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.
1,416
datasets available to search
ShareScore release 0.9.0
Dataset results
1,416 results for “counts”
Counting Words That Count: NLP for exploring Romanian Parliament Transcripts
<p>The data is obtained by scraping the cdep.ro website and contains 500k+ instances of speech from the parliament podium from 1996 to 2019. (Up to 2001 only the Chamber of Deputies published transcripts, after jan. 2001 Senate data is also included.) <br> <br> Columns: </p> <p>'index' - incremented integer as row number in order of scraping</p> <p>'title', - title of the scraped page, usually contains the name of the chamber and the exact data</p> <p>'name', - the name of the speaker, preappended with Mr. or Mrs. </p> <p>'speech', - the content of the speech, </p> <p>'gender', - the gender of the speaker</p> <p>'url' - the url to the profile of the speaker (useful for extending the data)</p> <p> </p> <p>CDEPs2.csv - Contains all transcripts, prone to parsing errors. 100% of data.</p> <p>validated-1.csv - Consists of 99% of original data. Less than 1% dropped for convenience. Ready to use.</p>
The North Pacific Eukaryotic Gene Catalog: clustered nucleotide metatranscripts and read counts
<p>This data continues with the development of the NPEGC Trinity <em>de novo</em> metatranscriptome assemblies from the protein data repository of <a href="../doi/10.5281/zenodo.10472589">The North Pacific Eukaryotic Gene Catalog</a>. The nucleotide sequences corresponding to the NPEGC cluster representatives are collected together in these repository files:<br><br><em>NPac.G1PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G2PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G3PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G3PA_diel.bf100.id99.nt.fasta.gz</em><br><em>NPac.D1PA.bf100.id99.nt.fasta.gz</em><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.<br><br>These nucleotide sequences have been sourced from the Zenodo repository for raw assemblies: <a href="../records/7332796">The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3</a></p> <p>Key processing steps are sampled below with links to the detailed code on the main github code repository: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog">https://github.com/armbrustlab/NPac_euk_gene_catalog</a></p> <p><br>Code used to build the kallisto indices and map the short reads against indices with kallisto are online in the code repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/NPEGC.nt_kallisto_counts.sh">NPEGC.nt_kallisto_counts.sh</a><br><br>There are two main steps:<br>1. Generate the kallisto index on the sets of clustered nucleotide metatranscripts<br>2. Map the short reads from environmental samples back to the assembly index</p> <p>As generated above, kallisto generates separate results files for each of the sample files. Even after compression, the total size of the tarballed kallisto output results directories are prohibitively large (>50GB). We use the code in this template R script to join together the 'est_count' estimated count values for the tens of millions of protein sequences in each project metatranscriptome, along with length.</p> <p>The code in this template script was used for each project: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/aggregate_kallisto_counts.R">aggregate_kallisto_counts.R</a><br>The output count files for each project are Gzip-compressed and uploaded to the NPEGC nucleotide data repository here: </p> <p><em>G1PA.raw.est_counts.csv.gz</em><br><em>G2PA.raw.est_counts.csv.gz</em><br><em>G3PA.raw.est_counts.csv.gz</em><br><em>G3PA_diel.raw.est_counts.csv.gz</em><br><em>D1PA.raw.est_counts.csv.gz</em></p>
An example RNA-seq count table using nf-core/rnaseq
<p>An example RNA-seq count table generated from data deposited under GSE40419 (lung adenocarcinoma study) and nf-core/rnaseq for testing downstream RNA-seq analyses.</p>
Morphological phytoplankton counts for the SOMLIT-Astan time-series (2007-2017)
<p>The present text file includes morphologic microscopic counts for SOMLIT-Astan station (Western English Channel). Samples (250 mL) of natural seawater intended for the acquisition of microscopic counts were preserved with acid Lugol’s iodine (Sournia, 1978, Guilloux et al. 2013), stored in the dark, and further processed between 15 days and up to 1 year after sampling.</p> <p>The morphological taxa contingency table was carefully examined to detect inconsistencies (e.g., abrupt changes in cell counts over the time series), and taxa for which identification was uncertain were grouped into broader taxonomic categories. For example, <em>Fragilaria</em> and <em>Brockmaniella</em> or <em>Cylindrotheca closterium</em> and <em>Nitzschia longissima</em> which are difficult to distinguished between each other, were considered in association in the same group of microscopic counts. The final morphological dataset consisted of counts of 146 taxonomical entities (taxa larger than 10µm in size) across 185 dates from 2007 to 2017.</p> <p>Raw microscopic counts were regularly stored in a local MS-Access database and uploaded in the RESOMAR PELAGOS (<a href="http://abims.sb-roscoff.fr/pelagos/">http://abims.sb-roscoff.fr/pelagos/</a>) national database.</p>
GDC-PANCAN.htseq_counts and associated meta-data no longer available from gdc.xenahubs.net
<p>The RNA-seq data and associated meta-data I used for my publications, downloaded from gdc.xenahubs.net but no longer available in this form (i.e. raw counts) from the website. </p>
Dataset for Algorithms and Complexity for Counting Configurations in Steiner Triple Systems
<p>This dataset contains the classification of full n-line configurations (for all n <= 13, filename: "full_line_config_<n>.txt.gz") and w_3 configurations (for all w <= 16, filename: "w_3_config_<w>.txt.gz") together with the sizes of minimum generating sets. Each file lists "m<s>" so that s is the size of the minimum generating set of the subsequent configuration, which is denoted by writing the points of each of its lines row-wise. For example</p> <p>m3<br> 0 1 4<br> 0 2 6<br> 0 3 5<br> 1 2 5<br> 1 3 6<br> 2 3 4<br> 4 5 6</p> <p>is the Fano plane and its minimum generating set has size 3.</p> <p>Additionally, the file "fulllineconjecture.txt.gz" contains the 623 Steiner triple systems of order 25 (i.e., all rows which contain curly brackets) used in Theorem 7 in the paper below, followed by a row starting with 1 and then describing the number of occurrences of all 179 full n-line configurations for n <= 8, i.e., first the number of occurrences of Pasch configurations, then mitre configurations, then the 5 full 6-line configurations, the 19 full 7-line configurations, and finally the 153 full 8-line configurations contained in the STS(25) in the preceding row. The ordering follows the ordering within the files "full_line_config_<n>.txt.gz". This file is built so that omitting all lines with curly brackets is a valid gap code and results in a prove of said theorem (i.e., zgrep -v "{" fulllineconjecture.txt.gz | gap yields 180).</p> <p><br> Further details can be found in the corresponding publication</p> <p>"Algorithms and Complexity for Counting Configurations in Steiner Triple Systems"</p> <p>by Daniel Heinlein and Patric R. J. Östergård.</p> <p>All files are compressed with gzip.</p>
German CBOW FastText embeddings with min count 250
<p>FastText embeddings built from Common Crawl german dataset</p> <table> <caption>Parameters</caption> <thead> <tr> <th scope="col">Parameters</th> <th scope="col">Value(s)</th> </tr> </thead> <tbody> <tr> <td>Dimensions</td> <td>256 and 384</td> </tr> <tr> <td>Context window</td> <td>5</td> </tr> <tr> <td>Negative sampled</td> <td>10</td> </tr> <tr> <td>Epochs</td> <td>1</td> </tr> <tr> <td>Number of buckets</td> <td>131072 or 262144</td> </tr> <tr> <td>Min n</td> <td>3</td> </tr> <tr> <td>Max n</td> <td>6</td> </tr> </tbody> </table>
Waterbirds counts in the Drugeon bassin, France (game reserves and hunting areas) in 2020
<p>This dataset describes waterbird observations performed in game reserves and in hunting areas of the Drugeon river basin, France, in 2020.</p> <p>For each game reserve, a similar hunting area was selected in the same or an adjacent commune. If the hunting reserve was a stretch of river, as in Bonnevaux or Houtaud, another stretch of river was surveyed for comparison. In Frasne, the hunting reserve covers the Lothaud pond. Therefore, the adjacent Lucien pond was surveyed as a huntable area. The other nine wetland reserves are located in marshes and therefore nine other marshes were surveyed. Once this work was done, 10 other wetlands were also surveyed to complete the study. In total, 12 hunting reserves and 22 huntable areas, all in wetlands, were surveyed twice during the autumn of 2020. One of the 13 reserves was not surveyed (just forgotten in the sampling plan). The first survey was carried out between 24 August and 5 September, i.e. just before the opening of the waterfowl hunting season on 6 September. The second pass was carried out between 19 and 29 October, i.e. 44 to 54 days after the opening of the hunting season.</p> <p>Each sector was surveyed on foot, with round trips spaced at approximately 20 m intervals. The use of the Caynax application on Android allowed to control the location of the route, the regular spacing of the passages and the length of the route taken on each wetland. For both ponds, the water bodies were circled and birds were counted along the banks and on the water table. All characteristic wetland birds were noted, specifying whether they had been seen landing or in transit flight. Only birds landing are taken into account in this study. The observations were then recorded in an Excel table with the name of the area, its hunting status, the surface area, the number of kilometres covered during the survey and the date of the survey (see table <a href="https://zenodo.org/api/files/1546581e-0a8a-4966-9250-cc37eeafb414/db0.txt">db0.txt</a> and article cited below for more details).</p> <p><strong>FILE DESCRIPTION:</strong></p> <p><a href="https://zenodo.org/record/7539822/files/CartePAIGN_BB.jpg?download=1">CartePAIGN_BB.jpg </a>map of the areas studied. Green, game reserves sampled; red , hunting areas sampled: blue, one game reserve not sampled.</p> <p><a href="https://zenodo.org/record/7539822/files/db0.txt?download=1">db0.txt</a> counts for each area sampled</p> <ul> <li>com, commune</li> <li>lieu, place name</li> <li>pair, pair ID</li> <li>res, game reserve (OUI = yes, NON = no)</li> <li>ha, area sampled</li> <li>km, number of kilometers walked</li> <li>mois, month</li> <li>The next 28 colums are species ID, for full names see table <a href="https://zenodo.org/record/7539822/files/Statuts.txt?download=1">Statuts.txt</a>.</li> </ul> <p><a href="https://zenodo.org/record/7539822/files/Game reserves and hunting areas sampled.kml?download=1">Game reserves and hunting areas sampled.kml </a> kml file of the areas sampled (game reserves in green, hunting areas sampled in red; in blue, one reserve not sampled)</p> <p><a href="https://zenodo.org/record/7539822/files/Statuts.txt?download=1">Statuts.txt</a> list of species recorded</p> <ul> <li>espece, specie name (in French)</li> <li>nom latin, latin name</li> <li>nom anglais, species name (in English)</li> <li>sp, species ID</li> <li>protection, protection status: Protégée, protected; Chassable, huntable</li> <li>migrateur, migratory status: Migrateur tardif, late migratory; Migrateur précoce, early migratory</li> </ul> <p><strong>SUPPLEMENTARY FILES</strong></p> <p><a href="http://zenodo.org/record/7539822/files/R_code_for_Analysis.zip?download=1">R_code_for_analysis.zip</a> Workflow (in French) and R code for the analyses carried out in the article Michelat & Giraudoux (2023).</p> <p> </p>
Frequency counts of all adjectives and past participles in the Dutch LassyGroot corpus
<p>This record contains two datasets with the frequency counts of all adjectives and all past participles in the Dutch <a href="https://taalmaterialen.ivdnt.org/download/tstc-lassy-groot-corpus/">LassyGroot</a> corpus. Some of the tokenisation has been cleaned up. For more information, visit the <a href="https://github.com/AntheSevenants/Adjectiveness">GitHub repository</a>.</p>
CMIP6 variable counts per model
<p>The number of variables (y-axis) published for the historical simulation by each model (as represented in the DKRZ Earth System Grid Federation (ESGF) index node March 2022) is shown in blue columns against the model rank, where models are ranked in order of decreasing variable count. Also shown, in orange, is the number of variables which are included by all models up to the given rank.</p> <p>Data provided by Martin Juckes, image created by Beth Dingley</p>
Curated GWAS summary statistics on African ancestry on 19 blood count traits and glycemic traits (hg38)
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p> </p>
Curated GWAS summary statistics on East Asian ancestry on 19 blood count traits and glycemic traits
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p>Full description of the method used to derive this dataset can be found in </p>
Data for 2D Signal Estimation for Sparse Distributed Target Photon Counting Data
<p>Data used in publication of 2D Signal Estimation for Sparse Distributed Target Photon Counting Data</p> <p>Data provided consists of:</p> <p>raw and PTV processed MicroPulse DIAL (MPD) data (10.26023/MX0D-Z722-M406)</p> <p>simulated photon counting data and the processed results</p> <p> </p>
Invasive rat eradication effects on seedling counts on Palmyra Atoll, 2004-2016
In order to investigate the impacts of rat eradication on plant communities on the Palmyra Atoll, Line Islands, Central Pacific Ocean ecosystem, a study was conducted surveying the native and non-native tree and palm seedling abundance before and after an eradication event which took place in June 2011. The common plant species examined were P. grandis and C. nucifera, while the less-common species were B. asiatica, C. subcordata, H. sonora, G. speciosa, N. oppositifolium, and C. inophyllum. Data for common plant species were collected in 2007, 2011, 2012, 2014, and 2016, and for less-common species in 2004, 2011, 2012, 2014, and 2016. Data were collected via survey methods; 55 transect sites were examined for the common species, and between 49 to 55 sites were examined depending on the year, for the less-common species.
Santa Barbara Channel Marine BON: Gray Whales Count
This dataset documents the passage of gray whales (Eschrichtius robustus) migrating northbound since 2007, through the nearshore areas of the Santa Barbara Channel, along a corridor extending approximately 3 nautical miles (nm) from the mainland shore. The data is collected by a non-profit organization Gray Whales Count (http://www.graywhalescount.org/GWC/The_Count/The_Count.htmt), which is a research and education project. The survey is conducted at the Coal Oil Point Reserve in Goleta, California, USA. The coastline runs east-west, with northbound whales traveling west, left to right across our Point towards Point Conception. Every year, we survey 98 consecutive days from early February through mid-May. Conditions permitting, each survey-day begins at 9 AM and ends usually at 5 PM.
Annual Point Count Breeding Bird Survey at Pepperwood Preserve in the California Coast Ranges 2007-2019
The Dwight Center for Conservation Science at Pepperwood is an ecological institute dedicated to educating, engaging, and inspiring our community through habitat preservation, science-based conservation, leading-edge research, and interdisciplinary educational programs. Our mission is to steward the life and landscapes of the 3,200-acre Pepperwood Preserve and to advance science-based conservation of ecosystems throughout our region and beyond. The Pepperwood Breeding Bird Survey was initiated in the spring of 2007 with the goal of establishing a set of baseline bird community data that would be built upon for years to come. Four transects (totaling 38 points) are surveyed annually using standardized five-minute point count protocols outlined by the Point Reyes Bird Observatory (now called Point Blue Conservation Science; Ballard et al. 2003) and the Handbook of Field Methods for Monitoring Landbirds (Ralph et al. 1993). Surveys are conducted by experienced volunteers during the breeding season starting in late April and ending in June, with each transect surveyed a total of three times. The Rogers Creek and Martin Creek transects were established in 2007. The Weimar Flat and Pepperwood Road transects were established in 2008 and 2012, respectively, to ensure comprehensive coverage across the various habitats that occur at the preserve including Douglas-fir forest, mixed hardwood forest, oak woodland/forest, chaparral, and open grasslands. This dataset includes data collected between 2007-2019.
SGS-LTER Long-term Monitoring Project: Spotlight Rabbit Count on the Central Plains Experimental Range, Nunn, Colorado, USA 1994-2006, ARS Study Number 98 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/327/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/136/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83448. Rabbits are the most important small-mammal herbivores in shortgrass steppe, and may significant influence the physiognomy and population dynamics of herbaceous plants and woody shrubs. Rabbits also are the most important prey of mammalian carnivores such as coyotes and large raptors such as golden eagles and great horned owls. Two hares (Lepus californicus, L. townsendii) and one cottontail rabbit (Sylvilagus audubonii) occur in shortgrass steppe. In 1994, we initiated long-term studies to track changes in relative abundance of rabbits on the Central Plains Experimental Range (CPER). On four nights each year (one night each season, usually on new moon nights in January,
Harvest data including the shoot leaf area index, position in the canopy, and shoot and plant tissue area, count and mass for each shoot harvested at three levels in the canopy from 19 1m x 1m plots near LTER Shrub plots, Toolik Field Station, AK 2012.
Leaf and plant tissue area and mass from shoots harvested from 19 1m x 1m point frame plots near Toolik Field Station, AK during the summer of 2012. Six shoots were harvested from each plot, two from each canopy layer: upper, middle, and low. Each shoot came from a different plant, and the species selected was based on the species dominant in that canopy layer. The leaf area and mass were used to correct A/Ci and light response curves taken on each shoot [data published separately]. At the time of collection, the location relative to the point frame, height, and leaf area index (LAI) of each shoot was measured; those data are included here.
Identification of taxa, counts, biomass and carbon biomass calculations for phytoplankton and ciliates from artic lakes near Toolik Lake LTER in summer 1998.
Identification of taxa, counts, biomass and carbon biomass calculations for phytoplankton and ciliates from artic lakes near Toolik Lake LTER in summer 1998.
Vegetation Plots of the Bonanza Creek LTER Control Plots: Species Count (1975 - 2004)
These data are the vegetation datasets for 27 LTER sites in Bonanza Creek Experimental Forest. The 27 sites are divided into three replicates for six primary successional stages on the floodplains (3 replicates X 6 successional stages = 18 sites) and three replicates for three secondary successional stages in the uplands (3 replicates X 3 successional stages = 9 sites). Data include: 1) Visual estimates of percent cover, 2) Stem counts (the number of individuals/species), and 3) Heights (cm) for "tall shrub species" in twenty 4 m2 plots. Shrubs are considered "Tall shrubs" if they are Salix sp., Alnus sp., Rosa acicularis, Viburnum edule, Betula nana, Betula glandulosa, or Rubus idaeus. Initial colonziations plots (FP0s, SL1s, HR1A) were remeasured every year. Early successional plots were remeasured every 2-4 years. Later succesional plots were remeasured approximately every five years. For a detail schedule of plot measurements please see the file: Vegetation Monitoring Schedule.xls Although most sites were established in 1988 some sites have vegetation plots that have been sampled periodically since 1965. In 2006 shrub data collection was changed to a transect method of sampling. These data can be found in the file: <a href="http://www.lter.uaf.edu/data_detail.cfm?datafile_pkey=530"> Shrub, Seedling and Sapling Density at Bonanza Creek LTER Research Sites (2006-Present) </a>.
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.