cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22320373.v2
title: terraceDL: A geomorphology deep learning dataset of agricultural terraces in Iowa, USA

description: <strong>scripts.zip</strong> <br> <strong>arcgisTools.atbx:</strong> <strong>terrainDerivatives</strong>: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). <strong>rasterizeFeatures</strong>: convert vector polygons to raster masks (1 = feature, 0 = background). <br> <strong>makeChips.R</strong>: R function to break terrain derivatives and chips into image chips of a defined size. <strong>makeTerrainDerivatives.R</strong>: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). <strong>merge_logs.R</strong>: R script to merge training logs into a single file. <strong>predictToExtents.ipynb</strong>: Python notebook to use trained model to predict to new data. <strong>trainExperiments.ipynb</strong>: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. <strong>assessmentExperiments.ipynb</strong>: Python code to generate assessment metrics using PyTorch and the torchmetrics library. <strong>graphs_results.R</strong>: R code to make graphs with ggplot2 to summarize results. <strong>makeChipsList.R</strong>: R code to generate lists of chips in a directory. <strong>makeMasks.R</strong>: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool). <br> <strong>terraceDL.zip</strong> <br> <strong>dems</strong>: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. <strong>extents</strong>: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. <strong>vectors</strong>: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs<strong>. </strong>
DOI: 10.6084/m9.figshare.22320373.v1
title: terraceDL: A geomorpholgy deep learning dataset of agricultural terraces in Iowa, USA

description: <strong>scripts.zip</strong> <br> <strong>arcgisTools.atbx:</strong> <strong>terrainDerivatives</strong>: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). <strong>rasterizeFeatures</strong>: convert vector polygons to raster masks (1 = feature, 0 = background). <br> <strong>makeChips.R</strong>: R function to break terrain derivatives and chips into image chips of a defined size. <strong>makeTerrainDerivatives.R</strong>: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). <strong>merge_logs.R</strong>: R script to merge training logs into a single file. <strong>predictToExtents.ipynb</strong>: Python notebook to use trained model to predict to new data. <strong>trainExperiments.ipynb</strong>: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. <strong>assessmentExperiments.ipynb</strong>: Python code to generate assessment metrics using PyTorch and the torchmetrics library. <strong>graphs_results.R</strong>: R code to make graphs with ggplot2 to summarize results. <strong>makeChipsList.R</strong>: R code to generate lists of chips in a directory. <strong>makeMasks.R</strong>: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool). <br> <strong>terraceDL.zip</strong> <br> <strong>dems</strong>: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. <strong>extents</strong>: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. <strong>vectors</strong>: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs<strong>. </strong>
DOI: 10.6084/m9.figshare.22318522
title: vfillDL: A geomorphology deep learning dataset of valley fill faces resulting from mountaintop removal coal mining (southern West Virginia, eastern Kentucky, and southwestern Virginia, USA)

description: <strong>scripts.zip</strong> <br> <strong>arcgisTools.atbx:</strong> <strong>terrainDerivatives</strong>: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). <strong>rasterizeFeatures</strong>: convert vector polygons to raster masks (1 = feature, 0 = background). <br> <strong>makeChips.R</strong>: R function to break terrain derivatives and chips into image chips of a defined size. <strong>makeTerrainDerivatives.R</strong>: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). <strong>merge_logs.R</strong>: R script to merge training logs into a single file. <strong>predictToExtents.ipynb</strong>: Python notebook to use trained model to predict to new data. <strong>trainExperiments.ipynb</strong>: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. <strong>assessmentExperiments.ipynb</strong>: Python code to generate assessment metrics using PyTorch and the torchmetrics library. <strong>graphs_results.R</strong>: R code to make graphs with ggplot2 to summarize results. <strong>makeChipsList.R</strong>: R code to generate lists of chips in a directory. <strong>makeMasks.R</strong>: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool). <br> <strong>vfillDL.zip</strong> <br> <strong>dems</strong>: LiDAR DTM data partitioned into training, three testing, and two validation datasets. Original DTM data were obtained from 3DEP (https://www.usgs.gov/3d-elevation-program) and the WV GIS Technical Center (https://wvgis.wvu.edu/) . <strong>extents</strong>: extents of the training, testing, and validation areas. These extents were defined by the researchers. <strong>vectors</strong>: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers.
DOI: 10.6084/m9.figshare.22318522.v1
title: vfillDL: A geomorpholgy deep learning dataset of valley fill faces resulting from mountaintop removal coal mining (southern West Virginia, eastern Kentucky, and southwestern Virginia, USA)

description: <strong>scripts.zip</strong> <br> <strong>arcgisTools.atbx:</strong> <strong>terrainDerivatives</strong>: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). <strong>rasterizeFeatures</strong>: convert vector polygons to raster masks (1 = feature, 0 = background). <br> <strong>makeChips.R</strong>: R function to break terrain derivatives and chips into image chips of a defined size. <strong>makeTerrainDerivatives.R</strong>: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). <strong>merge_logs.R</strong>: R script to merge training logs into a single file. <strong>predictToExtents.ipynb</strong>: Python notebook to use trained model to predict to new data. <strong>trainExperiments.ipynb</strong>: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. <strong>assessmentExperiments.ipynb</strong>: Python code to generate assessment metrics using PyTorch and the torchmetrics library. <strong>graphs_results.R</strong>: R code to make graphs with ggplot2 to summarize results. <strong>makeChipsList.R</strong>: R code to generate lists of chips in a directory. <strong>makeMasks.R</strong>: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool). <br> <strong>vfillDL.zip</strong> <br> <strong>dems</strong>: LiDAR DTM data partitioned into training, three testing, and two validation datasets. Original DTM data were obtained from 3DEP (https://www.usgs.gov/3d-elevation-program) and the WV GIS Technical Center (https://wvgis.wvu.edu/) . <strong>extents</strong>: extents of the training, testing, and validation areas. These extents were defined by the researchers. <strong>vectors</strong>: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers.
DOI: 10.5281/zenodo.7671163
title: A robotic honeycomb for interaction with a honeybee colony

description: This repository contains data for the results described in: Barmak R. and Stefanec M., Hofstadler D., Piotet L., Schnwetter-F-S S., Mondada F., Schmickl T., Mills R.,<br> A robotic honeycomb for interaction with a honeybee colony.<br> Science Robotics, 2023<br> DOI: 10.1126/scirobotics.add7385
DOI: 10.5281/zenodo.7671162
title: A robotic honeycomb for interaction with a honeybee colony

description: This repository contains data for the results described in: Barmak R. and Stefanec M., Hofstadler D., Piotet L., Schnwetter-F-S S., Mondada F., Schmickl T., Mills R.,<br> A robotic honeycomb for interaction with a honeybee colony.<br> Science Robotics, 2023<br> DOI: 10.1126/scirobotics.add7385
DOI: 10.34740/kaggle/dsv/5216079
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.3886/icpsr08875.v3
title: Recidivism Among Released Prisoners, 1983: [United States]
title: Version 3

description: This data collection provides comprehensive criminal history data on prisoners released from custody in 1983. Precise estimates are supplied on recidivism among prisoners of all ages with all types of postrelease supervision. Data cover recidivism both within and outside the states in which the prisoners were released. Variables include sociodemographic indices, type of sentence, length of sentence, offense, court action, and date of court action.
description: Systematic stratified sample.
description: Datasets: DS1: Recidivism Among Released Prisoners, 1983: [United States]
description: Prisoners released from prisons in California, Florida, Illinois, Michigan, Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, and Texas in 1983.
description: This dataset has five groups of data at two levels. The first level (group 1, with 16,355 records and 71 variables) is an Identification and Corrections record, containing information about the individual offender. The second level (groups 2 through 5) consists of combinations of records that contain information about the offender based on one or more "cycles" or "events." Cycles consist of information on arrest (group 2, with 126,620 records and 59 variables), judicial (group 3, with 84,741 records and 123 variables), and custody or other post-sentencing information (group 4, with 35,337 records and 149 variables), and may not have all three segments, depending on the completeness of the information available from individual states. Events (group 5, with 36,844 records and 144 variables) represent unique transactions and consist of arrest, entry to jail or prison, military, or other non-criminal information. Cycles and events may be logically related, and they are ordered chronologically, from earliest arrest to the most recent.
DOI: 10.6084/m9.figshare.22318285
title: _ Emersion possibility preserved the energy metabolism of semiterrestrial crab Neohelice granulata (Dana, 1851) at high temperature.xlsx

description: We present the mortality, PO2 and lactate data we measured in the semiterrestrial crab <em>Neohelice granulata</em> under heat stress.
DOI: 10.6084/m9.figshare.22318285.v2
title: _ Emersion possibility preserved the energy metabolism of semiterrestrial crab Neohelice granulata (Dana, 1851) at high temperature.xlsx

description: We present the mortality, PO2 and lactate data we measured in the semiterrestrial crab <em>Neohelice granulata</em> under heat stress.
DOI: 10.6084/m9.figshare.22318285.v1
title: _ Emersion possibility preserved the energy metabolism of semiterrestrial crab Neohelice granulata (Dana, 1851) at high temperature.xlsx

description: We present the mortality, PO2 and lactate data we measured in the semiterrestrial crab <em>Neohelice granulata</em> under heat stress.
DOI: 10.35078/8dr1qf
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Uberaba| Município: Fronteira

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.3886/icpsr09073.v2
title: National Judicial Reporting Program, 1986
title: Version 2

description: This data collection tabulates the number of persons convicted of felonies in state courts and describes the sentences these individuals received. Data were collected from state courts and state prosecutors in 100 counties of the United States. Sociodemographic information includes age, race, and sex of felon. Types of offenses committed include homicide, rape, and robbery. Adjudication variables referring to the process between arrest and sentencing are also a part of this dataset. Data can be analyzed on a national level or by the individual counties.
description: A two-stage stratified cluster sampling design was employed. At the first stage all counties in the nation were divided into six strata. At the second stage, a systematic sample of felons sentenced for murder/nonnegligent manslaughter, rape, robbery, aggravated assault, burglary, felony larceny/motor vehicle theft, drug trafficking, and other offenses was selected from each county's official records.
description: Datasets: DS1: National Judicial Reporting Program, 1986
description: Sentenced felons in the United States.
DOI: 10.6084/m9.figshare.20449720
title: Cytogenetic and epigenetic aberrations in peripheral lymphocytes of northwest Arkansas Marshallese

description: Nuclear weapons testing in the northern Marshall Islands between 1946 and 1958 resulted in ionizing radiation (IR) exposure of the thousands of Marshallese. Furthermore, numerous islands were contaminated by radioactive fallout. Significant increases in cancer and metabolic syndrome incidences have been reported among Marshallese, and potential for further increases looms due to the latency of radiation-induced health effects. The purpose of this study was to investigate the genetic and epigenetic effects of exposure to IR that could be associated with radiation-induced disease among the Northwest Arkansas (NWA) Marshallese. We performed analysis of chromosomal aberrations and DNA methylation based on residential and exposure history of NWA Marshallese. Analysis of chromosomal aberrations demonstrated higher incidence of genetic rearrangements in women with self-reported history of radiation exposure (95% CI: 0.10, 1.22; <i>p</i>=.022). Further clustering of study participants based on their residential history demonstrated that participants who spent substantial amounts of time (≥6 months) in the northern atolls (thus, in the proximity of nuclear tests) before 1980 had more chromosomal aberrations than their peers who lived only in the southern atolls (95% CI: 0.08, −0.95; <i>p</i>=.021), and that this difference was driven by women. A relationship between the time spent in the northern atolls and increase in chromosomal aberrations was observed: 0.31 increase in chromosomal aberrations for every 10 years spent at northern atolls (95% CI: 0.06, 0.57; <i>p</i>=.020). Finally, significant inverse correlations between the chromosomal aberrations and the extent of DNA methylation of four LINE-1 elements L1PA2, L1PA16, L1PREC1, and L1P4B were identified. The results of this study provide first evidence of the presence of stable genetic and epigenetic rearrangements in peripheral lymphocytes of NWA Marshallese and warrant further studies to analyze the role of radiation exposure in health disparities experienced by this Pacific Island nation.
DOI: 10.6084/m9.figshare.20449720.v2
title: Cytogenetic and epigenetic aberrations in peripheral lymphocytes of northwest Arkansas Marshallese

description: Nuclear weapons testing in the northern Marshall Islands between 1946 and 1958 resulted in ionizing radiation (IR) exposure of the thousands of Marshallese. Furthermore, numerous islands were contaminated by radioactive fallout. Significant increases in cancer and metabolic syndrome incidences have been reported among Marshallese, and potential for further increases looms due to the latency of radiation-induced health effects. The purpose of this study was to investigate the genetic and epigenetic effects of exposure to IR that could be associated with radiation-induced disease among the Northwest Arkansas (NWA) Marshallese. We performed analysis of chromosomal aberrations and DNA methylation based on residential and exposure history of NWA Marshallese. Analysis of chromosomal aberrations demonstrated higher incidence of genetic rearrangements in women with self-reported history of radiation exposure (95% CI: 0.10, 1.22; <i>p</i>=.022). Further clustering of study participants based on their residential history demonstrated that participants who spent substantial amounts of time (≥6 months) in the northern atolls (thus, in the proximity of nuclear tests) before 1980 had more chromosomal aberrations than their peers who lived only in the southern atolls (95% CI: 0.08, −0.95; <i>p</i>=.021), and that this difference was driven by women. A relationship between the time spent in the northern atolls and increase in chromosomal aberrations was observed: 0.31 increase in chromosomal aberrations for every 10 years spent at northern atolls (95% CI: 0.06, 0.57; <i>p</i>=.020). Finally, significant inverse correlations between the chromosomal aberrations and the extent of DNA methylation of four LINE-1 elements L1PA2, L1PA16, L1PREC1, and L1P4B were identified. The results of this study provide first evidence of the presence of stable genetic and epigenetic rearrangements in peripheral lymphocytes of NWA Marshallese and warrant further studies to analyze the role of radiation exposure in health disparities experienced by this Pacific Island nation.
DOI: 10.5281/zenodo.7761259
title: Grammar learning strategies: Hungary & Poland: Pawlak & Csizér (2022)

description: Dataset for the study investigating the use of grammar learning strategies by Polish and Hungarian university students majoring in English.
description: N/A
DOI: 10.5281/zenodo.7761258
title: Grammar learning strategies: Hungary & Poland: Pawlak & Csizér (2022)

description: Dataset for the study investigating the use of grammar learning strategies by Polish and Hungarian university students majoring in English.
description: N/A
DOI: 10.5281/zenodo.7757810
title: SCICHEM data for methane dispersion in the Permian Basin

description: This dataset contains the input (meteorological and terrain) and output data (raw concentration output, calculated methane columns) from SCICHEM used in the pre-print article "Using Frequent, High-Resolution Remote Sensing to Identify Intermittent and Overlapping CH<sub>4</sub> sources in Oil and Gas Development Regions" to be submitted to <em>Journal of Geophysical Research - Atmosphere</em>. The meteorological input data for SCICHEM (https://github.com/epri-dev/SCICHEM) is included in the zipped file "MET_INPUT.zip" and includes the files METSCI2016.SFC - surface met file METSCI2016.PRF - upper air met file The terrain input data for SCICHEM is included in the file "TERRAIN_INPUT.zip" and includes the files ter.inp - the input file for the SCICHEM preprocessor TERSCI. The references NED data can be downloaded from http://www.mrlc.gov/viewerjs/ (seamless GeoTiff) or ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/NED (ArcGrid) terrain.ter - the SCICHEM input file produced by TERSCI *.out - log files produced by TERSCI The raw output for the continuous methane source tests are in the file "continuous_rel_case.zip". This folder contains several comma separated value (CSV) files with the following columns x - UTM east-west coordinates in m (corresponding to the projection used in the TERSCI terrain preprocessor) y - UTM north-south coordinates in m z - height above ground level (m) c - enhanced methane concentration in µg m<sup>-3</sup> t - time in the format YYYYMMDDHHMMSS The raw output for the instantaneous methane source tests are in the file "instantaneous_rel_case.zip". This folder contains several comma separated value (CSV) files with the following columns x - UTM east-west coordinates in m (corresponding to the projection used in the TERSCI terrain preprocessor) y - UTM north-south coordinates in m z - height above ground level (m) c - enhanced methane concentration in µg m<sup>-3</sup> t - time in the format YYYYMMDDHHMMSS The processed columns for the continuous methane source tests are in the file "CALC_METHANE_COL_OUTPUT.zip", which contains two CSV files for the continuous and instantaneous releases with the following columns x - UTM east-west coordinates in m y - UTM north-south coordinates in m t - time in the format YYYYMMDDHHMMSS BG_mask - TRUE if location was used in background calculation for plume mask tcol_g_m2_with_bg_and_no_noise - total (enhanced plus background) methane column (g m<sup>-2</sup>) tcol_g_m2_with_bg_and_half_pct_noise - the total methane column with 0.5% Gaussian noise added (g m<sup>-2</sup>) plume_mask - TRUE if region was tentatively identified as within the plume using the t-test median_filt_mask - TRUE if region was tentatively identified as within the plume after the median filter was applied final_filtered_plume - TRUE if region was identified as within the plume after all filters were applied