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18,921 results for “Chip”
Dataset for "Performance of a Gridpix detector based on the Timepix3 chip"
<p>This is the data of the analysed run described in the paper "Performance of a Gridpix detector based on the Timepix3 chip" (to be submitted).</p> <p>The data can be converted and analysed with the scripts found at https://github.com/KeesLigtenberg/GridpixTrackFitter</p> <ul> <li>Raw data file from telescope in "mimosa_telescope_nikhef_m26_telescope_scan.h5"</li> <li>Raw data from Timepix3 converted to root tree in "W0015_H04-170713-071936-347.root"</li> <li>Converted telescope data in "mimosa_telescope_nikhef_m26_telescope_scan_combined.root" (combined because all planes are combined)</li> <li>Converted Timepix3 data file "W0015_H04-170713-071936-347_converted.root"</li> <li>"Alignment.dat" and "ToTCorrection.dat" are alignment files</li> <li>"example_converted.root" is a smaller file that can be used to get started with the data</li> </ul>
Bamfiles ChIP-seq
<p>Bamfiles resulting from mapping reads deposited in GEO (Accession GSE121283) to the genome of <em>Fusarium oxysporum</em> f. sp. <em>lycopersici </em>4287 (Fol4287).</p> <p>Adapter sequences were removed and quality scores were converted to Sanger format with the MAQ sol2sanger command if needed. Quality was checked manually using FastQC.</p> <p>Reads were aligned to the genome of Fol4287 using `bwa aln` (bwa version 0.7.12)<br> Duplicate reads were removed with `Picard tools` (version 1.134) (<a href="http://broadinstitute.github.io/picard,">http://broadinstitute.github.io/picard,</a> MarkDuplicates)</p> <p>See <a href="https://doi.org/10.1016/j.fgb.2015.03.006">10.1016/j.fgb.2015.03.006</a> for details on ChIP-seq experiment protocols. This dataset is described in 10.1101/465070. </p>
Bigwig files of ChIP and RNA sequencing experiments in Theileria annulata
<p>This repository provides bigwig files for ChIP and RNA sequencing experiments in Theileria annulata. DeepTools generated Coverage files, ReadCount normalised files and SES normalised files are provided. Also provided are genome and annotation files for visualisation with IGV software.</p>
Sub-sampled Fastq Files for ChIP-seq datasets from Click-Seq Science Paper (Science 2017, 10.1126/science.aal2066)
<p>Data were downloaded from SRA. We then randomly sub-sampled 20% of the reads using seqtk_sample v 1.2 in galaxy (seed 4).</p> <p>This dataset is a support dataset for the <a href="https://www.embl.de/training/events/2019/EPI19-01/index.html">EMBL Course: Chromatin Signatures During Differentiation: Integrated Omics</a></p>
Ultrafast photoresponse of vertically oriented TMD films probed in a vertical electrode configuration on Si chips
<p>This dataset contains the measurement data for figures published in the journal article: </p> <p> Ultrafast photoresponse of vertically oriented TMD films probed in a vertical electrode configuration on Si chips (https://doi.org/10.1039/D2NA00313A)</p> <p>by Topias Järvinen, Seyed-Hossein Hosseini Shokouh, Sami Sainio, Olli Pitkänen and Krisztian Kordas</p>
Figure 5 in CHIPS: a database of historic fish distribution in the Seine River basin (France)
Figure 5. - Species abundance for three rivers: Créanton River (top), Armançon River (middle), Ouanne River (bottom); in grey: historical data, in black: actual data. CONCLUSION AND PERSPECTIVE
Figure 4 in CHIPS: a database of historic fish distribution in the Seine River basin (France)
Figure 4. - Distribution changes of Salmo salar in the Seine basin during the last two centuries. Main migratory axis; Location of active spawning grounds; Sporadic salmon observations; Stocking attemps.
Figure 2 in CHIPS: a database of historic fish distribution in the Seine River basin (France)
Figure 2. - Main species in the database (% of observations). Black bars refer to taxa identification higher than species level.
Data accompanying "HSP70 inhibits CHIP E3 ligase activity to maintain germline function in Caenorhabditis elegans" article.
<p>This work was funded by the National Science Centre, Poland (grant PRELUDIUM number 2021/41/N/NZ1/03086) (to P.T.) and by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) under Germany’s Excellence Strategy – EXC 2030 – 390661388 and – FOR 5504 – project number 496650118 (to T.H.). M.T.P. received support by the Cologne Graduate School of Aging Research. N.A.S., A.S., K.J., and M.N. were supported by the International Institute of Molecular and Cell Biology in Warsaw.</p>
On-chip phonon-enhanced IR near-field detection of molecular vibrations
<p>This dataset contains the source data and raw interferograms associated with our manuscript, <em>'On-chip phonon-enhanced IR near-field detection of molecular vibrations</em>' by A. Bylinkin et al. The source data, provided in an .xlsx file, includes the datasets used to generate the figures in both the main text and the Supplementary Information. The raw interferogram dataset, located in the <em>'Interferograms</em>' folder, was used to calculate the experimental spectra shown in Fig. 3b, f, and Suppl. Fig. 13. This dataset was acquired using a NeaSNOM microscope (attocube AG).</p> <p>The data processing procedure for calculating spectra from the interferograms is described in the Methods section of our manuscript.</p>
Linked collectors and determiners for: Sistematización de la colección entomológica y actualización de la colección del herbario CHIP del Instituto de Historia Natural y Ecología (IHNE), Chiapas (Plantas).
Natural history specimen data linked to collectors and determiners held within, "Sistematización de la colección entomológica y actualización de la colección del herbario CHIP del Instituto de Historia Natural y Ecología (IHNE), Chiapas (Plantas)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1f6704e8-b455-4f6b-916c-0cd727293fca">https://bionomia.net/dataset/1f6704e8-b455-4f6b-916c-0cd727293fca</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1f6704e8-b455-4f6b-916c-0cd727293fca">https://gbif.org/dataset/1f6704e8-b455-4f6b-916c-0cd727293fca</a>. Formatted as a Frictionless Data package.
Modelling Cyclic Stretch of Patient-Derived Alveolar Epithelial Cells from Organoids using a New Alveoli-on-Chip Platform
<p><span>The data originates from lung tumor samples obtained at the University Hospital Bern. Patient-derived alveolar epithelial type 2 cells (AEC2) were isolated and cultured into organoids. Grown organoids where dissociated and seeded onto a novel alveoli-on-chip system submerged in medium. After 24 h of either no mechanical stimulus (control) or cyclic breathing stretch (stretch), RNA was isolated and sequenced. The RNA-seq data underwent quality control, alignment to the reference genome, read counting, DGE and GSEA. All analyses were run in R version 4.2.1.</span></p>
Dataset Related to "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller"
<p>Dataset (and programs used to create it) for the publication "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller":</p> <p>Hannes Tröpgen, Mario Bielert, and Thomas Ilsche. 2023. Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller. In Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (ICPE ’23), April 15–19, 2023, Coimbra, Portugal. ACM, New York, NY, USA, 10 pages. <a href="https://doi.org/10.1145/3578244.3583729">https://doi.org/10.1145/3578244.3583729</a></p> <p>Find additional descriptions of the data in the included readme files.</p> <p> </p> <p>This work is supported in part by the German National High Performance Computing (NHR@TUD).<br> The authors are grateful to the Center for Information Services and High Performance Computing at TU Dresden for providing the Power9 Systems used in the measurements and the support during them.</p>
LFY ChIP-SEQ analysis Galaxy Training Material
<p>Datasets for Galaxy Training on ChIP-SEQ analysis. Raw files can be downloaded from SRA project SRP051214</p>
Sentinel2 RGB chips over Colombia (NE) with ESA World Cover for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong></p> <p>We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a></p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a><br> <br> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a> </p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
Sentinel2 RGB chips over BENELUX with ESA World Cover for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</strong></p> <p>We use the communes administrative division which is standardized across Europe by EUROSTAT at:<br> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a><br> This is roughly equivalent to the notion municipalities in most countries.</p> <p>From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp.</p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a><br> <br> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a></p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
Sentinel2 RGB chips over Colombia (NE) with JRC GHSL Population Density 2015 for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong></p> <p>We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a></p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: Global Human Settlement Layers, Population Grid 2015</strong></p> <p> labels range from 0 to 31, with the following meaning:<br> label value original value in GEE dataset<br> 0 0<br> 1 1-10<br> 2 11-20<br> 3 21-30<br> ...<br> 31 >=291 </p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a></p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre> <p> </p>
Sentinel2 RGB chips over BENELUX with JRC GHSL Population Density 2015 for Learning with Label Proportions
<p>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</p> <p>We use the communes adminitrative division which is standardized across Europe by EUROSTAT at:<br> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a><br> This is roughly equivalent to the notion municipalities in most countries.</p> <p>From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp.</p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds acoording to QA60 band following the example<br> given in GEE dataset info page at:<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: Global Human Settlement Layers, Population Grid 2015</strong></p> <p> labels range from 0 to 31, with the following meaning:<br> label value original value in GEE dataset<br> 0 0<br> 1 1-10<br> 2 11-20<br> 3 21-30<br> ...<br> 31 >=291 </p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a><br> </p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
MLL3 ChIP sequencing in murine and human HCC cells
<p>Mutations in genes encoding components of chromatin modifying and remodeling complexes are among the most frequently observed somatic events in human cancers. For example, missense and nonsense mutations targeting the mixed lineage leukemia family member 3 (<em>MLL3</em>/<em>KMT2C</em>) histone methyltransferase occur in a range of solid tumors and heterozygous deletions encompassing <em>MLL3</em> occur in a subset of aggressive leukemias. Although <em>MLL3</em> loss can promote tumorigenesis in mice, the molecular targets and biological processes by which MLL3 suppresses tumorigenesis remain poorly characterized. Here we combined genetic, epigenomic, and animal modeling approaches to demonstrate that one of the mechanisms by which MLL3 links chromatin remodeling to tumor suppression is by co-activating the <em>Cdkn2a</em> tumor suppressor locus. Disruption of <em>Mll3</em> cooperates with <em>Myc</em> overexpression in the development of murine hepatocellular carcinoma (HCC), in which MLL3 binding to the <em>Cdkn2a</em> locus is blunted, resulting in reduced H3K4 methylation and low expression levels of the locus-encoded genes, <em>Ink4a</em> and <em>Arf</em>. Conversely, elevated <em>MLL3</em> expression increases its binding to the CDKN2A locus and co-activates gene transcription. Endogenous Mll3 restoration reverses these chromatin and transcriptional effects and triggers <em>Ink4a</em>/<em>Arf</em>-dependent apoptosis. Underscoring the human relevance of this epistasis, we found that genomic alterations in <em>MLL3</em> and <em>CDKN2A</em> display mutual exclusivity in human HCC samples. These results collectively point to a new mechanism for disrupting CDKN2A activity during cancer development and, in doing so, link MLL3 to an established tumor suppressor network.</p>
Microdialysis on-chip crystallization of HEWL and Thaumatin and in situ X-ray diffraction studies
<p>This deposition includes the mtz and pdb files for HEWL and Thaumatin crystal structures included in the article "Microdialysis on-chip crystallization of soluble and membrane proteins with the MicroCrys platform and in situ X-ray diffraction case studies". </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.