cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5c56x
title: MassIVE Reanalysis RMSV000000346.10 - CCMS ProteoCarta MS-GF+ reanalysis of Escherichia coli from Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DDA dataset) [launched by 5562a0fadfa641ac9660eda88d8cdc04]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Escherichia coli (MSV000083784/PXD013885).
DOI: 10.25345/c53p0b
title: MassIVE Reanalysis RMSV000000346.11 - CCMS ProteoCarta MS-GF+ reanalysis of Pseudomonas aeruginosa from Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DDA dataset) [launched by 5562a0fadfa641ac9660eda88d8cdc04]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Pseudomonas aeruginosa (MSV000083784/PXD013885).
DOI: 10.25345/c5zz7h
title: MassIVE Reanalysis RMSV000000346.12 - CCMS ProteoCarta MS-GF+ reanalysis of Streptococcus agalactiae from Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DDA dataset) [launched by 5562a0fadfa641ac9660eda88d8cdc04]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Streptococcus agalactiae (MSV000083784/PXD013885).
DOI: 10.25345/c5qg30
title: MassIVE Reanalysis RMSV000000346.14 - CCMS ProteoCarta MS-GF+ reanalysis of Staphylococcus saprophyticus from Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DDA dataset) [launched by 5562a0fadfa641ac9660eda88d8cdc04]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Staphylococcus saprophyticus (MSV000083784/PXD013885).
DOI: 10.25345/c5v55w
title: MassIVE Reanalysis RMSV000000346.13 - CCMS ProteoCarta MS-GF+ reanalysis of Staphylococcus epidermidis from Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DDA dataset) [launched by 5562a0fadfa641ac9660eda88d8cdc04]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Staphylococcus epidermidis (MSV000083784/PXD013885).
DOI: 10.5066/p9ioipsi
title: Fracture and Bedding Plane Analysis of Bedrock Beneath Bull Shoals Dam Using Acoustic and Optical Televiewer Geophysical Logs, Baxter County, Arkansas, 2021

description: In May 2021, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, logged 9 previously drilled boreholes in the bedrock beneath Bull Shoals Dam with acoustic and optical televiewer geophysical methods. The acoustic and optical televiewer logging results were used to identify fractures and small bedding planes in the bedrock beneath the dam using the image and structure interpretation module of Well CAD (v.5.4). This data release consists of each geophysical log in standard log American Standard Code for Information Interchange (ASCII) text (.las) format and a comma separated (.csv) file detailing the fracture and bedding plane analysis completed on each geophysical log.
DOI: 10.6084/m9.figshare.15025296.v3
title: Desmodus rotundus Occurrence Record Database

description: The common vampire bat (<i>Desmodus rotundus</i>) is a hematophagous bat species found across the North, Central, and South American continents. <i>Desmodus rotundus </i>is one of the only tree mammal species exclusively having a sanguivorous diet (i.e., blood). The species has a large distributional range and predates on a large range of vertebrate species. <i>Desmodus rotundus</i> is a know reservoir for the rabies virus and contributes to the continued spread of this pathogen across Latin America. Nevertheless, little is known about the historical distribuion of <i>D. rotundus</i> across it range. Historical occurrence data are critical for the assessment of past and current distributions for this species, and is necessary for a plethora of other ecological, biogeographic, and epidemiological studies. This is a dataset of <i>D. rotundus </i>historical occurrence including &gt;37,000 locality reports across the Americas to facilitate spatiotemporal studies of the species.<br>Data and metadata definitions. The following table provides standardized definitions of each occurrence metadata based on the the Darwin Core Archive format. Each piece of metadata for each occurrence is organized and recorded under the listed column headers.<br>Validation Code: This file contains code for usage and cleaning of the <i>Desmodus rotundus </i>record database. This code was also used for the Technical Validation process of the final <i>D. rotunudus</i> dataset.
DOI: 10.5281/zenodo.5534716
title: Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: code and processed data

description: Manuscript, figure, and analysis code and processed data for the Groza et al (2021) preprint.
DOI: 10.6084/m9.figshare.16695118.v1
title: OBLRtrappingdosageClarksvilleForMS.xlsx

description: Trap captures of obliquebanded leafroller, <i>Choristoneura rosaceana</i> (Harris) to pheromone blends in ratios approximating those reported in pheromone glands and a novel blend based on a volatile headspace collection from live virgin females were evaluated in field experiments in Michigan apple orchards. In an initial field trapping study, pheromone lures composed of either a three- or four-component blend approximation of the blend present in female pheromone glands at doses ranging from 0.1 to 20 mg/lure were compared. The four-component blend was a combination of (<i>Z</i>)-11-tetradecenyl acetate (<i>Z</i>11-14:Ac), (<i>E</i>)-11-tetradecenyl acetate (<i>E</i>11-14:Ac), (<i>Z</i>)-11-tetradecen-1-ol (<i>Z</i>11-14:OH), and (<i>Z</i>)-11-tetradecenal (<i>Z</i>11-14:Al) in a ratio of 96.5:1.8:1.4:0.2, respectively, while the three-component blend lacked <i>Z</i>11-14:Al. Pheromone emissions by groups of virgin females and commercial lures were collected in the laboratory and analyzed by gas chromatography. These data were used to formulate a new pheromone lure that was compared to a commercial lure in a second trapping study. In the first field study, traps baited with 10 mg pheromones or above captured significantly more moths than traps baited with 1 mg or less, regardless of the blend. Surprisingly, groups of virgin females only emitted two detectable pheromone components, <i>Z</i>11-14:Ac and <i>Z</i>11-14:OH in a ratio of 37:63 which was substantially different from the blends detected in pheromone glands in the literature. The newly formulated pheromone lure based on females’ emission was more than twice as attractive as the commercial lure which emitted a 74:5:21 three-component blend of <i>Z</i>11-14:Ac, <i>E</i>11-14:Ac, and <i>Z</i>11-14:OH. Our results suggested that the actual pheromones released by females may not translate directly to the content of pheromone glands.<b></b>
DOI: 10.5281/zenodo.5534785
title: ROR GRID Equivalents 2021-09-23

description: Subset of Sep 2021 ROR data file as CSV with just name, country, ROR ID and GRID ID. Intended for quickly finding the equivalent ROR ID for a given GRID ID.
DOI: 10.5281/zenodo.5534784
title: ROR GRID Equivalents 2021-09-23

description: Subset of Sep 2021 ROR data file as CSV with just name, country, ROR ID and GRID ID. Intended for quickly finding the equivalent ROR ID for a given GRID ID.
DOI: 10.5281/zenodo.5114330
title: ROR GRID Equivalents 2021-07-19

description: Subset of April 2021 ROR data file as CSV with just name, country, ROR ID and GRID ID. Intended for quickly finding the equivalent ROR ID for a given GRID ID.
DOI: 10.5281/zenodo.5114329
title: ROR GRID Equivalents 2021-07-19

description: Subset of April 2021 ROR data file as CSV with just name, country, ROR ID and GRID ID. Intended for quickly finding the equivalent ROR ID for a given GRID ID.
DOI: 10.15780/g2008h
title: CHIRTSdaily

description: Assessing weather-related hazards in a changing climate requires data sets that have accurate high-resolution spatial mean fields, good performance in data-sparse regions, and limited sources of non-stationary errors. High-resolution mean fields are important because impacts on health, agriculture, and other sectors are always local and typically non-linear. Impacts on humans or crops will be related to extremes in specific locations, and these impacts will often be strongly related to the variations in the absolute value of the weather variable under consideration. At present, there is a dearth of accurate information supporting the monitoring and evaluation of extreme temperatures in many food-insecure regions. Such extremes can wilt crops or decimate livestock herds, setting the stage for famine. Yet our ability to track these extremes in countries without weather station observations remains very limited. To address this limitation, the Climate Hazards Center (CHC) has developed a quasi-global (60°S – 70°N), high-resolution (0.05° x 0.05°, approx. 5km) data set of daily maximum and minimum temperatures, which we refer to as CHIRTS-daily. The CHIRTS-daily development and validation process is outlined in Figure 1. Briefly, the monthly fields from the CHIRTSmax1 data series are temporally disaggregated using downscaled fields from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA52,3). The ERA5 has been shown to have robust performance with respect to spatial covariance and daily anomalies. However, ERA5 shows significant cool bias globally, and most notably in Africa. This cool bias in turn results in ERA5 underrepresenting the number of extreme hot days globally, and most notably in Africa. Figure 2 quantifies the representation of hot days for ERA5, CHIRTS-daily, Princeton’s Global Meteorological Forcing dataset (PGF), and stations from the ~15,000 Berkeley Earth database. Table 1 quantifies the difference in mean number of hot days between 2007-2016 and 1983-1992 for these products.
DOI: 10.15780/g2t668
title: CHPclim - High-quality monthly rainfall climatology

description: Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1, is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.
DOI: 10.25739/t651-yy97
title: Genomes to Fields 2019 dataset

description: The dataset contains results from 2019 phenotypic evaluations of multi-location field trials. Also included is field level weather and soil data for the multiple locations where field testing was conducted. Data was collected by collaborators in the Genomes to Fields (G2F) initiative. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers, and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments.
DOI: 10.5281/zenodo.5534796
title: Harvey-Lab-UW/Morris_etal_2021_EcolApps: Release of data for Morris et al. 2021 EcolApps

description: This is the latest release of data for reproducing the analyses in the manuscript 'Does the legacy of historical thinning treatments foster resilience to bark beetle outbreaks in subalpine forests?' by Morris, Buonanduci, Agne, Battaglia, and Harvey published in Ecological Applications. <em>See the main text of the manuscript for complete descriptions of how data were collected, and greater specifics on values and classifications.</em> This information is licensed under a Creative Commons Attribution 4.0 International License. Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.
DOI: 10.5281/zenodo.5527234
title: Harvey-Lab-UW/Morris_etal_2021_EcolApps: Release of data for Morris et al. 2021 EcolApps

description: This is the latest release of data for reproducing the analyses in the manuscript 'Does the legacy of historical thinning treatments foster resilience to bark beetle outbreaks in subalpine forests?' by Morris, Buonanduci, Agne, Battaglia, and Harvey published in Ecological Applications. <em>See the main text of the manuscript for complete descriptions of how data were collected, and greater specifics on values and classifications.</em> This information is licensed under a Creative Commons Attribution 4.0 International License. Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.
DOI: 10.5517/ccdc.csd.cc2794k8
title: CCDC 2064369: Experimental Crystal Structure Determination

description: Related Article: Yuan‐Yuan Guo,  Clement Elliott,  Jason A. McNulty,  David B. Cordes,  Alexandra M. Z. Slawin,  Philip Lightfoot|2021|Eur.J.Inorg.Chem.|2021|3404|doi:10.1002/ejic.202100433
DOI: 10.5517/ccdc.csd.cc2794l9
title: CCDC 2064370: Experimental Crystal Structure Determination

description: Related Article: Yuan‐Yuan Guo,  Clement Elliott,  Jason A. McNulty,  David B. Cordes,  Alexandra M. Z. Slawin,  Philip Lightfoot|2021|Eur.J.Inorg.Chem.|2021|3404|doi:10.1002/ejic.202100433
DOI: 10.5517/ccdc.csd.cc2794mb
title: CCDC 2064371: Experimental Crystal Structure Determination

description: Related Article: Yuan‐Yuan Guo,  Clement Elliott,  Jason A. McNulty,  David B. Cordes,  Alexandra M. Z. Slawin,  Philip Lightfoot|2021|Eur.J.Inorg.Chem.|2021|3404|doi:10.1002/ejic.202100433
DOI: 10.5517/ccdc.csd.cc28435q
title: CCDC 2089312: Experimental Crystal Structure Determination

description: Related Article: David K. Tran, Ahmed Z. Rashad, Donald J. Darensbourg, Karen L. Wooley|2021|Polym.Chem.|12|5271|doi:10.1039/D1PY00784J
DOI: 10.6084/m9.figshare.16652890.v1
title: Documenting Soil Archive Use in Peer-Reviewed Publications: 1915-2021

description: Peer-reviewed publications with documented use of soil archives were identified through a literature search using the USDA National Agricultural Library’s Digitop search engine, supplemented with results from Wageningen University & Research Library and Google Scholar. References in identified publications were further screened for additional publications relating to use of archived soils. Only publications in English or with English abstracts were considered. Identified publications were vetted to ensure archived soils were experimentally used in each study. For purposes of this compilation, archived soils were designated as those that had been stored for five or more years. A total of 245 publications were identified. Each publication was reviewed for key metadata, including publication year, age of oldest sample, and sample collection location. Land use associated with archived soil, along with the subject of each investigation were also recorded. Compiled metadata were transcribed into a single spreadsheet with sortable columns. An abstract for each publication was included in the spreadsheet, and when available, a digital object identifier was recorded to facilitate electronic access. Compiled information in the spreadsheet provides researchers a valuable resource for documenting use of soil archives globally.
DOI: 10.34808/9pyd-ep91
title: Organochlorine pesticides and polichlorinated biphenyls concentrations in fresh snowfall or top layer of snow from Hornsund region, Svalbard, in the spring 2019

description: The dataset contains concentration of organochlorine persistent organic pollutants in snow samples collected from top layer of snow, which corresponded to fresh snowfall in most cases (except DS location, where these are 20 cm top layer sampled weekly). All snow samples have been collected within one month during spring 2019, in the vicinity of the Polish Polar Station, Svalbard. Snow sample location names ending in .1, .2 and .3 are local replicates of the same sample, i.e. snow sampled according to the same protocol, samples taken within the spacing of 1-3 m from one another. Sample locations H, R, F and DS refer to: Hans glacier, Revdalen (valley), Fugleberget slope, and the Environmental hut of the Polish Polar Station, respectively. All concentrations are given in ng/L of melted snow (water), i.e. ng/kg snow. Compound names are given at the top of columns denoting concentrations, for which an average of 3 analytical replicates and a standard deviation of those replicates is reported.The dataset is part of a project funded by SIOS (Svalbard Integrated Arctic Earth Observing System) Research Infrastructure Access Project 2018_0009 Sval-POPs: Spatial VAriabiLity: VALidation dataset on POPs concentrations in snow. The Polish Ministry for Science and Higher Education is thanked for the financial support to the SIOS programme, and the Polish Polar Station Hornsund crew from the 41st expedition of IG PAS, especially T. Biernacka and K. Ziemba, as well as A. Spolaor and C. Larose are acknowledged for valuable field assistance.
DOI: 10.21223/2weyhb/myqyod
title: 001_Data_entry_of_OFSP_traders_UG_N.tab
DOI: 10.21223/2weyhb/gkmygh
title: 002_Data_entry_of_OFSP_traders_UG_S.tab
DOI: 10.21223/2weyhb/m3huj5
title: 003­_Data_dictioanry_of_OFSP_traders_UG.tab

description: :unav
DOI: 10.21223/2weyhb
title: Dataset for: Sweetpotato value chain actors in Mbale and Gulu markets, Uganda

description: 112 traders in different markets, in Mbale and Gulu of Norther and Tororo, Bugiri, Busia in the Eastern regions of Uganda, were surveyed about the value chain Orange-fleshed Sweetpotato (OFSP)
DOI: 10.5281/zenodo.5122832
title: CINECA synthetic cohort NA Canada CHILD [CC-BY-NC-SA]

description: The "CINECA synthetic cohort NA Canada CHILD" dataset is a synthetic dataset developed to provide insight into how data is structured for select common attributes in the CHILD Cohort Study, but not reveal any personal or identifiable information associated with cohort participants. Such synthetic datasets are valuable for software developers to be able to see specific examples of data for common attributes (i.e. a minimal metadata model of a selection of common variables usually present in cohorts). This dataset comprises 100 variables for 150 synthetic participants which have faked phenotypic data that reflects CHILD cohort data. In addition, there is genetic data based on the 1000 Genomes project. This dataset was created within the context of the CINECA project. More information about the creation of this dataset can be found in the included documentation. <br> <em>Please note this preamble must be included with any distribution of this dataset: </em>This synthetic dataset (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about CHILD cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY; https://creativecommons.org/licenses/by-nc-sa/4.0/). Please ensure this preamble is included with this dataset and that the CHILD project and the CINECA project (funding: EC H2020 grant 825775 and CIHR grant 404896) are acknowledged. If you have any questions about this dataset contact Fiona Brinkman at brinkman@sfu.ca or Erin Gill at egill@sfu.ca. <strong>CINECA synthetic cohorts</strong> CINECA synthetic cohort Africa H3ABioNet CINECA synthetic cohort Europe CH SIB CINECA synthetic cohort Europe UK1