cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3942568
title: Comparing harvestmen species composition between habitats on Nantucket Island, Massachusetts

description: Dataset submitted in fulfillment of a 2010 Nantucket Biodiversity Initiative grant. From 2006 through 2008, we collected surface invertebrate specimens using pitfall traps from a variety of habitats on Nantucket Island and Tuckernuck Island, Massachusetts. In this project, we sorted 413 harvestmen from these samples, identified all specimens to species, and compared species distributions within four general habitat types. We identified all four species that appear on a historic species list from the late 1920’s and we added the non-native species Phalangium opilio. We found that H. maculosus prefers open heathland habitat over dense heathland and tupelo forest. Other species showed no significant preference for a specific habitat. We continue to sort specimens. allHarvestmentData.csv<br> anovaData.csv - data used for anova<br> dataDictionary.csv<br> mckenna-foster-paradis-nbi-report-2011.pdf<br> mckenna-foster-paradis-nbi-poster-2011.pdf - slides for a poster presentation at the 2011 NBIconference
DOI: 10.5281/zenodo.3942567
title: Comparing harvestmen species composition between habitats on Nantucket Island, Massachusetts

description: Dataset submitted in fulfillment of a 2010 Nantucket Biodiversity Initiative grant. From 2006 through 2008, we collected surface invertebrate specimens using pitfall traps from a variety of habitats on Nantucket Island and Tuckernuck Island, Massachusetts. In this project, we sorted 413 harvestmen from these samples, identified all specimens to species, and compared species distributions within four general habitat types. We identified all four species that appear on a historic species list from the late 1920’s and we added the non-native species Phalangium opilio. We found that H. maculosus prefers open heathland habitat over dense heathland and tupelo forest. Other species showed no significant preference for a specific habitat. We continue to sort specimens. allHarvestmentData.csv<br> anovaData.csv - data used for anova<br> dataDictionary.csv<br> mckenna-foster-paradis-nbi-report-2011.pdf<br> mckenna-foster-paradis-nbi-poster-2011.pdf - slides for a poster presentation at the 2011 NBIconference
DOI: 10.5281/zenodo.3942584
title: CPU Benchmarking data collected on CloudLab

description: This dataset includes over over 2M CPU performance measurements collected on the CloudLab testbed. For more information about *how* and *why* it is collected, please refer to the "Taming Performance Variability" paper (https://www.usenix.org/system/files/osdi18-maricq.pdf). For additional information about CPU measurements, you can refer to: https://www.flux.utah.edu/download?uid=288. All these records were exported from the live database dedicated to this CloudLab benchmarking project. The current version of the repo includes some memory benchmarking results (only June-July 2020).
DOI: 10.15468/w7xmwd
title: Description of a new species of the Rhinolophus trifoliatus-group (Chiroptera: Rhinolophidae) from Southeast Asia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Soisook, Pipat, Struebig, Matthew J., Noerfahmy, Sephy, Bernard, Henry, Maryanto, Ibnu, Chen, Shiang- Fan, Rossiter, Stephen J., Kuo, Hao- Chih, Deshpande, Kadambari, Bates, Paul J. J., Sykes, Dan, Miguez, Roberto Portela (2015): Description of a new species of the Rhinolophus trifoliatus-group (Chiroptera: Rhinolophidae) from Southeast Asia. Acta Chiropterologica 17 (1): 21-36, DOI: 10.3161/15081109ACC2015.17.1.002
DOI: 10.15468/9jwnb8
title: Description of a new species of the genus Aselliscus (Chiroptera, Hipposideridae) from Vietnam

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tu, Vuong Tan, Csorba, Gábor, Görföl, Tamás, Arai, Satoru, Son, Nguyen Truong, Thanh, Hoang Trung, Hasanin, Alexandre (2015): Description of a new species of the genus Aselliscus (Chiroptera, Hipposideridae) from Vietnam. Acta Chiropterologica 17 (2): 233-254, DOI: 10.3161/15081109ACC2015.17.2.002
DOI: 10.5281/zenodo.3742743
title: Corn-Soy Data Layer

description: <strong>Dataset Abstract:</strong> Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL) has played an important role in improving production forecasts and enabling large-scale study of agricultural inputs and outcomes. Although CDL offers crop type maps across the conterminous US from 2008 onward, such maps are missing in many Midwestern states or are uneven in quality before 2008. To fill these data gaps, we used the now-public Landsat archive and cloud computing services to map corn and soybean at 30m resolution across the US Midwest from 1999-2018. Our training data were CDL from 2008-2018, and we validated the predictions on CDL 1999-2007 where available, county-level crop acreage statistics, and state-level crop rotation statistics. The corn-soybean maps, which we call the Corn-Soy Data Layer (CSDL), are publicly hosted on Google Earth Engine and also available for download on Zenodo. <strong>Summary of Methods:</strong> Using Google Earth Engine, we trained a random forest classifier to classify each pixel of the study area into corn, soybean, and an aggregated "other crops" class. CDL 2008-2018 data were used as labels. The features input to the model were harmonic regression coefficients fit to the NIR, SWIR1, SWIR2, and GCVI bands/indices of time series from Landsat 5, 7, and 8 Surface Reflectance observations. Cloudy pixels were masked out using the <em>pixel_qa</em> band provided with Landsat Surface Reflectance products. <strong>Map Legend:</strong> 0 = outside study area 1 = corn 5 = soy 9 = other crop 255 = non-crop (masked by NLCD) Values were chosen to be consistent with CDL values when possible. <strong>Usage Notes:</strong> We recommend that users consider metrics such as (1) user's and producer's accuracy with CDL and (2) R<sup>2</sup> with NASS statistics across space and time to determine in which states/counties and years CSDL is of high quality. This can be done with the CSV file of user's and producer's accuracies included in this Zenodo, and annual county-level statistics and example code we have included in our repo at https://github.com/LobellLab/csdl.
DOI: 10.5281/zenodo.3240937
title: Snow-Cloud Validation Masks for Multispectral Satellite Data.

description: Geotiffs of manually validated snow, cloud, & clear-sky snow free pixels for 13 Landsat 8 images. These acquisitions are of mid-latitude mountainous regions that contain both snow and cloud cover. Four spectral libraries of snow and cloud are also provided. These are the snow and cloud spectra extracted from both these 13 scenes and the 13 L8 SPARCS Cloud Validation Masks that contained both snow and cloud. 1&2.) Snow and cloud top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands, aggregated from the 26 scenes. 3&4.) The top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands of all snow misidentified as cloud and cloud misidentified as snow by CFMASK, the cloud mask that ships in the BQA file of Landsat 8 Collection 1.
DOI: 10.5281/zenodo.3240936
title: Snow-Cloud Validation Masks for Multispectral Satellite Data.

description: Geotiffs of manually validated snow, cloud, & clear-sky snow free pixels for 13 Landsat 8 images. These acquisitions are of mid-latitude mountainous regions that contain both snow and cloud cover. Four spectral libraries of snow and cloud are also provided. These are the snow and cloud spectra extracted from both these 13 scenes and the 13 L8 SPARCS Cloud Validation Masks that contained both snow and cloud. 1&2.) Snow and cloud top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands, aggregated from the 26 scenes. 3&4.) The top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands of all snow misidentified as cloud and cloud misidentified as snow by CFMASK, the cloud mask that ships in the BQA file of Landsat 8 Collection 1.
DOI: 10.21227/smaf-a918
title: A Conditional Adversarial Network for Single Plane Wave Beamforming

description: Compared to traditional focused transmissions, plane wave (PW) ultrasound imaging could enable higher frame rates by over a hundred fold, which is clinically relevant to real-time applications and ultrafast imaging. However, along with reducing acquisition time, PW imaging is confounded by image quality degradation, acoustic clutter, and speckle noise. To tackle this problem, we present a deep learning-based method to analyze raw radiofrequency (RF) channel data acquired by the ultrasound probe and convert this signal to the final Bmode image, bypassing the traditional beamforming procedure. The deep learning architecture relies on a conditional generative adversarial network (cGAN), in which the generative model and classifying model work simultaneously to produce an indistinguishable output from a ground truth. The cGAN was trained to predict B-mode images that look like beamformed PW results after multiple insonifications. This network was trained and tested utilizing a publicly accessible PICMUS database composed of in vivo and ex vivo ultrasound inclusions with randomly distributed scatterers in various combinations. The proposed method produces signal-to-noise ratio (SNR) enhancements from 1.112 to 1.540 when compared with conventional delay-andsum (DAS) beamforming of a single PW insonification. The cross correlation coefficient between a 75 plane wave image and cGAN-predicted data is 0.976, compared to 0.641 with DAS beamforming of a single PW insonification. These results demonstrate the feasibility of using this adversarial network to substitute traditional DAS beamforming in future applications.
DOI: 10.17632/85zstkjpvs.1
title: Giant areas of enhanced conductivity and percolation threshold

description: AFM, MFM, phase conrast measurements of azolectin bilayer lipid membrane with magnetic nanoparticles
DOI: 10.17632/hgfyp5hv7s.1
title: Vandalism COVID-19 cases catalogue

description: Catalogue of vandal actions in the COVID-19 pandepic, statistic analysis and classification
DOI: 10.17632/fh6dtv82xd.1
title: Soil Parameter for SWAT Model

description: This data is supporting data in simulating the SWAT hydrological model. The input in the SWAT model, namely soil data, requires the inclusion of soil physical and chemical soil parameter data. The author considers a study related to the hydrology of an area is very necessary in preparing a regional planning concept. However, the obstacle in the study of hydrological models in Indonesia is data. One of them is data related to land conditions, namely soil. So the authors try to examine the availability of data using data that has been there before, namely by processing information contained in the Landsystem map released by the National Coordinating Survey and Mapping Agency in Indonesia (now renamed the Geospatial Information Agency) in the Regional Physical Planning Project for Transmigration (RePPProT) project ) in 1988 and data from the Online Web Soil Survey released by the USDA.
DOI: 10.17632/tv5y7xt9fb.1
title: Boron isotopes in tourmaline from hydrothermal ore systems

description: A new compilation of published data on the boron isotope composition of hydrothermal tourmaline is presented here. The database is provided in the excel file (Trumbull_etal_B-isotopes_Tourmaline_OreDeposits.xlsx) and contains ca. 4000 boron isotope analyses of tourmaline gathered from 57 publications (see References.docx), as well as additional information about the deposit, host rocks, age and temperature of the mineralization, particulars of tourmaline occurrence, etc. The file permits filtering based on the data and all other attributes. Some filter options that may be useful are: “Publication”, “Deposit category”, “Country”, “Tourmaline host category”, “Tourmaline origin”, “Fluid source”, “Zoning”, “Mineralization”. It is important to note that the article linked to this database (“Application of boron isotopes in tourmaline to understanding hydrothermal ore systems” by Trumbull et al.; doi: xx) discusses only tourmaline designated as “Hydrothermal” or “Hydrothermal?” in the database and reported in ISI-listed, English-language publications. This selection resulted in a total of 2622 d11B values reported in 49 publications. Cite this dataset Trumbull, Robert B.; Codeço, Marta S.; Jiang, Shao-Yong; Palmer, Martin R.; Slack, John F. (2020), “Boron isotopes in tourmaline from hydrothermal ore systems”, Mendeley Data, V1, doi: 10.17632/tv5y7xt9fb.1 Cite the article: Trumbull, Robert B.; Codeço, Marta S.; Jiang, Shao-Yong; Palmer, Martin R.; Slack, John F. (2020), “Application of boron isotopes in tourmaline to understanding hydrothermal ore systems”, Ore Geology Reviews, XX:YY-ZZ, doi:
DOI: 10.17632/tv5y7xt9fb.2
title: Boron isotopes in tourmaline from hydrothermal ore systems

description: A new compilation of published data on the boron isotope composition of hydrothermal tourmaline is presented here. The database is provided in the excel file (Trumbull_etal_B-isotopes_Tourmaline_OreDeposits.xlsx) and contains ca. 4000 boron isotope analyses of tourmaline gathered from 57 publications (see References.docx), as well as additional information about the deposit, host rocks, age and temperature of the mineralization, particulars of tourmaline occurrence, etc. The file permits filtering based on the data and all other attributes. Some filter options that may be useful are: “Publication”, “Deposit category”, “Country”, “Tourmaline host category”, “Tourmaline origin”, “Fluid source”, “Zoning”, “Mineralization”. It is important to note that the article linked to this database (“Application of boron isotopes in tourmaline to understanding hydrothermal ore systems” by Trumbull et al.; doi: xx) discusses only tourmaline designated as “Hydrothermal” or “Hydrothermal?” in the database and reported in ISI-listed, English-language publications. This selection resulted in a total of 2622 d11B values reported in 49 publications. Cite this dataset Trumbull, Robert B.; Codeço, Marta S.; Jiang, Shao-Yong; Palmer, Martin R.; Slack, John F. (2020), “Boron isotopes in tourmaline from hydrothermal ore systems”, Mendeley Data, V1, doi: 10.17632/tv5y7xt9fb.2 Cite the article: Trumbull, Robert B.; Codeço, Marta S.; Jiang, Shao-Yong; Palmer, Martin R.; Slack, John F. (2020), “Boron isotope variations in tourmaline from hydrothermal ore deposits: a review of controlling factors and insights for mineralizing systems”, Ore Geology Reviews, XX:YY-ZZ, doi:
DOI: 10.17632/z9swc8fd55.1
title: CONTRIBUTION OF ACCOMMODATION FACILITIES TO DIRECT EMISSIONS OF CARBON DIOXIDE (CO2) IN THE CITY OF PARNAÍBA (PIAUÍ STATE, BRAZIL)

description: Dataset of an article accepted by the Brazilian Journal of Tourism Research, with the title: CONTRIBUTION OF ACCOMMODATION FACILITIES TO DIRECT EMISSIONS OF CARBON DIOXIDE (CO2) IN THE CITY OF PARNAÍBA (PIAUÍ STATE, BRAZIL), including the structured interview model applied to the managers of the lodging facilities.
DOI: 10.17632/7jvwrm7ng3.1
title: Sample point data with land cover and land condition state for drylands in mainland Spain (2000-2010)

description: Table with 49,047 georeferenced points drawn from the drylands in mainland Spain through a stratified-random sampling design. Only points with agricultural or forestry land cover class are included. It has the following fields: • Lon: longitude in reference system EPSG 4326 • Lat: latitude in reference system EPSG 4326 • NUTS2: code of Autonomous Community (NUTS 2) • CLC06: code of CORINE Land Cover class for reference year 2006 • 2sRUEass: code of land condition state for period 2000-2010 CLC06 data were obtained from the CORINE LC portal (EEA, 2016). The original raster resolution of 250 m was rescaled to 1000 m by finding the modal land cover class at 250 m resolution within each 1000 m cell. Classes ‘Bare rocks’, ‘Sparsely vegetated areas’ and ‘Burnt areas’ were merged to the higher CLC level class ‘Open spaces with little or no vegetation’. Codes are consistent with the official CLC legend. Corresponding states of land condition (2dRUEass) were obtained from a 2dRUE application to Spain (Martínez-Valderrama et al., 2016; Sanjuán et al., 2014). Their codes are: 1-Underperforming anomaly, 2-Baseline performance, 3-Degraded, 4-Productive with low biomass, 5-Productive with high biomass, 6-Mature, 7-Reference performance and 8-Over-performing anomaly. Drylands were delimited according to the FAO-UNEP aridity index map associated to the cited 2dRUE output.
DOI: 10.17632/85zstkjpvs.2
title: Giant areas of enhanced conductivity and percolation threshold

description: AFM, MFM, phase conrast measurements of azolectin bilayer lipid membrane with magnetic nanoparticles
DOI: 10.17632/85zstkjpvs.3
title: Giant areas of enhanced conductivity and percolation threshold

description: AFM, MFM, phase conrast measurements of azolectin bilayer lipid membrane with magnetic nanoparticles
DOI: 10.17632/ghzpjbth3s.1
title: Protection heterogeneity in a harmonized European patent system

description: Data for Zingg, R., Elsner, E. "Protection heterogeneity in a harmonized European patent system". Eur J Law Econ (2020). https://doi.org/10.1007/s10657-020-09651-9
DOI: 10.17632/zx3kjzc3yx.3
title: MHCflurry 2.0: Improved pan-allele prediction of MHC Class I-presented peptides by incorporating antigen processing

description: Computational prediction of the peptides presented on MHC class I proteins is an important tool for studying T cell immunity. The data available to develop such predictors has expanded with the use of mass spec to identify naturally-presented MHC ligands. In addition to elucidating binding motifs, the identified ligands also reflect the antigen processing steps that occur prior to MHC binding. Here, we developed an integrated predictor of MHC I presentation that combines new models for MHC I binding and antigen processing. Considering only peptides first predicted by the binding model to bind strongly to MHC, the antigen processing model is trained to discriminate published mass spec-identified MHC I ligands from unobserved peptides. The integrated model outperformed the two individual components as well as NetMHCpan 4.0 and MixMHCpred 2.0.2 on held-out mass spec experiments. Our predictors are implemented in the open source MHCflurry package, version 2.0 (github.com/openvax/mhcflurry). See the paper at: https://doi.org/10.1016/j.cels.2020.06.010 Data S1. MULTIALLELIC benchmark dataset with predictions (CSV, gzipped). Data S2. MONOALLELIC benchmark dataset with predictions (CSV, gzipped). Data S3. Training data for MHCflurry 2.0 binding affinity (BA) predictor (CSV). Data S4. Training data for the variant of MHCflurry BA evaluated on the MONOALLELIC benchmark in Fig. S2 (CSV). Data S5. Training data for MHCflurry 2.0 antigen processing (AP) predictors (CSV). Data S6. Training data for MHCflurry 2.0 presentation score (PS) predictors (CSV).
DOI: 10.17632/jkz4bkb5j5.1
title: Zone-based Nottingham Bus Route Optimisation Instance

description: The present datasets contain instance data and results which were presented in the publications "Zone-based public transport route optimisation in an urban network" by Philipp Heyken Soares, 2020 in "Public Transport" (Springer) More detailed information can be found in the README files. ZiP files you find in the folders contain the entire data of the respective folders to simplify the download. Further included is a python program to evaluate route sets with the here provided instance data in the given format.
DOI: 10.17632/jk4kgf33k9.1
title: Datasets for: Age-related Response of Forest Floor BVOC Fluxes to Boreal Forest Succession after Wildfires

description: The processed data used for analysis presenting in the Figure 2, 3 and 4 in research paper Age-related Response of Forest Floor BVOC Fluxes to Boreal Forest Succession after Wildfires, and in the Figure S1, S2, S3 and Table S2 in supporting materials.
DOI: 10.26300/s501-ew23
title: Data from "The size of submesoscale baroclinic instability globally"
DOI: 10.17632/nj9r2k8zp9.4
title: Raw data for modeling the impact of consumer satisfaction on attitude toward the brand, brand preference, and purchase intentions of dairy products.

description: This data are collected via an online self-administered questionnaire from a sample of 195 Moroccan consumers of dairy products offered by the SAKIA brand. The questionnaire was self-administered via the Google Forms tool during the months of April 2020.
DOI: 10.5281/zenodo.3942600
title: Water Management Panel Data (1985-2018) of California's Western Sierra Nevada High-Elevation Basins

description: This is 34 years of water data from 14 high-elevation basins on the western slope of California's Sierra Nevada. All data originates from the California Data Exchange Center (CDEC: http://cdec.water.ca.gov/). The data were cleaned and aggregated between the years 2013-2019. The panel data table includes records of April-July runoff forecasts (the 50% exceedance forecast), forecast uncertainty (as measured by the difference between the 90% and 10% exceedance forecasts), available reservoir space to store runoff on March 31st, April-July releases, and the interaction term forecastXuncertainty. The data are organized as an unbalanced panel data table (N=416) for 34 years of records for 14 basins. All water volumes are reported in cubic kilometers. This data set includes a key that details the common names of each basins, each of their aggregated surface reservoir storage capacity, the measurement location where the data are aggregated in reference to, and a three letter code that links the key to the panel data table. This is the same three letter key used to query some of these data within CDEC. Detailed information on the project is available in the companion open-access publication (DOI available upon publication). The description of this data is in progress and will be fully updated upon acceptance of accompanying peer-reviewed journal article.
DOI: 10.5281/zenodo.3942599
title: Water Management Panel Data (1985-2018) of California's Western Sierra Nevada High-Elevation Basins

description: This is 34 years of water data from 14 high-elevation basins on the western slope of California's Sierra Nevada. All data originates from the California Data Exchange Center (CDEC: http://cdec.water.ca.gov/). The data were cleaned and aggregated between the years 2013-2019. The panel data table includes records of April-July runoff forecasts (the 50% exceedance forecast), forecast uncertainty (as measured by the difference between the 90% and 10% exceedance forecasts), available reservoir space to store runoff on March 31st, April-July releases, and the interaction term forecastXuncertainty. The data are organized as an unbalanced panel data table (N=416) for 34 years of records for 14 basins. All water volumes are reported in cubic kilometers. This data set includes a key that details the common names of each basins, each of their aggregated surface reservoir storage capacity, the measurement location where the data are aggregated in reference to, and a three letter code that links the key to the panel data table. This is the same three letter key used to query some of these data within CDEC. Detailed information on the project is available in the companion open-access publication (DOI available upon publication). The description of this data is in progress and will be fully updated upon acceptance of accompanying peer-reviewed journal article.
DOI: 10.26300/w8zh-m226
title: Data from "The scale and activity of symmetric instability globally"