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”
Soil biota counts from the Elevational Transect (ET) experiment, McMurdo Dry Valleys, Antarctica (1993-2024, ongoing)
Investigation of the effect of elevation and topography on soil biota and soil properties was part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. The number of soil organisms (nematodes, rotifers and tardigrades), divided by species, sex and maturity was monitored at 3 elevations, initially in Taylor Valley (1993) then Garwood and Miers Valleys (2012) in order to accomplish this.
Soil biota counts from the Biotic Effects Experiment (BEE), McMurdo Dry Valleys, Antarctica (1999-2024, ongoing)
Increases in soil temperature and moisture may change the bioavailability of essential elements by altering solubility and diffusion rates in soils, or by changing the amounts of organic compounds. Long-term experiments in the Bonney, Hoare and Fryxell basins have been established with three treatments: 1) increased moisture, 2) soil warming (ITEX chambers), and 3) soil warming + increased moisture. The identification and abundance of soil biota are reported. Only control treatments have been measured since the 2014-2015 austral summer.
MCR LTER: Coral Reef Resilience: North Shore Herbivorous Fish Counts, Habitat Associations, and Substrate, 2010
These data describe the species abundance, size distributions, and habitat associations of roving herbivorous fishes (fishes belonging to the families, Acanthuridae, Scaridae, and Siganidae) found in different habitats in the lagoon and forereef on the north shore of Moorea. Adult fishes and large juveniles were counted (and their size estimated) by a SCUBA diver or snorkeler on thirty-four 50 m by 5 m wide transects. After counting large fishes, the entire transect was swam a second time, with the diver looking exclusively for small juvenile fishes on a 1 m swath. In addition to recording the species identity and estimated size of each juvenile encountered, the diver also recorded the particular microhabitat each individual or group of individuals was associated with. Finally, to quantify the relative availability of different types of microhabitat, the diver conducted point contacts where the primary benthic substrate was identified at regular (1 m) intervals on the same transect. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Butterfly heavy metal content, wing size, egg count, and brain mass in the Minneapolis-St. Paul (MSP) Metropolitan Area
We collected 26 common species of butterflies across a gradient of lead pollution in the Twin Cities metropolitan area (Minneapolis and St. Paul, MN, USA). We measured their thorax lead concentrations and their body condition including wing area, number of eggs, and brain mass. We also quantified lead in the soil, host plant leaves, and air (through lichen bio-monitors) at sites where the butterflies were collected.
The stations used for counting birds in hayed salt marsh vs reference salt marsh sites, Rowley, MA, PIE LTER.
The birding observation stations used for counting birds in hayed marsh and reference marsh sites near Stackyard Rd. and Patmos Rd, Rowley, Massachusetts.
Mummichog (Fundulus heteroclitus) counts and gut content analysis from lift trap transect collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
The lift traps were used to capture mummichogs accessing the high marsh platform. Mummichogs were collected to study the effect of marsh-edge geomorphology on mummichog distribution and foraging. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.
Invert species counts and density along transects on the high marsh along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
At PIE, mummichog (Fundulus heteroclitus) use the spring-cycle high tides to access the flooded high marsh platform and consume invertebrate prey. Invertebrate surveys were conducted before and after spring tides that flooded the high marsh area to determine the effect of mummichog predation that occurs on the high marsh during the flood events and to assess the impact of low marsh geomorphology on top-down control by mummichog. These data were included in part of the study "Cross-habitat access modifies the ‘trophic relay’ in New England saltmarsh ecosystems” (Lesser et al. 2021) as well as a part of a NEU Three Seas Master's thesis.
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S1 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S0 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>
Gene expression count matrix for 4 T cell subtypes from ROSMAP participants
<p><span>Peripheral blood mononuclear cells (PBMCs) from participants in the Rush Religious Orders Study/Memory and Aging Project (ROSMAP) were isolated by Ficoll gradient centrifugation, then sorted by high-speed flow cytometry into the following T cell subtypes:<span> </span>CD4+CD45RO-, CD4+CD45RO+, CD8+CD45RO-, and CD8+CD45RO+.<span> </span>Total RNA was extracted using buffer TCL (Qiagen), then RNA-seq libraries were prepared according to the Single Cell RNA Barcoding and Sequencing method originally developed for single-cell RNA-seq</span><span>, adapted for extracted total RNA.<span> </span>RNA libraries were collected on a single 384-well plate and sequenced on the Illumina HiSeq </span><span>using the High-throughput 3<span>’</span> Digital Gene Expression (DGE) library</span><span>.<span> The "RNA count matrix" file is the raw counts from the 384-well plate, while the "ROSMAP_Tcell_DGE_PlateMap" file contains metadata for the wells on the plate, by well position.</span></span></p>
JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets
<h2>JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets (Finalised 8th of April 2024)</h2> <h3>How to cite these data:</h3> <p>The full data were published in Sproson <em>et al.</em>, 2024.</p> <p>Sproson AD, Yokoyama Y, Miyairi Y, Aze T, Clementi VJ, Riechelson H, Bova SC, Rosenthal Y, Childress LB & Expedition 379T Scientists. Near-synchronous Northern Hemisphere and Patagonian ice sheet variation over the last glacial cycle. <em>Nature Geoscience</em> <a href="https://doi.org/10.1038/s41561-024-01436-y">https://doi.org/10.1038/s41561-024-01436-y</a> (2024).</p> <h3>Files:</h3> <p><strong>Supplementary Table 1: </strong>Multiple linear regression results between 10Be/9Be ratios and sedimentation rate, K/Ca, Fe/Ca, Al/Ti (this study), Green/Blue (Li <em>et al.</em>, 2022), and Global Mean Sea Level (Lambeck <em>et al.</em>, 2014). The multiple linear regression was calculated using the MATLAB(R) function “regress”.</p> <p><strong>Supplementary Table 2: </strong> Age-depth model and beryllium isotope measurements for Site J1002. The age-depth model was calculated from radiocarbon dates and oxygen isotope stratigraphy (Li <em>et al.</em>, 2022) using the BIGMACS modelling routine (Lee <em>et al.</em>, 2022). Beryllium-9 and beryllium-10 were measured by Adam D. Sproson by HR-ICP-MS and AMS at the Atmosphere and Ocean Research Institute (Sproson <em>et al.</em>, 2021) and University of Tokyo (Matsuzaki et al., 2007), respectively. 10Be/9Be* ratios were corrected for 10Be paleo-production following von Blanckenburg <em>et al.</em> (2015). </p> <p><strong>Supplementary Table 3:</strong> X-ray Fluorescence Ti, K, Fe, Ca, and Al element counts per second for Site J1002 measured at the Lamont-Doherty Earth Observatory by Vincent J. Clementi.</p> <p><strong>Supplementary Table 4: </strong>Carbon isotope measurements for the benthic foraminifera, U. peregrina, measured at Rutgers University by Vincent J. Clementi.</p> <h3>Format:</h3> <p>Depth (m CCSF-A) = core composite depth below seafloor.</p> <p>Calendar age (kyr BP) = age in thousand years before present.</p> <p>[10Be]reac, [9Be]reac = the concentration of 10Be and 9Be in the reactive phase of marine sediments. </p> <p>Sample ID = expedition sample designation specifying hole (e.g., A), core number (e.g., 1), type (i.e., H), section number (e.g., 1), and then section half (i.e., W).</p> <p>σ = standard deviation.</p> <h3>References:</h3> <p>Lambeck K, Rouby H, Purcell A, Sun Y, Sambridge M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. <em>Proceedings of the National Academy of Sciences.</em> 2014;111(43):15296-15303. </p> <p>Lee T, Rand D, Lisiecki LE, Gebbie G, Lawrence CE. Bayesian age models and stacks: Combining age inferences from radiocarbon and benthic δ18O stratigraphic alignment. <em>EGUsphere.</em> 2022;2022:1-29.</p> <p>Li C, Clementi VJ, Bova SC, <em>et al.</em> The sediment green‐blue color ratio as a proxy for biogenic silica productivity along the Chilean Margin. <em>Geochemistry, Geophysics, Geosystems</em>. 2022:e2022GC010350. </p> <p>Matsuzaki H, Nakano C, Tsuchiya Y, <em>et al.</em> Multi-nuclide AMS performances at MALT. <em>Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms.</em> 2007;259(1):36-40. </p> <p>Sproson AD, Aze T, Behrens B, Yokoyama Y. Initial measurement of beryllium‐9 using high‐resolution inductively coupled plasma mass spectrometry allows for more precise applications of the beryllium isotope system within the Earth Sciences. <em>Rapid Communications in Mass Spectrometry.</em> 2021;35(8):e9059. </p> <p>Von Blanckenburg F, Bouchez J, Ibarra DE, Maher K. Stable runoff and weathering fluxes into the oceans over Quaternary climate cycles. <em>Nature Geoscience. </em>2015;8(7):538-542. </p>
Aqueous geochemical measurements and speciation calculations with concurrent copper resistance gene counts from sediment metagenomes over a seasonal cycle from 2015 to 2016 on Silver Bow Creek and Blacktail Creek near Butte, MT
<p>This dataset contains information from concurrently gathered geochemical and metagenomic samples collected from Silver Bow Creek and Blacktail Creek near Butte, MT (SBC/BC) during 2015 and 2016. SBC/BC is recovering from metal contamination related to extensive mining in the area. Full geochemical measurements, geochemical speciation calculations, and gene counts of sequences mapping to copper resistance genes using MG-RAST are included. </p>
Bird counts in the Central Forest Biosphere Reserve at 2021
<p>Учеты проводились авторами c 20 мая по 02 июля 2021 г. на территории Центрально-Лесного биосферного заповедника и в его охранной зоне. Учитывали на разовых маршрутах с фиксацией всех птиц и дальностей обнаружения. В каждом местообитании проходили за 2 недели по 3-7 км. учета (круг). Расчет обилия птиц проведен раздельно по дальностям обнаружения (Равкин, Ливанов, 2008). Данные по кругам осреднены. Всего пройдено с учетом 154 км полного учета и 20 частичного. Часть учетов (70 км) сопровождались картографированием мест размещения птиц, фиксацией учетных маршрутов и времени встреч с использованием технологии мобильных геоинформационных систем. Для этого применяли комплекс программ с открытым исходным кодом NextGIS QGIS (<a href="https://nextgis.ru/nextgis-qgis/">https://nextgis.ru/nextgis-qgis/</a>).</p> <p>Выделено 9 типов местообитаний: 1) неморальные ельники 2) бореальные ельники, 3) смешанные леса, 4) мелколиственные леса, 5) сфагновые сосняки, 6)угнетенный сосняк на верховом болоте, 7) открытое верховое болото, 8) луга с перелесками, 9) населенный пункт Б.Федоровское,</p> <p>Собранные материалы переданы в банк данных коллективного пользования Института Систематики и Экологии Животных СО РАН <a href="http://eco.nsc.ru/lab4.html">http://eco.nsc.ru/lab4.html</a></p> <p>Научная номенклатура и русскоязычная орнитонимика приведены по Списку птиц Российской Федерации (Коблик и др., 2006). </p>
Gene expression count data from human post-mortem spinal cord
<p>Gene expression data from human post-mortem tissue for three spinal cord sections (cervical, thoracic and lumbar) from amyotrophic lateral sclerosis (ALS) patients and non-neurological disease controls. RNA sequencing performed as part of the New York Genome Center ALS Consortium.</p> <p>Analysis workbooks: <a href="https://jackhump.github.io/ALS_SpinalCord_QTLs/">https://jackhump.github.io/ALS_SpinalCord_QTLs/</a> </p> <p>Preprint describing results: <a href="https://www.medrxiv.org/content/10.1101/2021.08.31.21262682v1">https://www.medrxiv.org/content/10.1101/2021.08.31.21262682v1</a> </p> <p><strong>Sample sizes:</strong></p> <table align="left"> <tbody> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p><strong>Control</strong></p> </td> <td> <p><strong>ALS</strong></p> </td> </tr> <tr> <td> <p>Cervical</p> </td> <td> <p>35</p> </td> <td> <p>139</p> </td> </tr> <tr> <td> <p>Thoracic </p> </td> <td> <p>10</p> </td> <td> <p>42</p> </td> </tr> <tr> <td> <p>Lumbar</p> </td> <td> <p>32</p> </td> <td> <p>122</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><strong>Library preparation</strong></p> <p>RNA was extracted from flash-frozen postmortem tissue using TRIzol (Thermo Fisher Scientific) chloroform, followed by column purification (RNeasy Minikit, QIAGEN). RNA integrity number (RIN) was assessed on a Bioanalyzer (Agilent Technologies). RNA-Seq libraries were prepared from 500ng total RNA using the KAPA Stranded RNA-Seq Kit with RiboErase (KAPA Biosystems) for rRNA depletion and Illumina-compatible indexes (NEXTflex RNA-Seq Barcodes, NOVA-512915, PerkinElmer, and IDT for Illumina TruSeq UD Indexes, 20022370). Pooled libraries (average insert size: 375 bp) passing the quality criteria were sequenced either on an Illumina HiSeq 2500 (125 bp paired end) or an Illumina NovaSeq (100 bp paired-end). The samples had a median sequencing depth of 42 million read pairs, with a range between 16 and 167 million read pairs.</p> <p><strong>Data processing</strong></p> <p>Samples were uniformly processed using RAPiD-nf, an efficient RNA-Seq processing pipeline implemented in the NextFlow framework. Following adapter trimming with Trimmomatic (version 0.36), all samples were aligned to the hg38 build (GRCh38.primary_assembly) of the human reference genome using STAR (2.7.2a), with indexes created from GENCODE, version 30. Gene expression was quantified using RSEM (1.3.1) using GENCODE v30. Quality control was performed using SAMtools and Picard, and the results were collated using MultiQC. Various technical metrics for sequencing quality control are provided in the metadata. Estimated read counts and normalised transcripts per million (TPM) matrices provided for each tissue.</p> <p><strong>Provided data:</strong></p> <p><em>gencode.v30.gene_meta.tsv.gz</em> - tab separated table with columns "genename", the HGNC gene symbol, and "geneid" the Ensembl ID, as set in the GENCODE v30 comprehensive annotation.</p> <p>For {tissue} in Cervical_Spinal_Cord, Thoracic_Spinal_Cord, Lumbar_Spinal_Cord:</p> <p><em>{tissue}_metadata.tsv.gz </em>- metadata describing each sample. Each row describes a sample. Descriptions of each column below.</p> <p><em>{tissue}_gene_tpm.tsv.gz</em> - the normalised TPM values from RSEM for all 58,884 genes in GENCODE v30. Each row describes a gene and each column describes a sample.</p> <p><em>{tissue}_gene_counts.tsv.gz</em> - the estimated read counts from RSEM for all 58,884 genes in GENCODE v30. Each row describes a gene and each column describes a sample.</p> <p><strong>Metadata Column Description</strong></p> <p><em>rna_id</em> - de-identified sample ID for each unique RNA-seq sample</p> <p><em>dna_id</em> - de-identified donor ID for each patient enrolled in the study</p> <p><em>site_id</em> - de-identified site name for each contributing site</p> <p><em>tissue</em> - name of tissue/region</p> <p><em>age_rounded</em> - age at death, rounded to nearest decade</p> <p><em>sex</em> - biological sex of donor</p> <p><em>subject_group</em> - long form disease group</p> <p><em>disease</em> - short form disease group</p> <p><em>site_of_motor_onset </em>- for ALS donors, where did symptoms start?</p> <p><em>disease_duration</em> - for ALS donors, how long did donor live with disease? </p> <p><em>mutations </em>- any known ALS gene mutations</p> <p><em>library_prep</em> - type of library preparation method used</p> <p><em>seq_platform </em>- sequencing platform used for sequencing</p> <p><em>rin</em> - RNA integrity number, 0-10</p> <p><em>c9orf72_repeat_size</em> - estimated C9orf72 repeat expansion size</p> <p><em>gPC1 - gPC5 </em>- principal component of genetic ancestry from whole genome sequencing</p> <p>Remaining metadata columns are from Picard - see here: <a href="http://broadinstitute.github.io/picard/picard-metric-definitions.html#RnaSeqMetrics">http://broadinstitute.github.io/picard/picard-metric-definitions.html#RnaSeqMetrics</a> </p> <p> </p>
Raw RNAseq count data for figure 4 - by Van de Beek et al., 2022
<p>This CSV file is generated by Van de Beek et al., (2022) and is part of "<strong><em>PRDM10</em></strong><strong> directs <em>FLCN</em> expression in a novel disorder overlapping with Birt-Hogg-Dubé syndrome and familial lipomatosis</strong>" to be published in Human Molecular Genetics.</p>
Bird counts at Buanyuantula, Makalondi, Niger (1991-1993)
<p>Bird counts along transects carried out by <a href="https://www.wikiwand.com/de/Pierre_Souvairan_(Missionar)">Pierre Souvairan (1921-1998)</a> from June 1991 to September 1993 at Buanyuantula (spelled <a href="https://www.openstreetmap.org/#map=16/12.8087/1.7377">Boignontoula in OpenStreetMap</a>), Makalondi, Niger, near the <a href="https://www.openstreetmap.org/#map=15/12.8029/1.7393">Kulbu pools</a>. Patrick Giraudoux helped Pierre Souvairan to design the protocol during his stays in Dijon, France. Patrick Giraudoux (in December 1991) and Dominique Michelat (in July-August 1995) visited Pierre Souvairan once on the spot at Buanyuantula, then digitized and secured the data.</p> <p>Two transect strips were walked, corresponding to grids of 100 x 100 m mesh. Each transect was walked along a line crossing the grid, observing birds at maximum 50 m on each side of the transect line.</p> <p>- The NS strip corresponds to a 2.5 km x 200 m strip. The grid consists in two contiguous sub-strips (one north, one south of 100 x 100 m mesh each) stretching E - W between Buanyuantula and the Kulbu pools depression. Three 2.5 km transects lines were walked: one "N" in the middle of the northern strip, one "M", median, at the common border of the two strips, one "S" in the middle of the southern strip.</p> <p>- Transects B, for "bas-fond" (wetland), has two discrete segments 01B-10B and 16B-25B, and was walked at the higher water limit of the Kulbu pools area.</p> <table align="center"> <caption> <p>Transect dates</p> </caption> <thead> <tr> <th scope="col">Transect</th> <th scope="col">Beginning</th> <th scope="col">End</th> <th scope="col"> <p>Fortnight number</p> </th> </tr> </thead> <tbody> <tr> <td>N</td> <td>June 1991, fortnight #1</td> <td>May 1992, fortnight #2</td> <td>24</td> </tr> <tr> <td>S</td> <td>June 1991, fortnight #1</td> <td>May 1992, fortnight #2</td> <td>24</td> </tr> <tr> <td>M</td> <td>September 1992, fortnight #1</td> <td>August 1993, fortnight #2</td> <td>24</td> </tr> <tr> <td>01B-10B</td> <td>October 1992, fortnight #1</td> <td>September 1993, fortnight #2</td> <td>24</td> </tr> <tr> <td>16B-25B</td> <td>September 1992, fortnight #1</td> <td>August 1993, fortnight #2</td> <td>24</td> </tr> </tbody> </table> <p><strong>Caveat:</strong> the <strong>approximate</strong> place of those transects has been georeferenced in July 2022 by Patrick Giraudoux and Dominique Michelat based on Google Earth and Bing satellite backgrounds imported in QuantumGIS 3.24.2-Tisler, and on a hand-made document (<a href="https://zenodo.org/record/6905506/files/Itine%CC%81raire%20lettre%2018101992.jpeg?download=1">Itinéraire lettre 18101992.jpeg)</a> Pierre Souvairan prepared and sent in October 1992. On the satellite background, Pierre Souvairan's compound (cabin and field), some tracks (e.g. Makalondi trail), the Buanyuantula village were still clearly visible as well as the general geomorphological features of the area. However, irrevocable differences still remain between georeferenced files and Pierre Souvairan's paper maps. We have prioritized the consistency with undoubtful features such as well identified tracks, acknowledging that after more than 30 years when the hand made maps have been drawn, vegetation features, wetland border, etc. might have sensibly changed.</p> <p>Pierre Souvairan was an excellent ornithologist, but had one bad eye (half blind). Assuming a constant bias due to this, comparability between observations is fine in term of relative abundance. This issue must however be considered, should comparisons with other observers be done.</p> <p><strong>FILE DESCRIPTION:</strong></p> <p><strong>Main files</strong></p> <p>Files <a href="https://zenodo.org/record/6905506/files/carres_date.txt?download=1">carres_date.txt </a>, <a href="https://zenodo.org/record/6905506/files/especes.txt?download=1">especes.txt </a>, <a href="https://zenodo.org/record/6905506/files/observations.txt?download=1">observations.txt </a>are tables of a relational database. They can be linked using the index field 'carre' between <a href="https://zenodo.org/record/6905506/files/carres_date.txt?download=1">carres_date.txt</a> and <a href="https://zenodo.org/record/6905506/files/observations.txt?download=1">observations.txt</a>, and the index field 'espece' between <a href="https://zenodo.org/record/6905506/files/observations.txt?download=1">observations.txt </a>and <a href="https://zenodo.org/api/files/7f6752aa-6261-4287-a7d3-db865b022f89/especes.txt?versionId=46b71a45-2815-4e8e-bhttps://zenodo.org/record/6905506/files/especes.txt?download=1689-44cc2999ae99">especes.txt</a>.</p> <p><a href="https://zenodo.org/record/6905506/files/carres_date.txt?download=1">carres_date.txt </a></p> <ul> <li>carre, grid square ID : digits 1-2, the grid square ID; digit 3, transect ID (N, north; S, south; M, Median; B, <em>Bas-fond</em>); digit 4-5, the fortnight number from the first survey fortnight to the last one (1 - 24). Examples: 01N01 is square 01 of transect N, first fortnight; 01N10 is square 01 of transect N, tenth fortnight.</li> <li>annee, year (91-93 for 1991-1993)</li> <li>duree, crossing duration (in hour fraction: e.g. 5 mn = 5/60 = 0.08). The speed at which each grid square was crossed (depending on vegetation density, number of observations, etc.)</li> <li>mois, month (1-12)</li> <li>date, the date of the crossing (computed for convenience from the year, month and fortnight: day 10 was given arbitrarily for the first fortnight, and day 20 for the second fortnight of the month).</li> </ul> <p><a href="https://zenodo.org/record/6905506/files/especes.txt?download=1">especes.txt </a></p> <ul> <li>espece, ID number</li> <li>nom, Latin name</li> <li>code, 4 digits abbreviation</li> </ul> <p><a href="https://zenodo.org/record/6905506/files/observations.txt?download=1">observations.txt </a></p> <ul> <li>carre, carre ID (for joining see the field ‘carre’ field in the file<a href="https://zenodo.org/record/6905506/files/carres_date.txt?download=1"> carres_date.txt</a>)</li> <li>espece, species ID number (for joining see the field ‘espece’ in the file <a href="https://zenodo.org/record/6905506/files/especes.txt?download=1">especes.txt</a>)</li> <li>nombre, number of individuals observed</li> </ul> <p><strong>Supplementary files</strong></p> <p><a href="https://zenodo.org/record/6905506/files/Carte Makalondi.jpeg?download=1">Carte Makalondi.jpeg </a>the scan of a map Pierre Souvairan drew with the location of the places mentioned in his correspondence.</p> <p><a href="https://zenodo.org/record/6905506/files/Geometries_Grid_NS_B_transectLines.zip?download=1">Geometries_Grid_NS_B_transectLines.zip </a>the grid and transect geometries (ESRI and kml).</p> <p><a href="https://zenodo.org/record/6905506/files/Itine%CC%81raire%20lettre%2018101992.jpeg?download=1">Itinéraire lettre 18101992.jpeg </a>the scan of a map Pierre Souvairan drew and sent to Patrick Giraudoux (letter received in October 1992). Georeferenced locations are available in <a href="https://zenodo.org/record/6905506/files/Name_coordinates.kml?download=1">Name_coordinates.kml</a>.</p> <p><a href="https://zenodo.org/record/6905506/files/Name_coordinates.kml?download=1">Name_coordinates.kml </a>the georeferenced locations of places mentioned in the field map <a href="https://zenodo.org/record/6905506/files/Carte Makalondi.jpeg?download=1">Carte Makalondi.jpeg</a>.</p> <p><a href="https://zenodo.org/record/6905506/files/PhotosNumbered.pdf?download=1">PhotosNumbered.pdf</a> photos taken on the spot in December 1991 by Patrick Giraudoux. The place where each photo was taken and the direction is in red in the transect map <a href="https://zenodo.org/record/6905506/files/ScanTransectPhotos.jpg?download=1">ScanTransectPhotos.jpg</a>.</p> <p><a href="https://zenodo.org/record/6905506/files/ScanTransectPhotos.jpg?download=1">ScanTransectPhotos.jpg</a> 20 x 140.4 cm field map of the NS strip with vegetation data, drawn by Pierre Souvairan likely in 1991.</p> <p><a href="https://zenodo.org/record/6905506/files/Transects_Grids_Map.jpg?download=1">Transects_Grids_Map.jpg</a> Map of the area, with the exact location of pools, Buanyuantula village, Pierre Souvairan's cabin, and the general location of the grids and transects. Note that the latter two have been <strong>approximately</strong> located based on Pierre Souvairan's field maps (see caveat above). Background Bing satellite accessed 31/07/2022.</p>
Joint AstraZeneca-Cancer Research Horizons Functional Genomics Centre's CRISPRn library benchmark screens: gRNA counts and associated metadata
<p>Genome-wide CRISPR sgRNA libraries have emerged as transformative tools to systematically probe gene function. While these libraries have been iterated over time to be more efficient, their large size limits their use in some applications. Here, we benchmarked publicly available genome-wide single-targeting sgRNA libraries and evaluated dual targeting as a strategy for pooled CRISPR loss-of-function screens. We leveraged this data to design two minimal genome-wide human CRISPR-Cas9 libraries that are 50% smaller than other libraries and that preserve specificity and sensitivity, thus enabling broader deployment at scale. </p>
Gene-level counts according to their poly(A) length and additional uridine modifications in several stages of zebrafish, Xenopus, and mouse embryos
<p>This HDF5 file contains the processed data of primary poly(A) tail length analyses for the TAIL-seq runs used for Chang and Yeo et al. (2018; doi:10.1016/j.molcel.2018.03.004). The read count tables are stored under the two-level group structure of the run identifier as the first level and the sample identifier as the second level. A dataset at a leaf node is an unsigned integer array of the read count numbers by the length of poly(A) in rows and the length of U tails following after poly(A) in columns.</p> <p>Please refer to the <a href="https://data.mendeley.com/datasets/tzc5wwczyg/1">supplementary data page</a> of the original paper for more information about the experimental design.</p> <p> </p>
Fremont Bridge Hourly Bicycle Counts by Month October 2012 to present
<p>The Fremont Bridge Bicycle Counter records the number of bikes that cross the bridge using the pedestrian/bicycle pathways. Inductive loops on the east and west pathways count the passing of bicycles regardless of travel direction. The data consists of a date/time field: Date, east pathway count field: Fremont Bridge NB, and west pathway count field: Fremont Bridge SB. The count fields represent the total bicycles detected during the specified one hour period. Direction of travel is not specified, but in general most traffic in the Fremont Bridge NB field is travelling northbound and most traffic in the Fremont Bridge SB field is travelling southbound.</p>
Keyword counts from US Presidential State of the Union Addresses and Presidential Budget Messages
<p>Keyword counts from US Presidential State of the Union Addresses and Presidential Budget Messages. This was done using the Python scripts provided under <a href="https://github.com/JeremySilver/KeywordCountsPresidentialMessages">https://github.com/JeremySilver/KeywordCountsPresidentialMessages</a>. The raw text data is from <a href="http://www.presidency.ucsb.edu/">The American Presidency Project</a> (<a href="http://www.ucsb.edu/">UCSB</a>), with some Presidential Budget Messages being extracted from US Federal Budget documents available through <a href="https://fraser.stlouisfed.org/">FRASER</a> (a digital library of U.S. economic, financial, and banking history) or, for the more recent documents the website of the <a href="https://www.whitehouse.gov/">White House</a>.</p> <p>The data headings are:</p> <ul> <li>pid: in most cases, this is the index for the text document as archived on <a href="http://www.presidency.ucsb.edu/">The American Presidency Project</a> website. In some cases, this was the filename of a plain-text file read directly.</li> <li>year: Year that the message was delivered.</li> <li>date: Date that the message was delivered.</li> <li>name: Name of the US President delivering the message.</li> <li>count_of_all_words: Count of all words in the document.</li> <li>count_of_keywords: Count of all keywords encountered in that document.</li> <li>Keyword specific columns - three per keyword. For example, for the 'energy' keyword, the 'energy' column gives the number of times the 'energy' keyword was counted in the message, 'energy_pct_of_keywords' gives this count as a percentage of all keywords, and 'energy_pct_of_all_words' gives this count as a percentage of all words</li> </ul> <p>Below is the list of keywords that match when the search is applied to a dictionary file containing over 99,000 US English words.</p> <ul> <li>energy: 'energy'</li> <li>tax: 'nontaxable', 'overtax', 'overtaxed', 'overtaxes', 'overtaxing', 'surtax', 'surtaxed', 'surtaxes', 'surtaxing', 'surtaxs', 'tax', 'taxable', 'taxation', 'taxations', 'taxed', 'taxes', 'taxing', 'taxpayer', 'taxpayers', 'taxs'</li> <li>defense: 'defend', 'defense'</li> <li>education: 'education'</li> <li>employment: 'employ', 'employable', 'employe', 'employed', 'employee', 'employees', 'employer', 'employers', 'employes', 'employing', 'employment', 'employments', 'employs', 'underemployed', 'unemployable', 'unemployed', 'unemployeds', 'unemployment', 'unemployments'</li> <li>research: 'research', 'researched', 'researcher', 'researchers', 'researches', 'researching', 'researchs'</li> <li>shooting: 'shooting'</li> <li>space: 'space'</li> <li>nuclear: 'nuclear'</li> <li>natural resources: 'natural resources'</li> <li>racism: 'racism', 'civil rights'</li> <li>crime: 'crime', 'crimes', 'criminal', 'criminally', 'criminals', 'decriminalization', 'decriminalizations', 'decriminalize', 'decriminalized', 'decriminalizes', 'decriminalizing'</li> <li>environment: 'environment', 'environmental', 'environmentalism', 'environmentalisms', 'environmentalist', 'environmentalists', 'environmentally', 'environments'</li> <li>religion: 'faith', 'god', 'prayer', 'religion'</li> <li>health: 'health', 'healthful', 'healthfully', 'healthfulness', 'healthfulnesss', 'healthier', 'healthiest', 'healthily', 'healthiness', 'healthinesss', 'healths', 'healthy', 'unhealthful', 'unhealthier', 'unhealthiest', 'unhealthy'</li> <li>terror: 'terror', 'terrorism', 'terrorisms', 'terrorist', 'terrorists', 'terrorize', 'terrorized', 'terrorizes', 'terrorizing', 'terrors'</li> <li>war: 'war', 'warrior', 'warriors', 'wars'</li> <li>economy: 'economic', 'economical', 'economically', 'economics', 'economicss', 'economy', 'economys', 'microeconomics', 'microeconomicss', 'socioeconomic', 'uneconomic', 'uneconomical'</li> <li>jobs: 'jobs'</li> <li>business: 'agribusiness', 'agribusinesses', 'agribusinesss', 'business', 'businesses', 'businesslike', 'businessman', 'businessmans', 'businessmen', 'businesss', 'businesswoman', 'businesswomans', 'businesswomen'</li> <li>drugs: 'drugs', 'narcotics'</li> <li>inflation: 'inflation'</li> <li>climate: 'climate'</li> <li>science: 'science', 'sciences', 'scientific', 'scientifically', 'scientist', 'scientists'</li> <li>gun: 'gun', 'gunfire', 'gunman', 'guns', 'handgun', 'rifle', 'shotgun'</li> <li>tech: 'biotechnology', 'biotechnologys', 'technical', 'technological', 'technologically', 'technologies', 'technologist', 'technologists', 'technology', 'technologys'</li> <li>military: 'military'</li> <li>security: 'security'</li> <li>housing: 'housing'</li> <li>pollution: 'pollution'</li> </ul> <p>The dictionary file used is a standard file among Linux systems, and the version used was provided with version 7.1-1 of the Ubuntu 'wamerican' package. Two extra phrases, which do not appear in the dictionary file, are added to the list: 'civil rights' (under the 'racism' keyword) and 'natural resources' (under the 'natural resources' theme).</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.