cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5559238
title: Daily Rainfall Anomalies in the Brazilian Northeast (DRA-BNE)

description: This dataset contains the daily precipitation anomalies in the Brazilian northeast calculated from the GPCP dataset (combined sources of precipitation measurement) in the period from 1996-10-01 to 2020-12-01, in the geographic boundaries 42°W - 30°W/20.5°S - 0°. The GPCP dataset has an original resolution of 1 degree, but this dataset was interpolated by the bilinear method, obtaining a resolution of 0.25 degrees.
description: This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – through the project Ciências do Mar II, AUXPE 1979/2014 and a pos doc grant to H. L. Varona. Finance Code 001.
description: {"references": ["H. L. Varona. (2021). mNC: A tool for Oceanographers and Meteorologists to easily create their NetCDF files using Matlab (1.0). Zenodo. https://doi.org/10.5281/zenodo.5572749", "H. L. Varona. (2021). CalcPlotAnomaly: Matlab function set for the calculation and plotting of anomalies (1.01). Zenodo. https://doi.org/10.5281/zenodo.5576889"]}
DOI: 10.6073/pasta/bc291c8544892df8fd866282d5769deb
title: Juvenile Salmonid Emigration Monitoring in the Lower American River, California.

description: Overview Operation of rotary screw traps on the Lower American River is part of a collaborative effort by the U.S. Fish and Wildlife Service, Pacific States Marine Fisheries Commission, and the California Department of Fish and Wildlife. The primary objectives of the study are to collect data that can be used to estimate the passage of juvenile fall-run Chinook Salmon Oncorhynchus tshawytscha and to quantify the raw catch of steelhead Oncorhynchus mykiss as well as winter, spring, and late fall runs of Chinook Salmon. Secondary objectives of the trapping operations focus on collecting biological data on juvenile salmonids and gathering environmental data that will be used to develop models that correlate environmental parameters with salmonid size, temporal presence, abundance, and production. The data package contains seven datasets including: raw catch, trap operation, environmental, and trap efficiency data. Raw Catch – Chinook Dataset This dataset covers ALL Chinook Salmon captured by the rotary screw traps. This spreadsheet includes biological data on: 1) unmarked fall-, spring-, and winter-run Chinook Salmon 2) marked (adipose clipped OR "fin clip") hatchery origin Chinook Salmon 3) recaptured marked fall-run (BBY OR "Pigment / Dye", Photonic Dye, and VIE OR "Elastomer") Chinook Salmon utilized in trap efficiency trials. Raw Catch – Steelhead Dataset This dataset covers ALL steelhead captured by the rotary screw traps. This spreadsheet includes biological data on: 1) unmarked (natural origin) steelhead 2) marked (adipose clipped OR "fin clip") hatchery origin steelhead 3) recaptured marked steelhead utilized in trap efficiency trials. Raw Catch – ByCatch Dataset This dataset provides biological data on ALL catch (EXCLUDING Chinook Salmon or steelhead) captured by the rotary screw traps. All catch in this table is of natural origin. Trap Operations Dataset This dataset provides trap operation data for each trap visit. Specifically, it includes data on the visit type, trap functioning status, start and end sampling dates and times, total revolutions and instantaneous revolution speeds, livewell intake statuses, debris data, and whether the catch associated for this record will be include for juvenile production estimates. Environmentals Dataset This dataset covers environmental data collected at the rotary screw traps. This spreadsheet includes turbidity (nephelometric turbidity units), water velocity (meters per second), dissolved oxygen (milligrams per liter), and water depth (centimeters). Trap Efficiency Summary Dataset This dataset provides a summary of each trap efficiency trial conducted. It includes the release date and time, the length of the test, the number of marked fish released and recaptured, and whether the trial was included for juvenile production estimates. Trap Efficiency Release FL Dataset This dataset provides biological data on a subsample of released fish utilized in trap efficiency trials.
DOI: 10.13011/m3-d7a6-ha90
title: DS1GlobusTest
DOI: 10.25625/4uwtwb/cyyfi1
title: 1967_A.B.A._Referees' and Judges' Manual.pdf

description: :unav
DOI: 10.25625/4uwtwb
title: A.b.a. referees' and judges' manual

description: Handbuch
DOI: 10.5281/zenodo.5786313
title: Index for use with the genotyper KAGE

description: Index for use with the genotyper KAGE. This index is for all variants &gt; 0.1% allele frequency from the 1000 Genomes Project and is generated by this pipeline: https://github.com/ivargr/genotyping-benchmarking, commit id 9e25292d96e168b46f4b2c72b407fce607bb1a1b.
DOI: 10.5255/ukda-sn-855298
title: Colombia River Stories Datasets Archive, 1994-2020

description: The goal of the project is the environmental monitoring of water resources using an innovative and multi-disciplinary methodology that brings social scientists and natural scientists together - integrating their research methods and techniques to capture human stories through community-level, participatory research and the river's story through field-based scientific monitoring and environmental reconstruction and mapping. The social science data of the project includes in-depth life history style interviews with individuals from the Afro-Colombian and Indigenous communities of the Atrato. The project used purposive sampling, recruiting participants through local contacts and then ‘snowballing’ for additional participants. As part of our citizen science programme, monitoring of the Rio Quito was carried out on a weekly basis by a local community. The monitoring consisted of measurement of in-situ parameters (turbidity, pH, conductivity, dissolved oxygen and temperature) and water sampling of Rio Quito. They also monitored the bacterial contamination of their drinking water. The hydrogeochemical data sets were based on a range of analytical results carried out on water (river and rain) and soil (riverbank and river bottom) samples, predominantly from the Rio Quito and the town of Quibdo. The parameters being acquired were the water temperature, pH, specific electrical conductivity (SEC), total dissolved solids (TDS), oxidation reduction potential (ORP), dissolved oxygen (DO), alkalinity, major anion and cations, water stable isotopes and suite of dissolved and total metals in water. The satellite images archive consists of temporal satellite images between 1994- 2020 that were pre-processed and processed in order to highlight changes that were observed along the river bank due to anthropogenic action. A temporal change detection analysis has been evaluated by highlighting the observed changes through time. Atrato Subcatchment Sediment Load Estimation & analysis of mining impacts on the planform of the Rio Quito quantifies the ways and extent to which alluvial gold mining in the Chocó has impacted the geomorphological form and function of the rivers in which it occurs. It specifically focusses on the assessment of mining impacts on: 1. total river sediment loads; 2. river suspended sediment concentration and turbidity; 3. river channel planform. The results presented contribute to the wider project objective of understanding the ways in which mining-driven changes to river form and function intersect with, and influence, the socio-economic, social-political, and socio-cultural systems that drive alluvial gold mining and conflict in the Chocó.
DOI: 10.5281/zenodo.5777843
title: Harnessing large-scale herbarium image datasets through representation learning (application images)

description: Herbarium specimen images used in the manuscript <em>Harnessing large-scale herbarium image datasets through representation learning</em> for application tasks 2 and 3. These tasks were formulated to test the applicability of representations learned by three different neural networks trained on the Half-Earth Challenge dataset. All images are for specimens held by Royal Botanic Gardens, Kew. The images from Kew's HerbCat service, using URLs collated by iDigBio. We first search iDigBio for the specimens we wanted, then downloaded the associated occurrence and image metadata files. We then used the URLs in the image metadata files to download the specimen images. The code used to download these images can be found in the GitHub repository for the paper. We searched iDigBio using these parameters: <pre><code>[x] Must have media Genus: Dendrobium Institution Code: K Basis of Record: PreservedSpecimen</code></pre> ©<em> All images copyright of the Board of Trustees of the Royal Botanic Gardens, Kew.</em>
DOI: 10.5281/zenodo.5777842
title: Harnessing large-scale herbarium image datasets through representation learning (application images)

description: Herbarium specimen images used in the manuscript <em>Harnessing large-scale herbarium image datasets through representation learning</em> for application tasks 2 and 3. These tasks were formulated to test the applicability of representations learned by three different neural networks trained on the Half-Earth Challenge dataset. All images are for specimens held by Royal Botanic Gardens, Kew. The images from Kew's HerbCat service, using URLs collated by iDigBio. We first search iDigBio for the specimens we wanted, then downloaded the associated occurrence and image metadata files. We then used the URLs in the image metadata files to download the specimen images. The code used to download these images can be found in the GitHub repository for the paper. We searched iDigBio using these parameters: <pre><code>[x] Must have media Genus: Dendrobium Institution Code: K Basis of Record: PreservedSpecimen</code></pre> ©<em> All images copyright of the Board of Trustees of the Royal Botanic Gardens, Kew.</em>
DOI: 10.6084/m9.figshare.17216567
title: Top 100 2021 - Techno Remix

description: In my third (and final!) Top 100 I’m putting aside both approaches in favour of a new method that’s inspired on the work of scientometricians from the last decade. Broadly speaking, citation performance is different by discipline, format and age (just like altmetrics!) and researchers have sought to normalize these variations by developing field-normalized metrics. Examples include the Field-Weighted Citation Index (FWCI), the Relative Citation Ratio (RCR) and the Field Citation Ratio (FCR).
DOI: 10.6084/m9.figshare.17216567.v1
title: Top 100 2021 - Techno Remix

description: In my third (and final!) Top 100 I’m putting aside both approaches in favour of a new method that’s inspired on the work of scientometricians from the last decade. Broadly speaking, citation performance is different by discipline, format and age (just like altmetrics!) and researchers have sought to normalize these variations by developing field-normalized metrics. Examples include the Field-Weighted Citation Index (FWCI), the Relative Citation Ratio (RCR) and the Field Citation Ratio (FCR).
DOI: 10.6084/m9.figshare.11283758.v6
title: Global Hydropower Database (GHD) and hydroelectricity production (HP) model

description: 1. GHD comprises 8716 hydropower plants distributed across 134 countries. The detailed description can be found in the Supplementary and the main text of the following article:Wan W, Zhao J, Popat E, Herbert C, Döll P. Analyzing the Impact of Streamflow Drought on Hydroelectricity Production: A Global-Scale Study. Water Resources Research. 2021, 4(57):e2020WR028087.<br><br>We kindly ask users to cite this article in any published material produced using the GHD.<br>2. 0.5° global drainage direction map DDM30 (Döll & Lehner, 2002)3. R script of hydroelectricity production model4. Other .nc file used<br>
DOI: 10.5281/zenodo.5786847
title: Raw data for the HiTIMe deliverable 1.3. - deliver material with magnetic dopants

description: Raw data for the HiTIMe deliverable 1.3. - TEM, SEM, AFM images, EDS spectra
DOI: 10.5281/zenodo.5786846
title: Raw data for the HiTIMe deliverable 1.3. - deliver material with magnetic dopants

description: Raw data for the HiTIMe deliverable 1.3. - TEM, SEM, AFM images, EDS spectra
DOI: 10.18112/openneuro.ds003959.v1.0.0
title: Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brain
DOI: 10.5281/zenodo.5773227
title: Dietary-based Environmental Harm

description: Collected survey data about perceptions.
DOI: 10.5281/zenodo.5773226
title: Dietary-based Environmental Harm

description: Collected survey data about perceptions.
DOI: 10.6084/m9.figshare.17216645.v1
title: Centris_data_article.xlsx

description: Data from the article "Method for maintaining adult solitary bee <i>Centris analis</i> under laboratory conditions" accepted in the Methods in Ecology and Evolution journal <br>
DOI: 10.6084/m9.figshare.17217542
title: Fort McKay South CH4 and CO2 mole fractions August 2019

description: This dataset contains hourly-averaged continuous measurements of methane (CH<sub>4</sub>), carbon dioxide (CO<sub>2</sub>), and carbon monoxide (CO) dry air mole fractions, as well as wind temperature and direction. These data were taken using a Picarro G2401 gas analyzer. The data obtained spans from the 16<sup>th</sup> to the 23<sup>rd</sup> of August 2019 and was taken at the Environment Canada atmospheric monitoring site Fort McKay South (57°08’57.54” N, 111°38’32.66” W), approximately 3 meters above ground level.
DOI: 10.5255/ukda-sn-855343
title: Mums Alone. The Relationship Between Loneliness, Social Isolation, Depression and Mother-Infant Bonding in the Perinatal Period: A Collaborative, Mixed Methods Study, 2000-2021

description: Background Around one in five women will experience depression in the perinatal period. This is a period of transition for women’s identities, social networks and relationships. Growing evidence suggests that loneliness, isolation and lack of social support are risk factors for depression. However, little research has explored pathways between these and perinatal depression, or whether women themselves connect loneliness and isolation to their mental health. Methods This project involved secondary data analysis only. We analysed qualitative interviews with fourteen women diagnosed with perinatal depression to see whether they described isolation and loneliness as contributing to their distress. We also examined the relationship between social support and depression, using quantitative data from 525 women interviewed in pregnancy and postnatally. We analysed all data collaboratively with a group of women with lived experience of perinatal mental health difficulties. Findings Qualitatively, we found that motherhood interacted with other aspects of women’s identities, such as being young, single, deprived, or a woman of colour, to result in isolation and feelings of 'dislocated' self and relationships, particularly for some mothers. Women felt confined to their homes by the mothering role, isolated from wider social networks, and unsupported by their partners/families. Fears of being seen as ‘bad mothers’ provoked feelings of inadequacy and made it hard to make authentic connections with others, increasing isolation and depression. Quantitatively, we found that a lack of social support antenatally was related to postnatal depression, and this relationship was stronger for women who were not living with a partner. Conclusions Our findings suggest that isolation, loneliness and social support are important in relation to perinatal depression. Our analysis highlights a need to: develop support which addresses interpersonal/social networks; advocate for social change to lessen pressures on perinatal women and their families; and challenge the social constructions of the ‘good mother’.
DOI: 10.5281/zenodo.5786527
title: GDSC1

description: GDSC1 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: https://www.orcestra.ca/pset/10.5281/zenodo.5786527 <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.5786526
title: GDSC1

description: GDSC1 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: https://www.orcestra.ca/pset/10.5281/zenodo.5786527 <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: 10.6084/m9.figshare.17216645.v2
title: Centris_data_article.xlsx

description: Data from the article "Method for maintaining adult solitary bee <i>Centris analis</i> under laboratory conditions" accepted in the Methods in Ecology and Evolution journal <br>
DOI: 10.5517/ccdc.csd.cc25zptp
title: CCDC 2025503: Experimental Crystal Structure Determination

description: Related Article: Yan Shu, Jia-Peng Wang, Bing-Xian Li, Jun-Li Gan, Hao Ding, Rui Liu, Le Cai, Zhong-Tao Ding|2022|Phytochemistry|194|113009|doi:10.1016/j.phytochem.2021.113009
DOI: 10.34740/kaggle/dsv/2935446
title: Printed Digits Dataset
DOI: 10.5281/zenodo.5786475
title: Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y13 & Y14

description: A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2008 (Y13) and 2009 (Y14).
DOI: 10.5281/zenodo.5786474
title: Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y13 & Y14

description: A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2008 (Y13) and 2009 (Y14).
DOI: 10.5281/zenodo.5786894
title: Raw data for HITIMe Deliverable 1.2. - advanced charge doping

description: Raw data (SEM, EDX) for HiTIMe deliverable 1.2. - advanced charge doping
DOI: 10.5281/zenodo.5786893
title: Raw data for HITIMe Deliverable 1.2. - advanced charge doping

description: Raw data (SEM, EDX) for HiTIMe deliverable 1.2. - advanced charge doping