cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5255/ukda-sn-855941
title: Assessing Financial Vulnerability and Risk in the UK’s Charities During and Beyond the Covid Crisis, 2020-2022

description: We advise that users familiarise themselves with the reporting requirements of the regulators on whose data we have drawn for this work. Some variables are easily understood (headline income and expenditure figures, or dates of registration and dissolution); others less so (e.g. familiarity with definitions of the components of income which charities are required to report would be desirable for work on the exposure of charities to specific income sources). We carried out work on various aspects of the financial vulnerability of charities and charitable companies, as follows: 1. patterns of registration and dissolution, as measured by the dates on which these events are recorded by the regulators. 2. the extent to which organisations held reserves prior to the onset of Covid-19. We used measures of "unrestricted reserves" which are usually provided only for larger organisations and expressed these as a proportion of the organisation's annual expenditures; 3. financial vulnerability, expressed in various ways - substantial (over 25%) fluctuations in incomes, or fluctuations in the excess of expenditure over income; 4. exposure of organisations to particular income streams. We define these in "VariableDescriptions_covid19_project.doc", attached to this deposit. Note that for time series analyses, the Charity Commission website data on the incomes and expenditures of charities only contains data for relatively recent time periods; a longer time series providing charity financials from the late 1990s to 2012 is available in the Third Sector Research Centre data collection at https://reshare.ukdataservice.ac.uk/850933/ and we recommend this is linked to the current data from the Charity Commission. Financial histories are not available for as long a time period for Scottish charities since the regulator was not established until 2006. Other data of relevance to work on this project would be a publicly-available classification of charities at https://charityclassification.org.uk/ Charitable organisations largely fall into a small number of sections of the Standard Industrial Classification and as a result scholars have developed more granular schemas. the data at the above website are publicly-available and can be linked via charity ID numbers. Project papers describing the work in more detail are available at https://www.birmingham.ac.uk/research/tsrc/research/assessing-financial-vulnerability-and-risk-in-the-uks-charities-during-and-beyond-the-covid-19-crisis.aspx
DOI: 10.18430/m3.irrmc.6349
title: X-ray diffraction data for the UCSF-P0068 project
DOI: 10.5281/zenodo.7038427
title: Dataset: Quantification of post-glacier bedrock surface erosion in the European Alps using 10Be and optically stimulated luminescence exposure dating

description: This contains the dataset associated with the publication titled "Quantification of post-glacier bedrock surface erosion in the European Alps using 10Be and optically stimulated luminescence exposure dating" published in Earth Surface Dynamics (2022).
DOI: 10.5281/zenodo.7038426
title: Dataset: Quantification of post-glacier bedrock surface erosion in the European Alps using 10Be and optically stimulated luminescence exposure dating

description: This contains the dataset associated with the publication titled "Quantification of post-glacier bedrock surface erosion in the European Alps using 10Be and optically stimulated luminescence exposure dating" published in Earth Surface Dynamics (2022).
DOI: 10.6084/m9.figshare.20751904
title: NCBI sequence data for teleost membrane-bound carbonic anhydrase phylogeny

description: Publicly available nucleotide sequences from NCBI were used to create a phylogenetic tree of teleost membrane-bound carbonic anhydrase 4 and 15s. NCBI accession numbers for the 438 sequences used and the detailed names for each sequence used in the final tree can be found on the first tab. Subcohort and cohort information associated with species of each sequence, and color codes used in the final tree can be found on the second tab.
DOI: 10.6084/m9.figshare.20751904.v1
title: NCBI sequence data for teleost membrane-bound carbonic anhydrase phylogeny

description: Publicly available nucleotide sequences from NCBI were used to create a phylogenetic tree of teleost membrane-bound carbonic anhydrase 4 and 15s. NCBI accession numbers for the 438 sequences used and the detailed names for each sequence used in the final tree can be found on the first tab. Subcohort and cohort information associated with species of each sequence, and color codes used in the final tree can be found on the second tab.
DOI: 10.18430/m3.irrmc.6350
title: X-ray diffraction data for the UCSF-P0070 project
DOI: 10.18430/m3.irrmc.6382
title: X-ray diffraction data for the UCSF-P0067 project
DOI: 10.18430/m3.irrmc.6351
title: X-ray diffraction data for the UCSF-P0071 project
DOI: 10.18430/m3.irrmc.6352
title: X-ray diffraction data for the UCSF-P0072 project
DOI: 10.18430/m3.irrmc.6353
title: X-ray diffraction data for the UCSF-P0073 project
DOI: 10.18430/m3.irrmc.6354
title: X-ray diffraction data for the UCSF-P0074 project
DOI: 10.18430/m3.irrmc.6355
title: X-ray diffraction data for the UCSF-P0075 project
DOI: 10.18430/m3.irrmc.6356
title: X-ray diffraction data for the UCSF-P0076 project
DOI: 10.18430/m3.irrmc.6357
title: X-ray diffraction data for the UCSF-P0077 project
DOI: 10.18430/m3.irrmc.6358
title: X-ray diffraction data for the UCSF-P0078 project
DOI: 10.18430/m3.irrmc.6359
title: X-ray diffraction data for the UCSF-P0079 project
DOI: 10.18430/m3.irrmc.6360
title: X-ray diffraction data for the UCSF-P0080 project
DOI: 10.18430/m3.irrmc.6361
title: X-ray diffraction data for the UCSF-P0083 project
DOI: 10.18430/m3.irrmc.6362
title: X-ray diffraction data for the UCSF-P0084 project
DOI: 10.18430/m3.irrmc.6363
title: X-ray diffraction data for the UCSF-P0085 project
DOI: 10.18430/m3.irrmc.6364
title: X-ray diffraction data for the UCSF-P0086 project
DOI: 10.18430/m3.irrmc.6365
title: X-ray diffraction data for the UCSF-P0087 project
DOI: 10.18430/m3.irrmc.6366
title: X-ray diffraction data for the UCSF-P0088 project
DOI: 10.18430/m3.irrmc.6367
title: X-ray diffraction data for the UCSF-P0091 project
DOI: 10.18430/m3.irrmc.6368
title: X-ray diffraction data for the UCSF-P0092 project
DOI: 10.18430/m3.irrmc.6369
title: X-ray diffraction data for the UCSF-P0096 project
DOI: 10.18430/m3.irrmc.6370
title: X-ray diffraction data for the UCSF-P0097 project
DOI: 10.18430/m3.irrmc.6371
title: X-ray diffraction data for the UCSF-P0098 project
DOI: 10.18430/m3.irrmc.6372
title: X-ray diffraction data for the UCSF-P0099 project
DOI: 10.18430/m3.irrmc.6373
title: X-ray diffraction data for the UCSF-P0100 project
DOI: 10.18430/m3.irrmc.6374
title: X-ray diffraction data for the UCSF-P0101 project
DOI: 10.18430/m3.irrmc.6375
title: X-ray diffraction data for the UCSF-P0102 project
DOI: 10.18430/m3.irrmc.6376
title: X-ray diffraction data for the UCSF-P0103 project
DOI: 10.18430/m3.irrmc.6377
title: X-ray diffraction data for the UCSF-P0107 project
DOI: 10.18430/m3.irrmc.6378
title: X-ray diffraction data for the UCSF-P0108 project
DOI: 10.18430/m3.irrmc.6379
title: X-ray diffraction data for the UCSF-P0110 project
DOI: 10.18430/m3.irrmc.6380
title: X-ray diffraction data for the UCSF-P0111 project
DOI: 10.18430/m3.irrmc.6381
title: X-ray diffraction data for the UCSF-P0113 project
DOI: 10.34740/kaggle/dsv/3935528
title: ASVP-ESD(Speech & Non-Speech Emotional Sound)
DOI: 10.48420/20230944.v1
title: Figure 3 and Figure S2 source data and processing

description: Source data for Figure 3 and Figure S2 as Unicode Origin projects and comma-separated data files.
DOI: 10.48420/20230944
title: Figure 3 and Figure S2 source data and processing

description: Source data for Figure 3 and Figure S2 as Unicode Origin projects and comma-separated data files.
DOI: 10.5281/zenodo.6539427
title: Dataset for manuscript Precursor-free eruption triggered by edifice rupture at Nyiragongo volcano

description: This archive file contains datafiles used in "Precursor-free eruption triggered by edifice rupture at Nyiragongo volcano" manuscript. i.e. : Ash Index, Lava flow shp, fractures shp, eruptive fissure shp, GNSS, InSAR, Seismic catalog, SO2 measurmement and Dike Modeling inputs and results.
DOI: 10.5281/zenodo.6539426
title: Dataset for manuscript Precursor-free eruption triggered by edifice rupture at Nyiragongo volcano

description: This archive file contains datafiles used in "Precursor-free eruption triggered by edifice rupture at Nyiragongo volcano" manuscript. i.e. : Ash Index, Lava flow shp, fractures shp, eruptive fissure shp, GNSS, InSAR, Seismic catalog, SO2 measurmement and Dike Modeling inputs and results.
DOI: 10.5281/zenodo.6977369
title: Data_set_C2SMART_Project

description: This dataset can be used to train and test the algorithms proposed in the project.
DOI: 10.5281/zenodo.6977370
title: Data_set_C2SMART_Project

description: This dataset can be used to train and test the algorithms proposed in the project.
DOI: 10.5281/zenodo.6780290
title: Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 1 Video

description: This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file. Note that this data set is split into two because of the size of the videos. This is the video data from day 1 of 2 of data collection.
DOI: 10.5281/zenodo.6780291
title: Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 1 Video

description: This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file. Note that this data set is split into two because of the size of the videos. This is the video data from day 1 of 2 of data collection.
DOI: 10.5281/zenodo.6780101
title: Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 2 Video

description: This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file. Note that this data set is split into two because of the size of the videos. This is the video data from day 2 of 2 of data collection.
DOI: 10.5281/zenodo.6780102
title: Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 2 Video

description: This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file. Note that this data set is split into two because of the size of the videos. This is the video data from day 2 of 2 of data collection.
DOI: 10.5281/zenodo.6779856
title: Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: LIDAR Point Cloud of a Portion of UTEP Campus

description: This Autodesk ReCap file is a combination of numerous individual LiDAR scans captured using a Leica Terrestial LiDAR system. The scan includes some black and white and some color scans. The area of campus generally focuses on the southwestern portion of campus including the Interdisciplinary Research Building, the Mining Minds roundabout, the Sun Bowl 2 Parking Lot, the University Bookstore, and the Sun Bowl Parking Garage, and roads including University Ave. and Sun Bowl Drive.
DOI: 10.5281/zenodo.6779857
title: Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: LIDAR Point Cloud of a Portion of UTEP Campus

description: This Autodesk ReCap file is a combination of numerous individual LiDAR scans captured using a Leica Terrestial LiDAR system. The scan includes some black and white and some color scans. The area of campus generally focuses on the southwestern portion of campus including the Interdisciplinary Research Building, the Mining Minds roundabout, the Sun Bowl 2 Parking Lot, the University Bookstore, and the Sun Bowl Parking Garage, and roads including University Ave. and Sun Bowl Drive.
DOI: 10.5281/zenodo.6746690
title: Survey data collected during VR user studies for Work Zone Safety III

description: This dataset documents survey responses of participants in VR user testing of Worker Safety III project experiments on an integrated traffic simulation and VR platform. Each column header of the spreadsheet shows the survey questions and Likert scale meanings where applicable. Each row of the spreadsheet represents a single user/participant's survey response after they experienced a specific work zone safety Scenario in virtual reality (VR): Scenario 1: Placing cones in a mobile work zone Scenario 2: Sensor installation on highway Scenario 3: Surveying in urban intersections Each user was assigned a unique four digit ID number. Some users, but not all, participated in multiple Scenarios. For requesting the data and further questions, please contact Dr. Semiha Ergan at semiha@nyu.edu
description: The report is funded, partially or entirely by the C2SMART Center, with a grant from the U.S. Department of Transportation's University Transportation Centers Program under Grant Number 69A3551747124. However, the U.S. Government assumes no liability for the contents or use thereof.
DOI: 10.5281/zenodo.6746689
title: Survey data collected during VR user studies for Work Zone Safety III

description: This dataset documents survey responses of participants in VR user testing of Worker Safety III project experiments on an integrated traffic simulation and VR platform. Each column header of the spreadsheet shows the survey questions and Likert scale meanings where applicable. Each row of the spreadsheet represents a single user/participant's survey response after they experienced a specific work zone safety Scenario in virtual reality (VR): Scenario 1: Placing cones in a mobile work zone Scenario 2: Sensor installation on highway Scenario 3: Surveying in urban intersections Each user was assigned a unique four digit ID number. Some users, but not all, participated in multiple Scenarios. For requesting the data and further questions, please contact Dr. Semiha Ergan at semiha@nyu.edu
description: The report is funded, partially or entirely by the C2SMART Center, with a grant from the U.S. Department of Transportation's University Transportation Centers Program under Grant Number 69A3551747124. However, the U.S. Government assumes no liability for the contents or use thereof.
DOI: 10.48420/20230962
title: Figure 4 source data

description: Source data for energies shown in Figure 4 given as both a Microsoft Excel XML file and as a Unicode Origin Project.
DOI: 10.48420/20230962.v1
title: Figure 4 source data

description: Source data for energies shown in Figure 4 given as both a Microsoft Excel XML file and as a Unicode Origin Project.
DOI: 10.48420/20230980
title: Figure 5 and Figure S5 source data and processing

description: Data files for Figure 5 and Figure S5 given as PDFs and as a CSV file. <br> Figure S5. Protein mass spectra for QueF and its adducts with substrate and co-factors. a) relative abundance of dimers in sample of just QueF, b) source spectrum of only QueF, c) source spectrum of the preQ<sub>0</sub> adduct, d) source spectrum of the preQ<sub>0</sub> + NADPH adduct, e) source spectrum of the BnCN adduct, f) source spectrum of the BnCN + NADPH adduct.
DOI: 10.48420/20230980.v1
title: Figure 5 and Figure S5 source data and processing

description: Data files for Figure 5 and Figure S5 given as PDFs and as a CSV file. <br> Figure S5. Protein mass spectra for QueF and its adducts with substrate and co-factors. a) relative abundance of dimers in sample of just QueF, b) source spectrum of only QueF, c) source spectrum of the preQ<sub>0</sub> adduct, d) source spectrum of the preQ<sub>0</sub> + NADPH adduct, e) source spectrum of the BnCN adduct, f) source spectrum of the BnCN + NADPH adduct.
DOI: 10.13012/b2idb-5586647_v2
title: Data for Refining the role of nitrogen mineralization in mycorrhizal nutrient syndromes

description: This dataset includes data on soil properties, soil N pools, and soil N fluxes presented in the manuscript, "Refining the role of nitrogen mineralization in mycorrhizal nutrient syndromes". Please refer to that publication for details about methodologies used to generate these data and for the experimental design. For this verison 2, we added specific gross nitrogen mineralization rates (ugN/gOM/d), microbial biomass carbon (ugC/gdw), microbial biomass nitrogen (ugN/gdw) and microbial biomass C:N ratios to the newest version of the data set. Additionally, we updated values for gross nitrogen mineralization, microbial NO3 assimilation and microbial NH4 assimilation to reflect slight changes in data processing. Those changes are reflected in "220829_All data_repository.csv". "220829_nitrogen_mineralization_readme.txt " is updated readme for the new file. The other 2 files begin with “220426_” are older version and same as in V1.
DOI: 10.6084/m9.figshare.20752444
title: Data_Final

description: All data associated with the manuscript titled: Experimental microbiome manipulation indicates that liverwort microbiomes may foster plant resistance to dehydration stress
DOI: 10.6084/m9.figshare.20752444.v2
title: Data_Final

description: All data associated with the manuscript titled: Experimental microbiome manipulation indicates that liverwort microbiomes may foster plant resistance to dehydration stress
DOI: 10.6084/m9.figshare.20752444.v1
title: Data_Final

description: All the data associated with the manuscript titled: Experimental microbiome manipulation indicates that liverwort microbiomes may foster plant resistance to dehydration stress
DOI: 10.6084/m9.figshare.16567509
title: Data & code for - Hybridization may promote variation in cognitive phenotypes in experimental guppy hybrids

description: To test if hybridization can promote phenotypic variation in cognitive abilities, we compare the performance of guppies (Poecilia reticulata), Endler’s guppies (Poecilia wingei), and their experimental hybrids in a colour association and reversal learning tasks.In addition to analysing this data using standard cognition metrics (e.g. trials to learning criterion), we introduce a new approach to analyse and compare multidimensional cognitive phenotypes. We used Kernel Density Estimation (KDE) together with a geometric approach to quantify patterns of hybrid phenotypes and quantitatively compare phenotypic dispersion between hybrids and parentals and quantify the extent to which hybrids are transgressive and/or deviate from parental mean phenotypes. <br>This depository contains the datasheet that identifies the crossing group of each fish, the raw data for the associative learning and reversal learning tasks, and a processed data table containing information on the performance of the fish in both tasks (learning criterion). We also provide the R code used for all analyses and to generate the graphs presented in the manuscript.
DOI: 10.6084/m9.figshare.16567509.v1
title: Data & code for - Hybridization may promote variation in cognitive phenotypes in experimental guppy hybrids

description: To test if hybridization can promote phenotypic variation in cognitive abilities, we compare the performance of guppies (Poecilia reticulata), Endler’s guppies (Poecilia wingei), and their experimental hybrids in a colour association and reversal learning tasks.In addition to analysing this data using standard cognition metrics (e.g. trials to learning criterion), we introduce a new approach to analyse and compare multidimensional cognitive phenotypes. We used Kernel Density Estimation (KDE) together with a geometric approach to quantify patterns of hybrid phenotypes and quantitatively compare phenotypic dispersion between hybrids and parentals and quantify the extent to which hybrids are transgressive and/or deviate from parental mean phenotypes. <br>This depository contains the datasheet that identifies the crossing group of each fish, the raw data for the associative learning and reversal learning tasks, and a processed data table containing information on the performance of the fish in both tasks (learning criterion). We also provide the R code used for all analyses and to generate the graphs presented in the manuscript.