cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5786937
title: GDSC1

description: GDSC1 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: https://www.orcestra.ca/pset/10.5281/zenodo.5786937 <strong>Disclaimer</strong> The GDSC1 data have been generated and shared by the Wellcome Trust Sanger Institute as part of the Genomics of Drug Sensitivity in Cancer (GDSC) Programme. The Haibe-Kains Lab has reprocessed and re-annotated the data to maximize overlap with other pharmacogenomic datasets. <strong>Data Usage Policy</strong> Users have a non-exclusive, non-transferable right to use data files for internal proprietary research and educational purposes, including target, biomarker and drug discovery. Excluded from this licence are use of the data (in whole or any significant part) for resale either alone or in combination with additional data/product offerings, or for provision of commercial services. Please note: The data files are experimental and academic in nature and are not licensed or certified by any regulatory body. Genome Research Limited provides access to data files on an “as is” basis and excludes all warranties of any kind (express or implied). If you are interested in incorporating results or software into a product, or have questions, please contact depmap@sanger.ac.uk. Source: https://depmap.sanger.ac.uk/documentation/data-usage-policy/ Sanger's terms and conditions: http://www.cancerrxgene.org/legal <strong>Please cite the following when using these data</strong> Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LFA, Saez-Rodriguez J, McDermott U, Garnett MJ. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016 Jul 28;166(3):740-754. doi: 10.1016/j.cell.2016.06.017. Epub 2016 Jul 7. PMID: 27397505; PMCID: PMC4967469. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, Ramaswamy S, Futreal PA, Haber DA, Stratton MR, Benes C, McDermott U, Garnett MJ. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013 Jan;41(Database issue):D955-61. doi: 10.1093/nar/gks1111. Epub 2012 Nov 23. PMID: 23180760; PMCID: PMC3531057. Picco G, Chen ED, Alonso LG, Behan FM, Gonçalves E, Bignell G, Matchan A, Fu B, Banerjee R, Anderson E, Butler A, Benes CH, McDermott U, Dow D, Iorio F, Stronach E, Yang F, Yusa K, Saez-Rodriguez J, Garnett MJ. Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR-Cas9 screening. Nat Commun. 2019 May 16;10(1):2198. doi: 10.1038/s41467-019-09940-1. PMID: 31097696; PMCID: PMC6522557.
DOI: 10.5281/zenodo.5786936
title: GDSC1

description: GDSC1 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: https://www.orcestra.ca/pset/10.5281/zenodo.5786937 <strong>Disclaimer</strong> The GDSC1 data have been generated and shared by the Wellcome Trust Sanger Institute as part of the Genomics of Drug Sensitivity in Cancer (GDSC) Programme. The Haibe-Kains Lab has reprocessed and re-annotated the data to maximize overlap with other pharmacogenomic datasets. <strong>Data Usage Policy</strong> Users have a non-exclusive, non-transferable right to use data files for internal proprietary research and educational purposes, including target, biomarker and drug discovery. Excluded from this licence are use of the data (in whole or any significant part) for resale either alone or in combination with additional data/product offerings, or for provision of commercial services. Please note: The data files are experimental and academic in nature and are not licensed or certified by any regulatory body. Genome Research Limited provides access to data files on an “as is” basis and excludes all warranties of any kind (express or implied). If you are interested in incorporating results or software into a product, or have questions, please contact depmap@sanger.ac.uk. Source: https://depmap.sanger.ac.uk/documentation/data-usage-policy/ Sanger's terms and conditions: http://www.cancerrxgene.org/legal <strong>Please cite the following when using these data</strong> Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LFA, Saez-Rodriguez J, McDermott U, Garnett MJ. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016 Jul 28;166(3):740-754. doi: 10.1016/j.cell.2016.06.017. Epub 2016 Jul 7. PMID: 27397505; PMCID: PMC4967469. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, Ramaswamy S, Futreal PA, Haber DA, Stratton MR, Benes C, McDermott U, Garnett MJ. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013 Jan;41(Database issue):D955-61. doi: 10.1093/nar/gks1111. Epub 2012 Nov 23. PMID: 23180760; PMCID: PMC3531057. Picco G, Chen ED, Alonso LG, Behan FM, Gonçalves E, Bignell G, Matchan A, Fu B, Banerjee R, Anderson E, Butler A, Benes CH, McDermott U, Dow D, Iorio F, Stronach E, Yang F, Yusa K, Saez-Rodriguez J, Garnett MJ. Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR-Cas9 screening. Nat Commun. 2019 May 16;10(1):2198. doi: 10.1038/s41467-019-09940-1. PMID: 31097696; PMCID: PMC6522557.
DOI: 10.25318/3310041601_eng
title: Business or organization plans regarding recruitment, retention and training, fourth quarter of 2021

description: Business or organization plans regarding recruitment, retention and training, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2021.
DOI: 10.25318/3310041601_fra
title: Plans des entreprises ou des organismes en matière de recrutement, de maintien en poste et de formation, quatrième trimestre de 2021

description: Plans des entreprises ou des organismes en matière de recrutement, de maintien en poste et de formation, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2021.
DOI: 10.6084/m9.figshare.17225453
title: Soil nematode genera diversity and precipitation changes in grasslands

description: During the 2016-2017 growing season, a survey of soil nematode diversity at the genus level was conducted across three grassland sites in the Great Plains region to investigate whether changes in precipitation through time and space affect nematode diversity and their contribution to C cycling. We experimentally reduced and increased growing-season precipitation for 2 years in 120 field plots at arid, semiarid, and mesic grasslands and assessed precipitation controls on nematode genus diversity, community structure and C footprint.. This data package includes nematode abundances for each sample categorized by genus.
DOI: 10.6084/m9.figshare.17225453.v1
title: Soil nematode genera diversity and precipitation changes in grasslands

description: During the 2016-2017 growing season, a survey of soil nematode diversity at the genus level was conducted across three grassland sites in the Great Plains region to investigate whether changes in precipitation through time and space affect nematode diversity and their contribution to C cycling. We experimentally reduced and increased growing-season precipitation for 2 years in 120 field plots at arid, semiarid, and mesic grasslands and assessed precipitation controls on nematode genus diversity, community structure and C footprint.. This data package includes nematode abundances for each sample categorized by genus.
DOI: 10.25318/3310041701_eng
title: Expected impact of the absence of government support programs on the survival of business or organization, fourth quarter of 2021

description: Expected impact of the absence of government support programs on the survival of business or organization, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2021.
DOI: 10.25318/3310041701_fra
title: Incidence prévue de l’absence de programmes de soutien gouvernemental sur la survie des entreprises ou des organismes, quatrième trimestre de 2021

description: Incidence prévue de l’absence de programmes de soutien gouvernemental sur la survie des entreprises ou des organismes, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2021.
DOI: 10.5281/zenodo.5787033
title: Supplementary files

description: Figure S1. The scores plot of a PCA for all NMR profiles. Figure S2. (A) The scores plot of an OPLS-DA model for all NMR profiles from OC users and eumenorrheic women (control). (B) Permutation plot for OPLS-DA model. It indicates that the OPLS-DA model is valid.
DOI: 10.5281/zenodo.5787032
title: Supplementary files

description: Figure S1. The scores plot of a PCA for all NMR profiles. Figure S2. (A) The scores plot of an OPLS-DA model for all NMR profiles from OC users and eumenorrheic women (control). (B) Permutation plot for OPLS-DA model. It indicates that the OPLS-DA model is valid.
DOI: 10.5281/zenodo.5787029
title: Data Stroke

description: https://docs.google.com/spreadsheets/d/1gyZvGAK0t3D83jHWWULAdhNmDYWvHa8nvzGJeLP4oVk/edit#gid=1826721354
DOI: 10.6073/pasta/ebc7abeda4e87c0d7be8f0a1c67a7e8a
title: Hubbard Brook Experimental Forest: Soil Freeze Study - Tree Growth

description: The climate is changing in many temperate forests with the amount of forest area dominated by sugar maple experiencing an insulating snowpack expected to shrink between 49 and 95% compared to 1951-2005 values. A reduced snowpack and increased depth and duration of soil frost can injure or kill fine roots, which are essential for plant water and nutrient uptake. These adverse impacts on tree roots can have important impacts on tree growth and ecosystem carbon sequestration. We evaluated the effects of changing winter climate, including snow and soil frost dynamics, by using tree cores to measure sugar maple radial growth rates in the Soil Freezing Study plots at the Hubbard Brook Experimental Forest. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Analysis of these data are published in: Reinmann AB, Susser JR, Demara EMC, and Templer PH. 2019. Declines in northern forest tree growth following snowpack decline and soil freezing. Global Change Biology. 25(2):420-430. https://doi.org/10.1111/gcb.14420
DOI: 10.5441/001/1.dv5mm289/1
title: Eurasian wigeon (Mareca penelope) Netherlands Lithuania 2018-2019

description: van Toor ML, Kharitonov S, Švažas S, Dagys M, Kleyheeg E, Müskens G, Ottosson U, Žydelis R, Waldenström J. 2021. Migration distance affects how closely Eurasian wigeons follow spring phenology during migration. Mov Ecol. 9:61. doi:10.1186/s40462-021-00296-0
description: Background: The timing of migration for herbivorous migratory birds is thought to coincide with spring phenology as emerging vegetation supplies them with the resources to fuel migration, and, in species with a capital breeding strategy also provides individuals with energy for use on the breeding grounds. Individuals with very long migration distances might however have to trade off between utilising optimal conditions en route and reaching the breeding grounds early, potentially leading to them overtaking spring on the way. Here, we investigate whether migration distance affects how closely individually tracked Eurasian wigeons follow spring phenology during spring migration. Methods: We captured wigeons in the Netherlands and Lithuania and tracked them throughout spring migration to identify staging sites and timing of arrival. Using temperature-derived indicators of spring phenology, we investigated how maximum longitude reached and migration distance affected how closely wigeons followed spring. We further estimated the impact of tagging on wigeon migration by comparing spring migratory timing between tracked individuals and ring recovery data sets. Results: Wigeons migrated to locations between 300 and 4000 km from the capture site, and migrated up to 1000 km in a single day. We found that wigeons migrating to more north-easterly locations followed spring phenology more closely, and increasingly so the greater distance they had covered during migration. Yet we also found that despite tags equalling only around 2% of individual’s body mass, individuals were on average 11–12 days slower than ring-marked individuals from the same general population. Discussion: Overall, our results suggest that migratory strategy can vary dependent on migration distance within species, and even within the same migratory corridor. Individual decisions thus depend not only on environmental cues, but potentially also trade-offs made during later life-history stages.
DOI: 10.5441/001/1.dv5mm289/2
title: Eurasian wigeon (Mareca penelope) Netherlands Lithuania 2018-2019-reference-data

description: van Toor ML, Kharitonov S, Švažas S, Dagys M, Kleyheeg E, Müskens G, Ottosson U, Žydelis R, Waldenström J. 2021. Migration distance affects how closely Eurasian wigeons follow spring phenology during migration. Mov Ecol. 9:61. doi:10.1186/s40462-021-00296-0
description: Background: The timing of migration for herbivorous migratory birds is thought to coincide with spring phenology as emerging vegetation supplies them with the resources to fuel migration, and, in species with a capital breeding strategy also provides individuals with energy for use on the breeding grounds. Individuals with very long migration distances might however have to trade off between utilising optimal conditions en route and reaching the breeding grounds early, potentially leading to them overtaking spring on the way. Here, we investigate whether migration distance affects how closely individually tracked Eurasian wigeons follow spring phenology during spring migration. Methods: We captured wigeons in the Netherlands and Lithuania and tracked them throughout spring migration to identify staging sites and timing of arrival. Using temperature-derived indicators of spring phenology, we investigated how maximum longitude reached and migration distance affected how closely wigeons followed spring. We further estimated the impact of tagging on wigeon migration by comparing spring migratory timing between tracked individuals and ring recovery data sets. Results: Wigeons migrated to locations between 300 and 4000 km from the capture site, and migrated up to 1000 km in a single day. We found that wigeons migrating to more north-easterly locations followed spring phenology more closely, and increasingly so the greater distance they had covered during migration. Yet we also found that despite tags equalling only around 2% of individual’s body mass, individuals were on average 11–12 days slower than ring-marked individuals from the same general population. Discussion: Overall, our results suggest that migratory strategy can vary dependent on migration distance within species, and even within the same migratory corridor. Individual decisions thus depend not only on environmental cues, but potentially also trade-offs made during later life-history stages.
DOI: 10.21410/dshs_2016/yu4ujj
title: Enquête sur les pratiques résidentielles et scolaires [2008]

description: Questionnaire proposé à des parents d'élèves de 4 lycées situés en Seine-Saint-Denis sur les contextes urbains et scolaires. Le questionnaire comprend 47 questions sur les thèmes des trajectoires sociales, résidentielles et scolaires des parents, ainsi que leur rapport au quartier et à la scolarité de leurs enfants (dont des questions plus précises sur le programme expérimental d'éducation prioritaire de Sciences Po). Leurs points de vue sur les différents aspects du lycée et plus généralement sur l’éducation y sont abordés. Les lieux d'habitat peuvent être différents du lieu de scolarisation (Les Pavillons-sous-Bois, Montfermeil, Villepinte....). Récupération de 1075 questionnaires (1191 déclarés passés).
DOI: 10.25345/c59g51
title: MassIVE MSV000088573 - Melanopsin (OPN4) is a novel oncogene in cutaneous melanoma: evidence from an experimental study
title: The goal of this study was to evaluate the role of OPN4 in melanoma development using in vitro, in vivo, and proteomics approaches.
DOI: 10.5281/zenodo.5787064
title: Cochlefelis spatula RB0458 ARBOR decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5787063
title: Cochlefelis spatula RB0458 ARBOR decontaminated FSCR

description: .
DOI: 10.6084/m9.figshare.8969882
title: Data & Code: "Predicting missing links in global host-parasite networks"

description: Data & Code from Farrell et al. "Predicting missing links in global host-parasite networks"<br><b>Scripts</b><br>Within the <i>scripts </i>folder are scripts to process the raw data and model results:<br>1. Download, clean, and merge host-parasite interaction databases with mammal supertree (<i>process_raw_data.R</i>)<br>2. Re-create raw data plots from the manuscript (<i>raw_data_plots.R)</i><br>3. Plot posterior interaction matrices, scaled trees, and pull out top predicted links (<i>model_summaries.R</i>)<br>4. Re-create diagnostic plots from the manuscript (<i>diagnostic_plots.R)</i><br>5. Functions used for data manipulation and visualization that are sourced by other scripts (<i>network_analysis.R</i>)<br>6. Investiage bias propagation via node degree product (<i>bias_investigation.R</i>)<br>7. Generate risk maps (<i>risk_maps.R)</i><br><br><br><b>Data</b><br><b><br></b>- <i>raw_data:</i> folder includes the data necessary to amalgamate the host-parasite interaction databases (via script '<i>process_raw_data.R</i>').<br>- <i>clean_data: </i>folder includes the full host-parasite interaction list <i>'hp_list' </i>in both .csv and .rds formats, as well as the binary interaction matrices for the full dataset and ones subset by parasite type (virus, bacteria, fungi, etc...), and model diagnostics ('<i>model_diagnostics.csv') </i>used in '<i>diagnostic_plots.R' .</i><br>- <i>model_results: </i>folder contains .rds files per model, which has the output interaction matrix from each simulation ('<i>P'</i>), the table of model diagnostics ('<i>TB</i>'), and the phylogeny scaling parameter (<i>'Eta'</i>), if applicable. Note that to save space the full cross-fold fit posteriors are omitted (these total ~ 4.5GB). Please contact MF if these are required. <br>-<i> literature_results:</i> folder contains a .csv version of the results of the literature search outlined in the Supplementary Information.<br>- <i>plots_tables: </i>folder contains .csv files for the top 100 'missing' links for each model, and a .csv for the top 1000 links from the full model run on the full dataset.<br><br><br><br>
DOI: 10.6084/m9.figshare.8969882.v1
title: Data & Code: "Predicting missing links in global host-parasite networks"

description: Data & Code from Farrell et al. "Predicting missing links in global host-parasite networks"<br><b>Scripts</b><br>Within the <i>scripts </i>folder are scripts to process the raw data and model results:<br>1. Download, clean, and merge host-parasite interaction databases with mammal supertree (<i>process_raw_data.R</i>)<br>2. Re-create raw data plots from the manuscript (<i>raw_data_plots.R)</i><br>3. Plot posterior interaction matrices, scaled trees, and pull out top predicted links (<i>model_summaries.R</i>)<br>4. Re-create diagnostic plots from the manuscript (<i>diagnostic_plots.R)</i><br>5. Functions used for data manipulation and visualization that are sourced by other scripts (<i>network_analysis.R</i>)<br>6. Investiage bias propagation via node degree product (<i>bias_investigation.R</i>)<br>7. Generate risk maps (<i>risk_maps.R)</i><br><br><br><b>Data</b><br><b><br></b>- <i>raw_data:</i> folder includes the data necessary to amalgamate the host-parasite interaction databases (via script '<i>process_raw_data.R</i>').<br>- <i>clean_data: </i>folder includes the full host-parasite interaction list <i>'hp_list' </i>in both .csv and .rds formats, as well as the binary interaction matrices for the full dataset and ones subset by parasite type (virus, bacteria, fungi, etc...), and model diagnostics ('<i>model_diagnostics.csv') </i>used in '<i>diagnostic_plots.R' .</i><br>- <i>model_results: </i>folder contains .rds files per model, which has the output interaction matrix from each simulation ('<i>P'</i>), the table of model diagnostics ('<i>TB</i>'), and the phylogeny scaling parameter (<i>'Eta'</i>), if applicable.<br>-<i> literature_results:</i> folder contains a .csv version of the results of the literature search outlined in the Supplementary Information.<br>- <i>plots_tables: </i>folder contains .csv files for the top 100 'missing' links for each model, and a .csv for the top 1000 links from the full model run on the full dataset.<br><br><br><br>
DOI: 10.5281/zenodo.5787104
title: ECCO v4 standard analysis sample (v4r1)

description: This version was generated in Julia from the ECCO output (version 4, release 1) available at https://www.ecco-group.org
DOI: 10.6084/m9.figshare.17229908
title: Geum urbanum traits

description: <i>Geum urbanum</i> traits measured in 2018 and 2019.