (see Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><em>NOTE: The predictions associated with this release (version 2) were generated using an expanded set of data sets, an expanded training set, and corrected TSS coordinates.</em></p> <p>2) GWAS-derived evaluation SNP-gene link evaluation set.</p> <p><strong>gwas_evaluation.tsv</strong>: GWAS-derived evaluation SNP-gene link evaluation set. Column 1 provides SNP coordinates in the format <chr-start-end>. This evaluation framework was proposed by Weeks et al. 2024 <em>Nature Genetics</em> based on fine-mapping results from Kanai et al. <em>medrxiv</em> (see Methods: <em>Evaluation data sets</em> of Dorans et al.). "True" links (gold = 1) are non-coding variants fine-mapped to a focal trait (PIP > 0.1) with a coding variant for exactly one candidate gene within 1 Mb attaining PIP > 0.5 for the same trait. "False" links (gold = 0) are candidate SNP-gene pairs involving a SNP with a "true" link. This file of SNP-gene links was adapted from credible set-gene links <a href="https://github.com/Deylab999/GWAS_benchmark_IGVF/blob/bb91d08cc02d59cdd829eb1430569057ac26c5fe/V2G/ENCODE_E2G_2023/UKBiobank.ABCGene.anyabc.tsv">here</a> (the "truth" column defines true/false links) by identifying SNPs with PIP > 0.1 within each credible set-gene link.</p>
CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil - link to files.
<blockquote> <h3><strong>Version 1.2 (March 2025): </strong>Now with longer time series, expanded stream gauge coverage, meteorological data from additional sources, soil moisture time series, and observed rainfall time series from 11,853 rain gauges.</h3> </blockquote> <p> </p> <p>This is the CAMELS-BR dataset (Catchment Attributes and MEteorology for Large-sample Studies – Brazil) accompanying the paper: Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, <a href="https://doi.org/10.5194/essd-12-2075-2020" target="_blank" rel="noopener">https://doi.org/10.5194/essd-12-2075-2020</a>, 2020.</p> <p>CAMELS-BR provides daily observed streamflow time series for 4,025 stream gauges, daily observed rainfall for 11,853 rain gauges, daily meteorological time series and 65 attributes for 897 catchments in Brazil.</p> <p>The daily hydrometeorological time series include (i) observed streamflow accompanied by quality control information, (ii) precipitation extracted from five products, (iii) actual evapotranspiration extracted from three products, (iv) potential evapotranspiration extracted from two products, (v) reference evapotranspiration extracted from one product, (vi) minimum, mean, and maximum temperature extracted from three products, and (vii) soil moisture extracted from two products.</p> <p>The 65 catchment attributes cover properties such as (i) topography, (ii) climate, (iii) hydrology, (iv) land cover, (v) geology, (vi) soil, and (vii) human intervention.</p> <p>The data follow the same standards as other CAMELS datasets such as for the United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), and Great Britain (https://doi.org/10.5194/essd-2020-49).</p> <p><strong>How to cite:</strong> Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.</p> <p> </p> <h3><strong>Changes in CAMELS-BR version 1.2:</strong></h3> <p><strong>Major changes</strong></p> <ul> <li>Updated streamflow time series up to February 2025 (where available), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Some historical records have changed slightly due to ANA's quality control procedures. For eight gauges (see the readme.txt file), data are merged from 2025 and 2019 records (i.e. from CAMELS-BR version 1.1).</li> <li>Increased the stream gauge coverage to 4025 stream gauges (including both quality-controlled and non-quality-controlled series), up from 3679 in version 1.1.</li> <li>Added daily observed rainfall time series for 11853 rain gauges (not catchment averages), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Data include quality flags but are mostly not quality-controlled.</li> <li>Added a GeoPackage file with coordinates for 11853 rain gauges.</li> <li>Updated precipitation time series (catchment averages) up to October 2024 (where available). Now derived from: CHIRPS v2.0; CPC; ERA5-Land; MSWEP v2.8; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil – 864 catchments).</li> <li>Updated actual evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from: GLEAM v4.2a; ERA5-Land; and MGB-SA.</li> <li>Updated potential evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from GLEAM v4.2a and ERA5-Land.</li> <li>Added reference evapotranspiration time series (catchment averages). Derived from BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).</li> <li>Updated daily maximum, mean, and minimum temperature time series (catchment averages) up to October 2024 (where available). Now derived from: CPC; ERA5-Land; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).</li> <li>Added daily soil moisture time series (catchment averages) up to December 2024 (where available). Computed from GLEAM v4.2a and ERA5-Land.</li> <li>Improved meteorological data processing. Catchment averages now account for pixel fraction coverage.</li> <li>Reformatted meteorological time series files. Files now includes data from different products, with columns renamed for clarity.</li> <li>Hydrological and climatic indices were not updated, despite the new streamflow and meteorological data.</li> </ul> <p><strong>Minor changes</strong></p> <ul> <li>Updated stream gauge coordinates based on ANA's website on 27 February 2025. Coordinates were updated for 73 gauges in the 897 selected catchments and for 298 gauges across all catchments.</li> <li>Streamflow time series now include quality flag values from 0 to 7 (see the readme.txt file), previously from 0 to 4 in CAMELS-BR version 1.1. Flags from 5 to 7 may be present only in the last few years of data.</li> <li>Streamflow time series files for the 897 selected gauges now include values in both millimeters per day and cubic meters per second.</li> <li>Removed streamflow time series with fewer than 180 days of measurement.</li> <li>Converted gauge and catchment spatial data from Shapefile (.shp) to GeoPackage (.gpkg).</li> <li>Catchment areas computed by GSIM (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") flagged as "caution" for quality were set to "nan" due to low reliability.</li> <li>Updated catchment areas computed by ANA (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") to reflect the newest ANA's data from 27 February 2025. Streamflow values in millimeters per day remain unchanged because unit conversions rely on GSIM areas.</li> <li>Set catchment areas with zero squared kilometers, as computed by ANA, to "nan".</li> <li>Removed CPC daily mean temperature time series (catchment averages) because they were a simple average of minimum and maximum temperatures. For daily mean temperatures, refer to ERA5-Land data (now included) as they are computed from hourly data.</li> </ul> <p> </p>
Fig. 1 in Individual Movement Of Large Carabids As A Link For Activity Density Patterns In Various Forestry Treatments
Fig. 1. Mean activity density of Carabus scheidleri (a) and C. coriaceus (b) per sampling plot in different for- estry treatments (C = control, CC = clear-cutting, P = preparation cut- ting) between 2014 and 2018. Verti- cal lines represent a 95% confidence interval and capital letters above bars indicate significant differences based on Tukey's multiple compari- sons of means
Fig. 2 in Individual Movement Of Large Carabids As A Link For Activity Density Patterns In Various Forestry Treatments
Fig. 2. Movements of Carabus scheidleri (a) within and between forestry treatments (C = control, CC = clear-cutting, P = preparation cutting) based on CMR. The number next to the arrow corresponds with the number of recorded movements. Individual trajectories of radio-tracked C. coriaceus (b) in the experimental area, black dots represent the first release point for each trajectory
Topic Model for English Wikipedia's Biographies with list of all 1.8M articles linked to Wikidata
<p>A Genism LDA Topic Model of English Wikipedia biographical articles with list of all 1.8M articles, and some associated Wikidata information</p> <p>The model has 150 Topics.</p> <p>This model was developed in the process of isolating a set of visual arts biographical articles, as described in "Clowns in the Visual Artists: Topic Modeling Wikipedia and Wikidata" in the Spring 2022 issue of <em>Art Documentation - </em><a href="https://doi.org/10.1086/719999">https://doi.org/10.1086/719999</a></p> <p>Because names, nationalities, and birthdays are so prominent in biographies, the stopwords list removed 170,000 names, surnames, city names, place names, countries, days, months and other time related words (https://github.com/mandiberg/Names-Surnames-and-Countries-for-Stopwords). We also directly removed each article subject’s given and surname, which were almost always the most frequently occurring words in any given article. Otherwise, the model just produced topics based on nationality, and common names and surnames.</p> <p><strong>Files:</strong></p> <p>all_enwiki_bios_from_wikidata.csv<br> The list of all Wikidata items for humans with an enwiki page (e.g biographical article) was extracted from Wikidata JSON dump; list includes gender, occupation, and nationality. This was joined with the converted plaintext from an English Wikipedia dump. This data was downloaded in March 2021.</p> <p>Wikipedia Biographies LDA Topic Model human readable summary.csv<br> A human readable file with the 150 topics ranked by count of articles per topic from the 1.8M corpus. The most popular topics have categorical descriptions of the occupations of each cluster. Some are marked as not an occupation cluster. </p> <p>BoW_corpus.mm*<br> model_lda_full_Sep2_150Tv2*<br> These six files comprise the topic model. The code to load them is present in the python files. </p> <p>dict_full_Aug-28-2021<br> processed_docs_full_Aug-28-2021.txt<br> processed_docs_1000_Aug-18-2021.txt<br> These are the dictionary and processed corpuses required to build and implement the model using this code. The corpus with the first 1000 items is meant to be used for testing, as the full one is quite large and takes a long time to complete. </p> <p>topic-model-wikipedia-sept2021.zip<br> The code and settings used for creating and implementing this model are included in this zip and are also available here: https://github.com/mandiberg/topic-model-wikipedia</p> <p>All-Wikipedia-Biographies-with-topic1.csv<br> All-Wikipedia-Biographies-with-topic1and2.csv<br> These are the list of 1.8M biographies matched to topics. The "topic1" file just includes the first topic, this is a slightly larger list. The "topic1and2" file is slightly smaller because about 2% articles do not match to a second topic.</p> <p>Analysis-for-Clowns-Visual-Arts.zip<br> These are the raw data and final data produced for the "Clowns in the Visual Artists." Please see the article for context.</p>
A web-based tool for automatically linking clinical trials to their publications - example calculation
<p><strong>Objective</strong>: Evidence synthesis teams, physicians, policy makers, and patients and their families all have an interest in following the outcomes of clinical trials and would benefit from being able to evaluate both the results posted in trial registries and in the publications that arise from them. Manual searching for publications arising from a given trial is a laborious and uncertain process. We sought to create a statistical model to automatically identify PubMed articles likely to report clinical outcome results from each registered trial in ClinicalTrials.gov.</p> <p><strong>Materials and Methods</strong>: A machine learning-based model was trained on pairs (publications linked to specific registered trials). Multiple features were constructed based on the degree of matching between the PubMed article metadata and specific fields of the trial registry, as well as matching with the set of publications already known to be linked to that trial.</p> <p><strong>Results</strong>: Evaluation of the model using NCT-linked articles as gold standard showed that they tend to be top ranked (median best rank = 1.0), and 91% of them are ranked in the top ten.</p> <p><strong>Discussion</strong>: Based on this model, we have created a free, public web based tool at http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/TrialPubLinking/trial_pub_link_start.cgi that, given any registered trial in ClinicalTrials.gov, presents a ranked list of the PubMed articles in order of estimated probability that they report clinical outcome data from that trial. The tool should greatly facilitate studies of trial outcome results and their relation to the original trial designs.</p>
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.