cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8011445
title: Indian Soil Erodibility Dataset (ISED)

description: Soil erosion has been spotted as one of the major global problems, and soil erodibility is the property of soil which refers to the erosive nature of the soil. India is also struggling with the erosion problem in most parts of the country. A national-scale soil erosion study is needed to assess such types of problems. ISED (Indian Soil Erodibility Dataset) is a step toward building datasets in mapping soil erosion at a national scale after IRED (Indian Rainfall Erosivity Dataset). This dataset consists of K-factor (t-ha-h/ha/MJ/mm), soil erodibility indices like CR (Clay Ratio), Modified Clay Ratio (MCR), and CLOM (Critical Level of Organic Matter) map over India at high resolution (250 m). It also includes a Susceptibility to Erosion map of India due to Organic Matter availability. Distribution maps of soil erodibility corresponding to the districts and soil types of India are also added to this dataset. This dataset is an additional dataset at a national scale for soil erosion modeling which will be handy data for experts and researchers.
DOI: 10.5281/zenodo.8005339
title: NESPOMILA Audio Data

description: The files here are audio data collected under the <em>New Speakers of Minority Languages: Proficiency, Variation and Change</em> project (2021--2023) The files are responses to a Rapid Automated Picture Naming Task and a Video Narration Task. Audio files correspond to transcriptions in the project's open archive (see the Related Identifiers field). Languages: German, Kashubian, Polish, Russian, Wymysorys
description: The research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021: 2020/37/K/HS2/02779.
DOI: 10.5281/zenodo.8005338
title: NESPOMILA Audio Data

description: The files here are audio data collected under the <em>New Speakers of Minority Languages: Proficiency, Variation and Change</em> project (2021--2023) The files are responses to a Rapid Automated Picture Naming Task and a Video Narration Task. Audio files correspond to transcriptions in the project's open archive (see the Related Identifiers field). Languages: German, Kashubian, Polish, Russian, Wymysorys
description: The research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021: 2020/37/K/HS2/02779.
DOI: 10.5281/zenodo.8011197
title: Lake Mendota metabolism model and data set

description: This is a zipped file that includes the model code, written in R, as well as the input and output data for the model. This publication accompanies the manuscript entitled, Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake
DOI: 10.5281/zenodo.8011196
title: Lake Mendota metabolism model and data set

description: This is a zipped file that includes the model code, written in R, as well as the input and output data for the model. This publication accompanies the manuscript entitled, Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake
DOI: 10.5281/zenodo.8010936
title: IODP Expedition 369 Magnetic susceptibility (whole round)

description: Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
DOI: 10.5281/zenodo.8010935
title: IODP Expedition 369 Magnetic susceptibility (whole round)

description: Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
DOI: 10.5281/zenodo.8010790
title: nichenetr and multinichenetr packages addendum of MultiNicheNet paper

description: Ligand-Treatment datasets for NicheNet-v2 model evaluation (nichenetr package vignette) Subsetted scRNA-seq datasets (SingleCellExperiment objects) for the several multinichenetr package vignettes
DOI: 10.5281/zenodo.8010789
title: nichenetr and multinichenetr packages addendum of MultiNicheNet paper

description: Ligand-Treatment datasets for NicheNet-v2 model evaluation (nichenetr package vignette) Subsetted scRNA-seq datasets (SingleCellExperiment objects) for the several multinichenetr package vignettes
DOI: 10.5281/zenodo.8008825
title: GEO-Bench: m-SA-crop-type

description: <strong>GEO-Bench: m-SA-crop-type dataset</strong> This dataset has been modified to be included in the GEO-Bench dataset. All changes with respect to the original version are documented at https://github.com/ServiceNow/geo-bench. The original version of this dataset is due to the Western Cape Department of Agriculture and the Radiant Earth Foundation (2021) and is available at: https://mlhub.earth/data/ref_south_africa_crops_competition_v1. See the LICENSE file provided alongside this dataset for applicable licensing information.
DOI: 10.5281/zenodo.8008824
title: GEO-Bench: m-SA-crop-type

description: <strong>GEO-Bench: m-SA-crop-type dataset</strong> This dataset has been modified to be included in the GEO-Bench dataset. All changes with respect to the original version are documented at https://github.com/ServiceNow/geo-bench. The original version of this dataset is due to the Western Cape Department of Agriculture and the Radiant Earth Foundation (2021) and is available at: https://mlhub.earth/data/ref_south_africa_crops_competition_v1. See the LICENSE file provided alongside this dataset for applicable licensing information.
DOI: 10.5281/zenodo.7797969
title: Data Collection and Manipulation Template for FlexTool

description: This repository includes the following files: <strong>1) FlexTool_Data_Collection_template.xlsx: </strong>Workbook for inserting raw data, manipulating it and preparing FlexTool input sheets. The data collection and manipulation template for IRENA FlexTool 2.0 serves as a quick and standardized methodology for putting together a single-node country model in FlexTool.<br> <strong>2) FlexTool_Starter_template.xlsm: </strong>Template FlexTool input file structured to receive the input sheets, as prepared in the FlexTool_Data_Collection_template.xlsx<br> <strong>3) Data Collection Template for IRENA FlexTool.pdf</strong>: Guidelines for preparing a FlexTool model based on the Data Collection and Manipulation template.
DOI: 10.5281/zenodo.8011219
title: Data Collection and Manipulation Template for FlexTool

description: This repository includes the following files: <strong>1) FlexTool_Data_Collection_template.xlsx: </strong>Workbook for inserting raw data, manipulating it and preparing FlexTool input sheets. The data collection and manipulation template for IRENA FlexTool 2.0 serves as a quick and standardized methodology for putting together a single-node country model in FlexTool.<br> <strong>2) FlexTool_Starter_template.xlsm: </strong>Template FlexTool input file structured to receive the input sheets, as prepared in the FlexTool_Data_Collection_template.xlsx<br> <strong>3) Data Collection Template for IRENA FlexTool.pdf</strong>: Guidelines for preparing a FlexTool model based on the Data Collection and Manipulation template. <strong>4) OSeMOSYS and FlexTool data sharing</strong>: Instructions on how to populate the FlexTool input file from the OSeMOSYS SAND file<br> FlexTool data file population: <strong>5) Instructions on how to populate the FlexTool Data Collection File from the OSeMOSYS Data Collection File</strong>.
DOI: 10.5281/zenodo.7797968
title: Data Collection and Manipulation Template for FlexTool

description: This repository includes the following files: <strong>1) FlexTool_Data_Collection_template.xlsx: </strong>Workbook for inserting raw data, manipulating it and preparing FlexTool input sheets. The data collection and manipulation template for IRENA FlexTool 2.0 serves as a quick and standardized methodology for putting together a single-node country model in FlexTool.<br> <strong>2) FlexTool_Starter_template.xlsm: </strong>Template FlexTool input file structured to receive the input sheets, as prepared in the FlexTool_Data_Collection_template.xlsx<br> <strong>3) Data Collection Template for IRENA FlexTool.pdf</strong>: Guidelines for preparing a FlexTool model based on the Data Collection and Manipulation template. <strong>4) OSeMOSYS and FlexTool data sharing</strong>: Instructions on how to populate the FlexTool input file from the OSeMOSYS SAND file<br> FlexTool data file population: <strong>5) Instructions on how to populate the FlexTool Data Collection File from the OSeMOSYS Data Collection File</strong>.
DOI: 10.5281/zenodo.7884191
title: Mapping the neuroethological signatures of pain, analgesia and recovery in mice

description: <strong>Mapping the neuroethological signatures of pain, analgesia and recovery in mice.</strong><br> Repository containing datasets obtained for M. Bohic, L Pattison, et al. 2023. Neuron. <strong>Abstract</strong><br> Ongoing pain is driven by direct activation of pain-sensing neurons and neuroimmune mediated sensitization. These heightened pain states alter physiology, reduce motor function, and affect motivation to engage in normal behaviors. The complexity of the pain state has evaded a comprehensive definition, especially in nonverbal animals. Here in mice, we capture the physiological state of sensitized neurons and use computational tools to automatically map behavioral signatures of evoked and spontaneous displays of pain at different time points for both an acute model of paw inflammatory pain and a chronic model of knee joint pain. First, retrograde labeling coupled with electrophysiology of neurons innervating injury sites revealed key time points corresponding to peripheral sensitivity in multiple pain models. Next, we used high-speed videography combined with supervised and unsupervised machine learning tools to uncover sensory-evoked defensive coping postures, unique to each pain condition. Using 3D pose analytics inspired by natural language processing, we identify movement sequences that correspond to robust representations of ongoing pain states. With this new analytical framework, we find that commonly used analgesics do not return an animal’s behavior to a pre-injury state. Instead, animals adopt a novel set of spontaneous behaviors that are maintained even after most evoked behavioral features of pain have resolved. Together, these findings reveal previously unidentified neuroethological signatures of pain and analgesia at timescales when inflammation induces heightened pain states and during recovery.
DOI: 10.5281/zenodo.7884190
title: Mapping the neuroethological signatures of pain, analgesia and recovery in mice

description: <strong>Mapping the neuroethological signatures of pain, analgesia and recovery in mice.</strong><br> Repository containing datasets obtained for M. Bohic, L Pattison, et al. 2023. Neuron. <strong>Abstract</strong><br> Ongoing pain is driven by direct activation of pain-sensing neurons and neuroimmune mediated sensitization. These heightened pain states alter physiology, reduce motor function, and affect motivation to engage in normal behaviors. The complexity of the pain state has evaded a comprehensive definition, especially in nonverbal animals. Here in mice, we capture the physiological state of sensitized neurons and use computational tools to automatically map behavioral signatures of evoked and spontaneous displays of pain at different time points for both an acute model of paw inflammatory pain and a chronic model of knee joint pain. First, retrograde labeling coupled with electrophysiology of neurons innervating injury sites revealed key time points corresponding to peripheral sensitivity in multiple pain models. Next, we used high-speed videography combined with supervised and unsupervised machine learning tools to uncover sensory-evoked defensive coping postures, unique to each pain condition. Using 3D pose analytics inspired by natural language processing, we identify movement sequences that correspond to robust representations of ongoing pain states. With this new analytical framework, we find that commonly used analgesics do not return an animal’s behavior to a pre-injury state. Instead, animals adopt a novel set of spontaneous behaviors that are maintained even after most evoked behavioral features of pain have resolved. Together, these findings reveal previously unidentified neuroethological signatures of pain and analgesia at timescales when inflammation induces heightened pain states and during recovery.
DOI: 10.5281/zenodo.8011420
title: Virtual Viewing Experiences (Application)

description: How to simulate hands-on media practices in a virtual environment using 3D-represented cultural heritage objects in such a way that the material characteristics of the objects and their usages become part of the virtual and immersive user experience? In this project, we have explored this question by focusing on two media historical objects: the Kinora motion picture viewer (ca. 1907) and the Pathé Baby 9.5mm film projector (ca. 1924) as early-twentieth-century technologies of home cinema. The application development was carried out in four stages: (1) digitization, (2) conceptualization, (3) contextualization, and (4) digital storytelling. The project was developed within the framework of a research internship at the Open University of the Netherlands in collaboration with Maastricht University, the Netherlands. It was conducted in support of the project CRAFTED: Enrich and promote traditional and contemporary crafts, co-financed by the Connecting Europe Facility of the European Commission. Video demonstration of the application: https://www.youtube.com/watch?v=8UnoPSDZCiw.
DOI: 10.5281/zenodo.8008800
title: GEO-Bench: m-cashew-plantation

description: <strong>GEO-Bench: m-cashew-plantation dataset</strong> This dataset has been modified to be included in the GEO-Bench dataset. All changes with respect to the original version are documented at https://github.com/ServiceNow/geo-bench. The original version of this dataset is due to Jin et al. (2021) and is available at: https://mlhub.earth/data/ts_cashew_benin. See the LICENSE file provided alongside this dataset for applicable licensing information.
DOI: 10.5281/zenodo.7998146
title: A long-lasting, distant journey of a male griffon vulture informs on the success of its mate solo parental investment

description: CSV dataset
DOI: 10.5281/zenodo.8008799
title: GEO-Bench: m-cashew-plantation

description: <strong>GEO-Bench: m-cashew-plantation dataset</strong> This dataset has been modified to be included in the GEO-Bench dataset. All changes with respect to the original version are documented at https://github.com/ServiceNow/geo-bench. The original version of this dataset is due to Jin et al. (2021) and is available at: https://mlhub.earth/data/ts_cashew_benin. See the LICENSE file provided alongside this dataset for applicable licensing information.
DOI: 10.5281/zenodo.8010942
title: IODP Expedition 369 Magnetic susceptibility (point or contact system)

description: Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
DOI: 10.5281/zenodo.8010941
title: IODP Expedition 369 Magnetic susceptibility (point or contact system)

description: Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
DOI: 10.5281/zenodo.8011573
title: The Impact of Climate Change on Extreme Winds over Northern Europe According to CMIP6: data and codes

description: Data and codes for the paper submitted to WES "The Impact of Climate Change on Extreme Winds over Northern Europe According to CMIP6" Data: 1. U50*.nc: 50-year wind with spectral correction method from 18 CMIP6 models 2. annual_max*.nc: annual wind maxima from 18 CMIP6 models 3. spe*.dat: example of power spectrum from the original wind speed time series from a CMIP6 model and power spectrum with spectral correction Codes: 1. AnnualMax*.py : calculate extreme winds from 18 CMIP6 models 2. XFuture-code-sharing.nb: Mathematca code for analysis of data on the effect of climate change, and pack data for plotting using MATLAB 3. *.m: MATLAB codes for plotting figures on climate change
DOI: 10.5281/zenodo.8011572
title: The Impact of Climate Change on Extreme Winds over Northern Europe According to CMIP6: data and codes

description: Data and codes for the paper submitted to WES "The Impact of Climate Change on Extreme Winds over Northern Europe According to CMIP6" Data: 1. U50*.nc: 50-year wind with spectral correction method from 18 CMIP6 models 2. annual_max*.nc: annual wind maxima from 18 CMIP6 models 3. spe*.dat: example of power spectrum from the original wind speed time series from a CMIP6 model and power spectrum with spectral correction Codes: 1. AnnualMax*.py : calculate extreme winds from 18 CMIP6 models 2. XFuture-code-sharing.nb: Mathematca code for analysis of data on the effect of climate change, and pack data for plotting using MATLAB 3. *.m: MATLAB codes for plotting figures on climate change
DOI: 10.5281/zenodo.7903331
title: TEAMx-PC22 (TEAMx pre-campaign 2022) – Vertical profiles and Multi-Point In-Situ Measurements at Nafingalm collected with the SWUF-3D UAS fleet

description: This dataset contains aggregated measurements from a fleet of multicopter UAS. The data was measured during the period 21 June 2022 through 27 June 2022 at Nafingalm, Austria with the SWUF-3D fleet. The data was collected in association to the TEAMx-PC22 field campaign. A maximum of three UAS were operated simultaneously. Processed level-2 data is provided. For level-2 data, time synchronization between individual UAS was done through interpolation, if multiple UAS are operated simultaneously. In this dataset vertical profiles (swuf3dvpro) between 10m and 120m above ground level and time series of UAS hovering for approx. 10 minutes at fixed positions (swuf3dhover) are provided with a temporal resolution of 1 Hz. The data are provided in NetCDF format with metadata and variable descriptions in the style of the SAMD Product standard: Jahnke-Bornemann, Annika. (2022, August 18). The SAMD Product Standard (Standardized Atmospheric Measurement Data) (Version 2.2). http://doi.org/10.25592/uhhfdm.10416
description: Problems with the fixation of the acceleration sensors caused higher vibrations in this campaign. Wind data should be used only with caution for turbulence estimation, especially for uav31 and uav32.
DOI: 10.5281/zenodo.7673443
title: TEAMx-PC22 (TEAMx pre-campaign 2022) – Vertical profiles and Multi-Point In-Situ Measurements at Nafingalm collected with the SWUF-3D UAS fleet

description: This dataset contains aggregated measurements from a fleet of multicopter UAS. The data was measured during the period 21 June 2022 through 27 June 2022 at Nafingalm, Austria with the SWUF-3D fleet. The data was collected in association to the TEAMx-PC22 field campaign. A maximum of three UAS were operated simultaneously. Processed level-2 data is provided. For level-2 data, time synchronization between individual UAS was done through interpolation, if multiple UAS are operated simultaneously. In this dataset vertical profiles (swuf3dvpro) between 10m and 120m above ground level and time series of UAS hovering for approx. 10 minutes at fixed positions (swuf3dhover) are provided with a temporal resolution of 1 Hz. The data are provided in NetCDF format with metadata and variable descriptions in the style of the SAMD Product standard: Jahnke-Bornemann, Annika. (2022, August 18). The SAMD Product Standard (Standardized Atmospheric Measurement Data) (Version 2.2). http://doi.org/10.25592/uhhfdm.10416
description: Problems with the fixation of the acceleration sensors caused higher vibrations in this campaign. Wind data should be used only with caution for turbulence estimation, especially for uav31 and uav32.
DOI: 10.5281/zenodo.8011634
title: The baobab trees density over sahel (500m)

description: 500m resolution map of baobab trees over arid and semi-arid region of West Africa belt. This is aggregated by a wall to wall map of baobab trees derived by deep learning model and very high resolution satellite imagery for 2010s.
DOI: 10.5281/zenodo.8011633
title: The baobab trees density over sahel (500m)

description: 500m resolution map of baobab trees over arid and semi-arid region of West Africa belt. This is aggregated by a wall to wall map of baobab trees derived by deep learning model and very high resolution satellite imagery for 2010s.
DOI: 10.5281/zenodo.7983007
title: jacobqs/MST-Himalaya: Data release

description: Release 1.0: Contains data used in the Master Thesis
DOI: 10.5281/zenodo.7049204
title: Supporting Evidence For OLAC and Serials: Pre-Conference Release

description: This release does not have the specific OLAC records for Journal, Article, Volume, Issue.
DOI: 10.5281/zenodo.6787610
title: Resilience Resources, Coping, and Health Outcomes in College Students during the Covid-19 Pandemic

description: The current study tests coping behaviors as mediators of associations between resilience resources and somatization, depression, and anxiety symptoms in undergraduates during the Covid-19 pandemic. Participants (n=193) completed an online survey assessing resilience resources, coping, and symptoms in late 2020. The data set includes variables tested in three parallel mediation models. RRS=Resilience Resources Scale copePR=Coping through Positive Reinterpretation and Growth copeES=Coping through Seeking Emotional Support copeSU=Coping through Substance Use phq = Somatic Symptoms (PHQ-15) dpsx =Depressive Symptoms (PROMIS) anxsx =Anxiety Symptoms (PROMIS)
DOI: 10.5281/zenodo.6787609
title: Resilience Resources, Coping, and Health Outcomes in College Students during the Covid-19 Pandemic

description: The current study tests coping behaviors as mediators of associations between resilience resources and somatization, depression, and anxiety symptoms in undergraduates during the Covid-19 pandemic. Participants (n=193) completed an online survey assessing resilience resources, coping, and symptoms in late 2020. The data set includes variables tested in three parallel mediation models. RRS=Resilience Resources Scale copePR=Coping through Positive Reinterpretation and Growth copeES=Coping through Seeking Emotional Support copeSU=Coping through Substance Use phq = Somatic Symptoms (PHQ-15) dpsx =Depressive Symptoms (PROMIS) anxsx =Anxiety Symptoms (PROMIS)
DOI: 10.5519/qd.k5u9hskv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (51 records)