cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25387/g3.12061656
title: Supplemental Material for Cary, Podshivalova, and Kenyon, 2020

description: <b>File S1. Partitioned S matrix (genes that belong to each of the 209 DEXICA modules). </b>The two hemi-modules are designated by opposite signs: 0 indicates that a gene does not belong to a given module; 1 indicates that a gene belongs to the “a” hemi-module of a given module; -1 indicates that a gene belongs to the “b” hemi-module of a given module. <b>File S2. Module annotation summaries</b>. For each module, the perturbation (from the 716 perturbations in the microarray compendium) in which that module explains the greatest amount of variance (i.e. the perturbation that activates that module the most strongly) is indicated. “None” indicates that a module does not explain more than 5% of the variance in any perturbation (SVE is between -0.05 and 0.05). The top two GO terms from each GO category that are enriched in each hemi-module are also indicated. In cases where enriched GO terms and the perturbation show clear semantic agreement, this is indicated in the first column “Module likely represents”. <b>File S3. Finding experiments that activate similar modules. </b>Module activity in all possible pairs of perturbations (contrasts) in the microarray compendium was calculated. The pairs are ranked by the absolute value of the Pearson correlation coefficient calculated based on activity of each module (SVE).<b> </b><b>File S4</b>. <b>Module-weighted GO terms</b>. Weighted associations between each gene and each GO term are shown. Numbers represent z-scores of gene weights in each GO category. GO categories used for this analysis contain as least 15 genes.<br><br><b>Supplemental information</b> file contains supplemental discussion, supplemental methods, 8 supplemental figures and supplemental references<br><br>
DOI: 10.25387/g3.12061656.v2
title: Supplemental Material for Cary, Podshivalova, and Kenyon, 2020

description: <b>File S1. Partitioned S matrix (genes that belong to each of the 209 DEXICA modules). </b>The two hemi-modules are designated by opposite signs: 0 indicates that a gene does not belong to a given module; 1 indicates that a gene belongs to the “a” hemi-module of a given module; -1 indicates that a gene belongs to the “b” hemi-module of a given module. <b>File S2. Module annotation summaries</b>. For each module, the perturbation (from the 716 perturbations in the microarray compendium) in which that module explains the greatest amount of variance (i.e. the perturbation that activates that module the most strongly) is indicated. “None” indicates that a module does not explain more than 5% of the variance in any perturbation (SVE is between -0.05 and 0.05). The top two GO terms from each GO category that are enriched in each hemi-module are also indicated. In cases where enriched GO terms and the perturbation show clear semantic agreement, this is indicated in the first column “Module likely represents”. <b>File S3. Finding experiments that activate similar modules. </b>Module activity in all possible pairs of perturbations (contrasts) in the microarray compendium was calculated. The pairs are ranked by the absolute value of the Pearson correlation coefficient calculated based on activity of each module (SVE).<b> </b><b>File S4</b>. <b>Module-weighted GO terms</b>. Weighted associations between each gene and each GO term are shown. Numbers represent z-scores of gene weights in each GO category. GO categories used for this analysis contain as least 15 genes.<br><br><b>Supplemental information</b> file contains supplemental discussion, supplemental methods, 8 supplemental figures and supplemental references<br><br>
DOI: 10.25387/g3.12061656.v1
title: Supplemental Material for Cary, Podshivalova, and Kenyon, 2020

description: <b>File S1. Partitioned S matrix (genes that belong to each of the 209 DEXICA modules). </b>The two hemi-modules are designated by opposite signs: 0 indicates that a gene does not belong to a given module; 1 indicates that a gene belongs to the “a” hemi-module of a given module; -1 indicates that a gene belongs to the “b” hemi-module of a given module. <b>File S2. Module annotation summaries</b>. For each module, the perturbation (from the 716 perturbations in the microarray compendium) in which that module explains the greatest amount of variance (i.e. the perturbation that activates that module the most strongly) is indicated. “None” indicates that a module does not explain more than 5% of the variance in any perturbation (SVE is between -0.05 and 0.05). The top two GO terms from each GO category that are enriched in each hemi-module are also indicated. In cases where enriched GO terms and the perturbation show clear semantic agreement, this is indicated in the first column “Module likely represents”. <b>File S3. Finding experiments that activate similar modules. </b>Module activity in all possible pairs of perturbations (contrasts) in the microarray compendium was calculated. The pairs are ranked by the absolute value of the Pearson correlation coefficient calculated based on activity of each module (SVE).<b> </b><b>File S4</b>. <b>Module-weighted GO terms</b>. Weighted associations between each gene and each GO term are shown. Numbers represent z-scores of gene weights in each GO category. GO categories used for this analysis contain as least 15 genes.
DOI: 10.5281/zenodo.3974521
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 28

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3974520
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 28

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3974523
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 29

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3974522
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 29

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3974525
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 30

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3974524
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 30

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3974528
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 31

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3974527
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 31

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3974532
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 24

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974531
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 24

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974535
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 25

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974534
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 25

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974540
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 26

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974539
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 26

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974542
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 29

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974541
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 29

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974545
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 30

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974544
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 30

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974548
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 31

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3974547
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 31

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3604284
title: Decision making in slow and rapid reaching: Sacrificing success to minimize effort

description: Datasets associated with the following publication: Hesse, C., Kangur, K., & Hunt, A. (2020). Decision making in slow and rapid reaching: Sacrificing success to minimize effort. Cognition The folder contains data files for each of the Experiments as presented in the article. There is a separate description file providing further relevant information for the data files uploaded for each experiment. For further questions, please contact:<br> c.hesse[at]abdn.ac.uk
DOI: 10.5281/zenodo.3604283
title: Decision making in slow and rapid reaching: Sacrificing success to minimize effort

description: Datasets associated with the following publication: Hesse, C., Kangur, K., & Hunt, A. (2020). Decision making in slow and rapid reaching: Sacrificing success to minimize effort. Cognition The folder contains data files for each of the Experiments as presented in the article. There is a separate description file providing further relevant information for the data files uploaded for each experiment. For further questions, please contact:<br> c.hesse[at]abdn.ac.uk
DOI: 10.5258/soton/d1493
title: Data for Broadband Millimeter Wave Textile-Based Flexible Rectenna

description: Dataset supporting the paper "Broadband Millimeter-Wave Textile-Based Flexible Rectenna for Wearable Energy Harvesting" published in the IEEE Transactions on Microwave Theory and Techniques. Abstract: "Millimeter-Wave (mmWave) bands, a key part of future 5G networks, represent a potential channel for RF energy harvesting, where the high-gain antenna arrays offer improved end-to-end efficiency compared to sub-6 GHz networks. This paper presents a broadband mmWave rectenna, the first rectenna realized on a flexible textile substrate for wearable applications. The proposed novel antenna’s bandwidth extends from 23 to 40 GHz, with a minimum radiation efficiency of 67% up to 30 GHz, over 3 dB improvement compared to a standard patch. A stable gain of more than 8 dB is achieved based on a textile reflector plane. The antenna is directly connected to a textile based microstrip voltage doubler rectifier utilizing commercial Schottky diodes. The rectifier is matched to the antenna using a tapered line feed for high-impedance matching, achieving broadband high voltage-sensitivity. The rectifier has a peak RF DC efficiency of 12% and a 9.5 dBm 1 V sensitivity from 23 to 24.25 GHz. The integrated rectenna is demonstrated with more than 1.3-V DC output from 12 dBm of mmWave wireless power across a 28% fractional bandwidth from 20 to 26.5 GHz, a 15% half-power fractional bandwidth, and a peak output of 6.5V from 20 dBm at 24 GHz."
DOI: 10.25625/qvwcnt/ncu339
title: Fig4e cell CSD red

description: SUM_vero5_556_250im_4p_30c_42d_1_MMStack_Pos0.ome.tif
DOI: 10.5281/zenodo.3742095
title: Chromosight benchmarks and processed data

description: Input data and scripts required to rerun chromosight's benchmarks and associated figures, as well as output results in text format. The record contains 2 tarballs corresponding to two different benchmarks. Each tarball contains the scripts, input and output data of its benchmark: <strong>Performance benchmark:</strong> This benchmark is contained in "20200406_benchmark_chromosight_performance.tar.gz" and compares chromosight running time and RAM use with 2 other softwares. This benchmark is run on a real high resolution human Hi-C matrix with different subsampling values. Benchmark scripts are expected to be run on a regular laptop or desktop. <strong>Results benchmark:</strong> This benchmark is contained in "20200406_benchmark_chromosight_results.tar.gz" and assess chromosight's ability to detect chromatin loop patterns on Hi-C contact maps. Chromosight is compared to 4 other softwares. For each software, precision, recall (=sensitivity) and F1 scores are measured using 2000 small synthetic Hi-C matrices with known loop coordinates. Each software is run with a range of 50-200 parameter combinations for all data. Scripts to run this benchmark are written to run as a job array on a SLURM computing cluster to reduce compute time. <strong>Processed data files</strong> The contact matrices in cool format, which are used to generate figures throughout the manuscript.
DOI: 10.25625/qvwcnt/zodrnf
title: Fig4e cell CSD green

description: SUM_vero5_473_250im_4p_30c_42d_2_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/pup2tc
title: Fig4f cell ISM green

description: ISM_vero5_473_250im_4p_30c_42d_2_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/kmuv35
title: Fig4ef cell raw metadata blue

description: vero5_405_250im_4p_30c_42d_2_MMStack_Pos0_metadata.txt
DOI: 10.25625/qvwcnt/dv6mwp
title: Fig4f cell ISM blue

description: ISM_vero5_405_250im_4p_30c_42d_2_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/gjxoxs
title: Fig4ef cell raw metadata green

description: vero5_473_250im_4p_30c_42d_2_MMStack_Pos0_metadata.txt
DOI: 10.25625/qvwcnt/xnlgan
title: Fig4f cell ISM red

description: ISM_vero5_556_250im_4p_30c_42d_1_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/qunnxf
title: Fig4ef cell raw metadata red

description: vero5_556_250im_4p_30c_42d_1_MMStack_Pos0_metadata.txt
DOI: 10.25625/qvwcnt/tdbo5b
title: Fig4ef cell raw green

description: vero5_473_250im_4p_30c_42d_2_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/17eku4
title: Fig4abef reference file

description: ref_473_250im_4p_2c_42d_1_MMStack_Pos0.ome.POS_XY2.txt
DOI: 10.25625/qvwcnt/elvfqn
title: Fig4e cell CSD blue

description: SUM_vero5_405_250im_4p_30c_42d_2_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/howvvw
title: Fig4ef cell raw blue

description: vero5_405_250im_4p_30c_42d_2_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/0e4pju
title: Fig4ef cell raw red

description: vero5_556_250im_4p_30c_42d_1_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/vrcv2l
title: Fig4c lines CSD

description: AVG_ArgoSIM_lines2_405_4p6_c200_delay25_im250_512FOV_1_MMStack_Pos0.ome_FFcorr.tif
DOI: 10.25625/qvwcnt/faywgg
title: Fig4cd lines raw data

description: ArgoSIM_lines2_405_4p6_c200_delay25_im250_512FOV_1_MMStack_Pos0_FF.ome.tif
DOI: 10.25625/qvwcnt/w5uhsr
title: Fig4d lines ISM

description: ISM_ArgoSIM_lines2_405_4p6_c200_delay25_im250_512FOV_1_MMStack_Pos0.ome_FFcorr.tif
DOI: 10.5281/zenodo.3974645
title: Characterizing alternative feeds for rainbow trout (O. mykiss) by 1H-NMR metabolomics

description: The CPMG NMR representative spectra of alternative feeds used in the manuscript draft entiteld: <strong>Characterizing alternative feeds for rainbow trout </strong><strong>(<em>O. mykiss</em>) by <sup>1</sup>H-NMR metabolomics</strong> Simon Roques<sup>1,2,3</sup>, Catherine Deborde<sup>3,4</sup>, Nadège Richard<sup>2</sup>, Luce Sergent<sup>5</sup>, Francis Kurz<sup>6</sup>, Sandrine Skiba-Cassy<sup>1</sup>, Benoît Fauconneau<sup>1</sup>, Annick Moing<sup>3,4</sup> <sup>1</sup> INRA, Univ Pau & Pays Adour, E2S UPPA, UMR 1419, Nutrition Métabolisme, Aquaculture, Saint Pée sur Nivelle, F-64310, France <sup>2</sup> Phileo Lesaffre Animal Care, 59700 Marcq-en-Baroeul, France <sup>3</sup> Bordeaux Metabolome Facility, MetaboHUB, CGFB, Centre INRA de Nouvelle Aquitaine Bordeaux, 33140 Villenave d’Ornon, France <sup>4</sup> INRA, Univ. Bordeaux, UMR 1332 Fruit Biology and Pathology, Centre INRA de Nouvelle Aquitaine Bordeaux, 33140 Villenave d’Ornon, France <sup>5</sup> Copalis Industrie, 62480 Le Portel, France <sup>6</sup> Algae Natural Food, 67400 Illkirch-Graffenstaden, France <em>Corresponding author: </em>simon.roques@inra.fr<em> </em>
DOI: 10.25625/qvwcnt/s0bapc
title: Fig4ab beads raw data

description: TSbeads_473_250im_4p_20c_42d_1_MMStack_Pos0.ome.tif
DOI: 10.5281/zenodo.1423123
title: Characterizing alternative feeds for rainbow trout (O. mykiss) by 1H-NMR metabolomics

description: The CPMG NMR representative spectra of alternative feeds used in the manuscript draft entiteld: <strong>Characterizing alternative feeds for rainbow trout </strong><strong>(<em>O. mykiss</em>) by <sup>1</sup>H-NMR metabolomics</strong> Simon Roques<sup>1,2,3</sup>, Catherine Deborde<sup>3,4</sup>, Nadège Richard<sup>2</sup>, Luce Sergent<sup>5</sup>, Francis Kurz<sup>6</sup>, Sandrine Skiba-Cassy<sup>1</sup>, Benoît Fauconneau<sup>1</sup>, Annick Moing<sup>3,4</sup> <sup>1</sup> INRA, Univ Pau & Pays Adour, E2S UPPA, UMR 1419, Nutrition Métabolisme, Aquaculture, Saint Pée sur Nivelle, F-64310, France <sup>2</sup> Phileo Lesaffre Animal Care, 59700 Marcq-en-Baroeul, France <sup>3</sup> Bordeaux Metabolome Facility, MetaboHUB, CGFB, Centre INRA de Nouvelle Aquitaine Bordeaux, 33140 Villenave d’Ornon, France <sup>4</sup> INRA, Univ. Bordeaux, UMR 1332 Fruit Biology and Pathology, Centre INRA de Nouvelle Aquitaine Bordeaux, 33140 Villenave d’Ornon, France <sup>5</sup> Copalis Industrie, 62480 Le Portel, France <sup>6</sup> Algae Natural Food, 67400 Illkirch-Graffenstaden, France <em>Corresponding author: </em>simon.roques@inra.fr<em> </em>
DOI: 10.25625/qvwcnt/g2efqa
title: Fig4ab beads raw metadata

description: TSbeads_473_250im_4p_20c_42d_1_MMStack_Pos0_metadata.txt
DOI: 10.25625/qvwcnt/rub0zs
title: Fig4b beads ISM

description: ISM_TSbeads_473_250im_4p_20c_42d_1_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt/vhy7ym
title: Fig4a beads CSD

description: SUM_TSbeads_473_250im_4p_20c_42d_1_MMStack_Pos0.ome.tif
DOI: 10.25625/qvwcnt
title: Figure 4

description: Data for Figure 4 All data files are standard TIFF files or text files. The TIFF files can be opened in ImageJ or Fiji. The super-resolved ISM images have been obtained from the raw data with the software provided at https://projects.gwdg.de/projects/csdism-2020. Note: Fig. 4 g) and h) are regions in e) and f) and have therefore no individual raw data. "blue", "green", "red" refers to the color channel in the final figure.
DOI: 10.5286/isis.e.rb1940600-1
title: Shutter closed; background
DOI: 10.25625/wte3oi/pz52ri
title: Fig3 bottom right

description: ISM_ref640_250im_4p_200c_0d40_conv_1_m12.tif
DOI: 10.25625/wte3oi/47dz0z
title: Fig 3 raw data

description: ref640_250im_4p_200c_0d40_conv_1.tif
DOI: 10.25625/wte3oi/ol0nks
title: Fig3 bottom left

description: ISM_ref640_125im_4p_200c_0d40_conv_1_m2.tif
DOI: 10.15468/dl.yh26v3
title: Occurrence Download

description: A dataset listing the 6 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Pseudoplatystoma fasciatum (Linnaeus, 1766)"
 ]
}

 The dataset's 6 records were derived from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010187-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8y76cv
title: Occurrence Download

description: A dataset containing 440 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Himatanthus tarapotensis (Schum. ex Markgr.) Plumel"
 ]
}

 The dataset includes 440 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0239859-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ncvivo
title: Occurrence Download

description: A dataset containing 412 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "TaxonKey is Loxodonta cyclotis (Matschie, 1900)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 412 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008533-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7crx7u
title: Occurrence Download

description: A dataset containing 1629 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Cervus elaphus Linnaeus, 1758",
 "Year 1970-1980"
 ]
}

 The dataset includes 1629 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0311042-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w4hqpx
title: Occurrence Download

description: A dataset containing 2051 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Vitex lucens Kirk)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2051 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051689-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kwsz3k
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is China",
 "Country is Chinese Taipei"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lutra lutra (Linnaeus, 1758)"
 ]
}

 The dataset includes 19 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0221253-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t8r44z
title: Occurrence Download

description: A dataset containing 3942 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((5.925 52.825,5.975 52.825,5.975 52.875,5.925 52.875,5.925 52.825))",
 "DatasetKey is Observation.org, Nature data from around the World",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 3942 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0306931-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.