cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9lnrilt
title: MODFLOW-NWT model of the Former Badger Army Ammunition Plant, Wisconsin

description: This model archive makes available a calibrated, transient MODFLODW-NWT model and a MODPATH7 particle-tracking model used to simulate the groundwater flow system at the former Badger Army Ammunition Plant, in Sauk County, Wisconsin, during 1984&ndash;2020. The development of the MODFLODW-NWT and MODPATH7 models are described in the associated U.S. Geological Survey Scientific Investigations Report 2023-5040. This model archive contains all the files needed to document and run the groundwater flow and particle-tracking models. The directories in the archive are each presented as a separate .zip file and include an a &quot;bin&quot; directory, a &quot;georef&quot; directory, a &quot;model&rdquo; directory, an &quot;output&quot; directory, and a &quot;source&quot; directory. There is also an &ldquo;ancillary_pest&rdquo; directory included in the archive that contains files used during the calibration of the flow model. There is a README file describing all the files and directories in the archive and information on how to run the model. Each primary directory also contains a README file which describes the contents of that directory.
DOI: 10.5281/zenodo.7908028
title: Annual Average Suspended Sediment Concentration Composite Map (in GeoTIFF format)

description: These Geotiff files are intended for use only during the anonymous peer-review process of the manuscript. For the source regions of Yangtze River, please refer to files with the prefix "CJY". As for the Qinghai-Tibet Plateau, we have only uploaded one year of data for review. We will upload all the Geotiff files upon acceptance of the manuscript. Please feel free to contact the email provided if you have any questions:jinlongli@stu.scu.edu.cn.
DOI: 10.5281/zenodo.6898478
title: Annual Average Suspended Sediment Concentration Composite Map (in GeoTIFF format)

description: These Geotiff files are intended for use only during the anonymous peer-review process of the manuscript. For the source regions of Yangtze River, please refer to files with the prefix "CJY". As for the Qinghai-Tibet Plateau, we have only uploaded one year of data for review. We will upload all the Geotiff files upon acceptance of the manuscript. Please feel free to contact the email provided if you have any questions:jinlongli@stu.scu.edu.cn.
DOI: 10.5066/p92a0h98
title: Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020 (Data Release and Model Archive)

description: The U.S. Geological Survey and the University of Wisconsin &ndash; Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at Mainstem Plum Creek and West Plum Creek in northeastern Wisconsin. Monitoring data from 2010&ndash;2020 at Mainstem Plum and 2013&ndash;2020 at West Plum were used to detect changes in hydrologic and water-quality responses during runoff events. Runoff events were defined by hydrographers and used to compute event loads and event flow-weighted mean concentrations of total phosphorus and total suspended solids &ndash; all of which are included in this data release. Additionally, changes in these parameters were assessed between two time periods (&ldquo;initial&rdquo; and &ldquo;post-CMP implementation&rdquo;) using the R scripts included in this model archive. Because event discharges, loads, and concentrations are influenced by factors such as weather and the conditions preceding events, random-forest and regression models were developed to control for these factors and to elucidate water-quality changes more directly associated with CMP implementation. Residuals from random-forest models were used to detect changes between the two time periods via Wilcoxon signed-rank tests, and multiple linear regression models were used to determine percent change in responses via time-period dummy variable coefficients. Results indicate statistically insignificant changes in most responses during runoff events. This parent page serves as a landing page for two child items associated with Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020. Data release child page: contains the event times, event loads, and flow-weighted mean concentrations used for modeling purposes. Model archive child page: contains the inputs, scripts, and outputs used and produced to evaluate changes in water quality associated with conservation management practice implementation.
DOI: 10.5281/zenodo.7813673
title: Data in support of: `Two-Dimensional Strain Mapping with Scanning Precession Electron Diffraction: An Investigation into Data Analysis Routines'

description: This upload contains data in support of a manuscript currently under review. More details to follow.
DOI: 10.5281/zenodo.7813672
title: Data in support of: `Two-Dimensional Strain Mapping with Scanning Precession Electron Diffraction: An Investigation into Data Analysis Routines'

description: This upload contains data in support of a manuscript currently under review. More details to follow.
DOI: 10.5066/p9m41g3w
title: Data from Lab Experiments to Support Interim Guidance for Performing Calibration Checks on the Sequoia Scientific LISST-ABS Acoustic Backscatter Sensor

description: As part of its mission to unify and standardize research and development activities of Federal agencies involved in fluvial sediment studies, the Federal Interagency Sedimentation Project (FISP) partnered with the USGS Wyoming-Montana (WY-MT) Water Science Center to examine the potential for use of standard, low-tech laboratory equipment to perform calibration checks on the LISST-ABS&trade; sensor (hereinafter &lsquo;ABS sensor&rsquo;) on long-term deployments. The ABS sensor was developed by Sequoia Scientific (Sequoia) as an alternative to turbidity sensors for monitoring suspended sediment concentrations (SSCs) in surface waters. The lab experiments were intended to provide USGS scientists and the public with interim guidance to assist in operation and maintenance of the ABS sensor. The apparatus components chosen for the lab experiments consisted of a benchtop stir plate, 2-liter (L) glass beaker, stir bar, and benchtop stand. Three Sequoia ABS sensors were used in each step of the lab experiments, and a fourth was used for the field deployment. The data contained in this data release represent raw readings recorded from the ABS sensors over a range of sediment concentrations in units of milligrams per liter (mg/L). The readings were obtained via a software interface between a notebook computer attached via to the ABS sensors by communication The software interface was developed by Sequoia and sold alongside the sensors. The first five lines of each data file represent a header that contains additional, non-quantitative information including the serial number of the sensor being used to take readings. The data contained in this data release were taken during tests of accuracy and repeatability for different combinations of stir bar and sediment standards. The term &lsquo;sediment standards&rsquo; refers to sediments in which the nominal diameters of the particles in a mixture are known with reasonable precision. For the accuracy tests, concentration test intervals for sediment standards were chosen to represent a standard range of concentrations 1.1, 3.2, 10.4, 100, 1,000, and 10,000 mg/L. For the repeatability tests, concentration test intervals for particle standards were chosen to replicate Sequoia Scientific&rsquo;s calibration intervals: 1.1, 10.4, 100, 1,000, and 10,000 mg/L. The data are organized by sediment standard, sensor serial number, concentration, and stir bar type. The data contained in this data release were used to compute standard statistics and errors and create figures 5 through 8, and tables 4 through 10 in the accompanying Open-File Report (OFR) which describes the results of the laboratory experiments.
DOI: 10.6084/m9.figshare.22779392
title: Evaluating the Phototherapy Practice and Adherence to Guideline in Managing Neonatal Hyperbilirubinemia in an Indonesian Tertiary Referral Hospital

description: Dataset for Manuscript entitled "Evaluating the Phototherapy Practice and Adherence to Guideline in Managing Neonatal Hyperbilirubinemia in an Indonesian Tertiary Referral Hospital"
DOI: 10.6084/m9.figshare.22779392.v1
title: Evaluating the Phototherapy Practice and Adherence to Guideline in Managing Neonatal Hyperbilirubinemia in an Indonesian Tertiary Referral Hospital

description: Dataset for Manuscript entitled "Evaluating the Phototherapy Practice and Adherence to Guideline in Managing Neonatal Hyperbilirubinemia in an Indonesian Tertiary Referral Hospital"
DOI: 10.5281/zenodo.7908217
title: Precipitation data for paper submitted to WRR

description: This table includes precipitation indices calculated for regional flood statistical modelling.
DOI: 10.5281/zenodo.7908216
title: Precipitation data for paper submitted to WRR

description: This table includes precipitation indices calculated for regional flood statistical modelling.
DOI: 10.21942/uva.22140113
title: island reference dataset

description: Most accurate island shoreline dataset currently available, adjusted from Sayre et al. (2019) to be used as reference dataset for island naming in TABS R-package. <br> Sayre, R., S. Noble, S. Hamann, R. Smith, D. Wright, S. Breyer, K. Butler, K. Van Graafeiland, C. Frye, D. Karagulle, D. Hopkins, D. Stephens, K. Kelly, Z. Basher, D. Burton, J. Cress, K. Atkins, D. Van Sistine, B. Friesen, R. Allee, T. Allen, P. Aniello, I. Asaad, M. Costello, K. Goodin, P. Harris, M. Kavanaugh, H. Lillis, E. Manca, F. Muller-Karger, B. Nyberg, R. Parsons, J. Saarinen, J. Steiner, and A. Reed. 2019. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: 10.1080/1755876X.2018.1529714
DOI: 10.21942/uva.22140113.v1
title: island reference dataset

description: Most accurate island shoreline dataset currently available, adjusted from Sayre et al. (2019) to be used as reference dataset for island naming in TABS R-package. <br> Sayre, R., S. Noble, S. Hamann, R. Smith, D. Wright, S. Breyer, K. Butler, K. Van Graafeiland, C. Frye, D. Karagulle, D. Hopkins, D. Stephens, K. Kelly, Z. Basher, D. Burton, J. Cress, K. Atkins, D. Van Sistine, B. Friesen, R. Allee, T. Allen, P. Aniello, I. Asaad, M. Costello, K. Goodin, P. Harris, M. Kavanaugh, H. Lillis, E. Manca, F. Muller-Karger, B. Nyberg, R. Parsons, J. Saarinen, J. Steiner, and A. Reed. 2019. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: 10.1080/1755876X.2018.1529714
DOI: 10.25345/c5h12vj0s
title: MassIVE MSV000091880 - Macrophage-to-endothelial Cell Crosstalk by 27-hydroxycholesterol Promotes Atherosclerosis in Male Mice
title: Human aortic endothelial cells were treated with vehicle (LUM1_S07615), estradiol (LUM1_S07617) or 27-hydroxycholesterol (LUM1_S07618), estrogen receptor alpha was immunoprecipitated, and the changes in the receptor interactome were interrogated.
DOI: 10.5281/zenodo.7908247
title: The role of policies in reducing the cost of capital for offshore wind

description: The dataset contains the data represented in the graphs in the publication The role of policies in reducing the cost of capital for offshore wind
DOI: 10.5281/zenodo.7908246
title: The role of policies in reducing the cost of capital for offshore wind

description: The dataset contains the data represented in the graphs in the publication The role of policies in reducing the cost of capital for offshore wind
DOI: 10.5281/zenodo.7799440
title: Data from the Bioradar Work Package of the German BMBF Research Project SORTIE

description: This research is part of the German research project SORTIE and is supported by the German Federal Ministry of Education and Research (BMBF) under grant number 13N15189. Related journal articles: <em>[1]</em> <em>Shi, D.; Gidion, G.; Aftab, T.; Reindl, L.M.; Rupitsch, S.J. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors 2023, 23, 1335. </em>https://doi.org/10.3390/s23031335<em>.</em> <em>[2] Shi, D.; Gidion, G.; Reindl, L.M.; Rupitsch, S.J. Automatic life detection based on efficient features of ground-penetrating rescue radar signals. Submitted to Sensors.</em>
description: 1. Folder '01_Measurement_Raw_Data' contains the raw data of the experiment described in [2]. Using configurations given in the folder '03_SDR_Configuration_Data_Acquisation', you can make measurements that save the data in the same format. Using the Matlab code given in the folder '04_Signal_Processing_Feature_Extraction_and_Prediction_with_SVM_using_Matlab', you can process the raw data. 2. Folder '02_Feature_Table' contains two feature tables of all measurements provided in folder '01'. The first one has nine candidate features. The second one only includes the four features of the optimal set. These tables are made using the Matlab code in folder '04'. You can load a feature table directly in Matlab and use 'classificationLearner' to train and analyze classification models.
description: {"references": ["Shi, D.; Gidion, G.; Aftab, T.; Reindl, L.M.; Rupitsch, S.J. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors 2023, 23, 1335. https://doi.org/10.3390/s23031335.", "Shi, D.; Gidion, G.; Reindl, L.M.; Rupitsch, S.J. Automatic life detection based on efficient features of ground-penetrating rescue radar signals. Submitted to Sensors"]}
DOI: 10.5281/zenodo.7799439
title: Data from the Bioradar Work Package of the German BMBF Research Project SORTIE

description: This research is part of the German research project SORTIE and is supported by the German Federal Ministry of Education and Research (BMBF) under grant number 13N15189. Related journal articles: <em>[1]</em> <em>Shi, D.; Gidion, G.; Aftab, T.; Reindl, L.M.; Rupitsch, S.J. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors 2023, 23, 1335. </em>https://doi.org/10.3390/s23031335<em>.</em> <em>[2] Shi, D.; Gidion, G.; Reindl, L.M.; Rupitsch, S.J. Automatic life detection based on efficient features of ground-penetrating rescue radar signals. Submitted to Sensors.</em>
description: 1. Folder '01_Measurement_Raw_Data' contains the raw data of the experiment described in [2]. Using configurations given in the folder '03_SDR_Configuration_Data_Acquisation', you can make measurements that save the data in the same format. Using the Matlab code given in the folder '04_Signal_Processing_Feature_Extraction_and_Prediction_with_SVM_using_Matlab', you can process the raw data. 2. Folder '02_Feature_Table' contains two feature tables of all measurements provided in folder '01'. The first one has nine candidate features. The second one only includes the four features of the optimal set. These tables are made using the Matlab code in folder '04'. You can load a feature table directly in Matlab and use 'classificationLearner' to train and analyze classification models.
description: {"references": ["Shi, D.; Gidion, G.; Aftab, T.; Reindl, L.M.; Rupitsch, S.J. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors 2023, 23, 1335. https://doi.org/10.3390/s23031335.", "Shi, D.; Gidion, G.; Reindl, L.M.; Rupitsch, S.J. Automatic life detection based on efficient features of ground-penetrating rescue radar signals. Submitted to Sensors"]}
DOI: 10.5281/zenodo.7898399
title: AIRA (Atmospheric Rivers Identification Algorithm) input dataset and results

description: This dataset contains the output of three regional climate simulations covering Europe for the period 1991-2010, used to apply Atmospheric Rivers Identification Algorithm (AIRA in Spanish), and the results of its performance. The simulations were carried out by the MAR group (www.um.es/gmar) of the University of Murcia, using the WRF-Chem model (v3.6.1). Each simulation includes different levels of aerosol interactions.
DOI: 10.6084/m9.figshare.21529245
title: Data of the paper <em>"</em> <em>A Transformer-based Method to Reduce Cloud Shadow Interference in Automatic Lake Water Surface Extraction from Sentinel-2 Imagery " </em> in <em>Journal of Hydrology</em>

description: The compressed files are the training datasets, the validation dataset, the lake prediction result and a checkpoint in the paper " <em><strong>A Transformer-based Method to Reduce Cloud Shadow Interference in Automatic Lake Water Surface Extraction from Sentinel-2 Imagery</strong></em> " in <em>Journal of Hydrology</em> . The “<strong>Training datasets. rar</strong>” includes 5 independent training datasets containing 0%, 1%, 2%, 3% and 4% cloud shadows respectively. Each training dataset includes ground truth folder and remote sensing image folder, namely "gt" and "image", which constitute 5000 sample pairs in total. The “<strong>Validation dataset.rar</strong>” is used to valid the accuracy of the model trained by the five training datasets, also including two folders "gt" and "image" , with a total of 220 sample pairs.The ”<strong>Prediction result.rar</strong>” is the lake prediction result in the Inner River Basin of the Tibetan Plateau using the model trained by the training dataset containing 4% cloud shadows. There are 112 tiles of images covering this region, so a total of 112 folders are included. And the “<strong>BestCheckpoint.ckpt</strong>” file is the model with the highest accuracy in the paper. <br> For details, please refer to the paper: Yan, X ., Song, J., Liu, Y., Lu, S., Xu, Y., Ma, C., Zhu, Y., 2021. A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery. J. Hydrol. 620, 129561. https://doi.org/10.1016/j.jhydrol.2023.129561<br>
DOI: 10.6084/m9.figshare.21529245.v1
title: Data in paper “Transformer-based Method to Eliminate Cloud Shadow Interference in Automatic Lake Extraction from Sentinel-2 Imagery”

description: The compressed files are the training datasets, the validation dataset, the lake prediction result and a checkpoint in paper “<strong>Transformer-based Method to Eliminate Cloud Shadow Interference in Automatic Lake Extraction from Sentinel-2 Imagery</strong>”. The “<strong>Training datasets. rar</strong>” includes 5 independent training datasets containing 0%, 1%, 2%, 3% and 4% cloud shadows respectively. Each training dataset includes ground truth folder and remote sensing image folder, namely "gt" and "image", which constitute 5000 sample pairs in total. The “<strong>Validation dataset.rar</strong>” is used to valid the accuracy of the model trained by the five training datasets, also including two folders "gt" and "image" , with a total of 220 sample pairs.The ”<strong>Prediction result.rar</strong>” is the lake prediction result in the Inner River Basin of the Tibetan Plateau using the model trained by the training dataset containing 4% cloud shadows. There are 112 tiles of images covering this region, so a total of 112 folders are included. And the “<strong>BestCheckpoint.ckpt</strong>” file is the model with the highest accuracy in the paper.
DOI: 10.6084/m9.figshare.21529245.v2
title: Data of the paper <em>"</em> <em>A Transformer-based Method to Reduce Cloud Shadow Interference in Automatic Lake Water Surface Extraction from Sentinel-2 Imagery " </em> in <em>Journal of Hydrology</em>

description: The compressed files are the training datasets, the validation dataset, the lake prediction result and a checkpoint in the paper " <em><strong>A Transformer-based Method to Reduce Cloud Shadow Interference in Automatic Lake Water Surface Extraction from Sentinel-2 Imagery</strong></em> " in <em>Journal of Hydrology</em> . The “<strong>Training datasets. rar</strong>” includes 5 independent training datasets containing 0%, 1%, 2%, 3% and 4% cloud shadows respectively. Each training dataset includes ground truth folder and remote sensing image folder, namely "gt" and "image", which constitute 5000 sample pairs in total. The “<strong>Validation dataset.rar</strong>” is used to valid the accuracy of the model trained by the five training datasets, also including two folders "gt" and "image" , with a total of 220 sample pairs.The ”<strong>Prediction result.rar</strong>” is the lake prediction result in the Inner River Basin of the Tibetan Plateau using the model trained by the training dataset containing 4% cloud shadows. There are 112 tiles of images covering this region, so a total of 112 folders are included. And the “<strong>BestCheckpoint.ckpt</strong>” file is the model with the highest accuracy in the paper. <br> For details, please refer to the paper: Yan, X ., Song, J., Liu, Y., Lu, S., Xu, Y., Ma, C., Zhu, Y., 2021. A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery. J. Hydrol. 620, 129561. https://doi.org/10.1016/j.jhydrol.2023.129561<br>
DOI: 10.5066/p9005vc6
title: Data on flea larvae survival following exposure to black-tailed prairie dog scat, 2016-2018

description: We collected fecal pellets from six black-tailed prairie dogs in captivity. Prairie dogs were provided free access to non-treated grain (oat groats) or fipronil-treated grain for 5 days. Two prairie dogs received non-treated grain and four prairie dogs received fipronil grain. During each day of the feeding trial, prairie dog fecal pellets were collected, stored in sealable plastic bags, labeled by treatment, and frozen. Fecal pellets from days 2, 3, 4, and 5 were placed in 1.5 milliliter (mL) centrifuge tubes, ground into morsels and powder (fine particles for flea larvae to eat) with a disposable polypropylene pestle, separated as 0.50 milligram (mg) subsamples into pre-labeled centrifuge tubes, and frozen. Oropsylla montana flea larvae were assayed in 6-well microplates (first experiment) or 12-well microplates (second experiment). Small holes were made in the microplate caps, immediately above each well, to allow for air exchange. Each well contained sterilized fine sand substrate (~1/4 to 1/2 of well depth) for larvae locomotion and refuge. During each experiment, wells in some of the test plates received fipronil-laced prairie dog feces and wells in the remaining plates received non-treated feces (0.50 mg/well). The microplates were lidded, loosely covered with aluminum foil, and stored in a dark, cool storage location for 24 hours at ~23 �C and ~85% relative humidity. After 24 hours, the microplates were uncovered and opened. Probes were used to prod each larva for 2 seconds. Live larva responded by coiling and moving away from the prod. Larvae that did not respond within 2 seconds were prodded for 5 additional seconds and considered dead if no movement was observed. A 60x to 120x pocket microscope was used to determine if each larva consumed prairie dog feces (yes = visible meal, colored like prairie dog scat, in the gut; no = no visible meal in the gut). The data set (Fipronil Flea Larvae Survival Data.xlxs) includes data from the two experiments (Experiment = 1 or 2) with four Treatments (non-fipronil grain, no feces consumed by larvae; non-fipronil grain, feces consumed by larvae; fipronil grain, no feces consumed by larvae; fipronil grain, feces consumed by larvae). The data includes the number of larvae tested in each treatment and the number of larvae classified as alive 24 hours after initial exposure to prairie dog feces. Financial and logistical support were provided by U.S. Geological Survey, Centers for Disease Control and Prevention, U.S. Fish and Wildlife Service, and Colorado State University. Additional logistical support was provided by Scimetrics Limited Corporation and Genesis Laboratories, Inc.