cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11950461.v1
title: Data for Modlmeier_et_al_2014_BES.xlsx

description: Data for Modlmeier et al. 2014 in Behavioral Ecology and Sociobiology. File includes raw data for T. curvispinosus repeatability in individual aggressiveness and the collective behavioral data used for analyses.<br>
DOI: 10.6084/m9.figshare.11950488.v1
title: path_similarity_v2.csv

description: Pair-wise pathway similarity scores targeted by of all small-molecule drugs in Drugbank database, empowered by PPI information
DOI: 10.6084/m9.figshare.11950488.v2
title: Pathway similarity scores

description: Pair-wise pathway similarity scores targeted by of all small-molecule drugs in Drugbank database, empowered by PPI information
DOI: 10.6084/m9.figshare.11950392
title: Development of an expert derived ICD-AIS map for serious AIS3+ injury identification

description: <b>Objective:</b> The objective of the mapping project was to develop an expert derived map between the International Statistical Classification of Diseases and Related Health Problems (ICD) clinical modifications (CM) and the Abbreviated Injury Scale (AIS) to be able to relate AIS severity to ICD coded data road traffic collision data in EU datasets. The maps were developed to enable the identification of serious AIS3+ injury and provide details of the mapping process for assumptions to be made about injury severity from mass datasets. This article describes in detail the mapping process of the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) and the International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10-CM) codes to the Abbreviated Injury Scale 2005, Update 2008 (AIS08) codes to identify injury with an AIS severity of 3 or more (AIS3+ severity) to determine ‘serious’ (MAIS3+) road traffic injuries. <b>Methods:</b> Over 19,000 ICD codes were mapped from the following injury categories; injury ICD-9-CM (Chapter 17) codes between ‘800 and 999.9’ and injury ICD-10-CM (Chapter 19) ‘S’ and ‘T’ prefixed codes were reviewed and mapped to an AIS08 category and then relate the severity to three groups; AIS3+, AIS <b>Results:</b> In total 2,504 ICD-9-CM codes were mapped to the AIS, of which 780 (31%) were assigned an AIS3+ severity. For the16,508 ICD-10-CM mapped codes a total of 2,323 (14%) were assigned an AIS3+ severity. Some 17% (n = 426) and 27% (n = 4,485) of ICD-9-CM and ICD-10-CM codes respectively were assigned to AIS9 (no-map) following the mapping process. It was evident there were ‘problem’ codes that could not be easily mapped to an AIS code to reflect severity. Problem maps affect the specificity of the map and severity when used to translate historical data in large datasets. <b>Conclusions:</b> The Association for the Advancement in Automotive Medicine, AAAM-endorsed expert-derived map offers a unique tool to road safety researchers to establish the number of MAIS3+ serious injuries occurring on the roads. The detailed process offered in this paper will enable researchers to understand the decision making and identify limitations when using the AIS08/ICD map on country-specific data. The results could inform protocols for dealing with problem codes to enable country comparisons of MAIS3+ serious injury rates.
DOI: 10.6084/m9.figshare.11950326
title: VRP instances.zip

description: this is a set of instances used in the ISA algorithm
DOI: 10.6084/m9.figshare.11950392.v1
title: Development of an expert derived ICD-AIS map for serious AIS3+ injury identification

description: <b>Objective:</b> The objective of the mapping project was to develop an expert derived map between the International Statistical Classification of Diseases and Related Health Problems (ICD) clinical modifications (CM) and the Abbreviated Injury Scale (AIS) to be able to relate AIS severity to ICD coded data road traffic collision data in EU datasets. The maps were developed to enable the identification of serious AIS3+ injury and provide details of the mapping process for assumptions to be made about injury severity from mass datasets. This article describes in detail the mapping process of the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) and the International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10-CM) codes to the Abbreviated Injury Scale 2005, Update 2008 (AIS08) codes to identify injury with an AIS severity of 3 or more (AIS3+ severity) to determine ‘serious’ (MAIS3+) road traffic injuries. <b>Methods:</b> Over 19,000 ICD codes were mapped from the following injury categories; injury ICD-9-CM (Chapter 17) codes between ‘800 and 999.9’ and injury ICD-10-CM (Chapter 19) ‘S’ and ‘T’ prefixed codes were reviewed and mapped to an AIS08 category and then relate the severity to three groups; AIS3+, AIS <b>Results:</b> In total 2,504 ICD-9-CM codes were mapped to the AIS, of which 780 (31%) were assigned an AIS3+ severity. For the16,508 ICD-10-CM mapped codes a total of 2,323 (14%) were assigned an AIS3+ severity. Some 17% (n = 426) and 27% (n = 4,485) of ICD-9-CM and ICD-10-CM codes respectively were assigned to AIS9 (no-map) following the mapping process. It was evident there were ‘problem’ codes that could not be easily mapped to an AIS code to reflect severity. Problem maps affect the specificity of the map and severity when used to translate historical data in large datasets. <b>Conclusions:</b> The Association for the Advancement in Automotive Medicine, AAAM-endorsed expert-derived map offers a unique tool to road safety researchers to establish the number of MAIS3+ serious injuries occurring on the roads. The detailed process offered in this paper will enable researchers to understand the decision making and identify limitations when using the AIS08/ICD map on country-specific data. The results could inform protocols for dealing with problem codes to enable country comparisons of MAIS3+ serious injury rates.
DOI: 10.5522/04/11950284
title: HIV cohort study

description: Dataset from HIV cohort study evaluating frequency of acute respiratory illness. <br>No identifiers. <br>Files with baseline data and frequency of acute respiratory illness, viral swab results during acute respiratory illness, patient reported symptom outcomes during acute respiratory illness.
DOI: 10.6084/m9.figshare.11950290
title: Map of current and projected ratio of peatland vs forest evapotranspiration

description: This dataset contains estimated ratios of afternoon evapotranspiration in peatlands and forests for the peak growing season (July). For the current climate, data from CRU TS version 4.03 was used and for climate change projections CMIP5 model simulations were used for the RCP4.5 and RCP8.5 scenarios. The Penman-Monteith equation was parameterised using eddy covariance flux data. For further details, please see Helbig et al., Nature Climate Change, 2020.
DOI: 10.6084/m9.figshare.11950290.v1
title: Map of current and projected ratio of peatland vs forest evapotranspiration

description: This dataset contains estimated ratios of afternoon evapotranspiration in peatlands and forests for the peak growing season (July). For the current climate, data from CRU TS version 4.03 was used and for climate change projections CMIP5 model simulations were used for the RCP4.5 and RCP8.5 scenarios. The Penman-Monteith equation was parameterised using eddy covariance flux data. For further details, please see Helbig et al., Nature Climate Change, 2020.
DOI: 10.6084/m9.figshare.11950236
title: Dryness index map and flux tower sites

description: This dataset contains a dryness index map of the boreal biome derived from CRU TS version 4.03 and information on flux tower sites used in Helbig et al., Nature Climate Change, 2020.
DOI: 10.6084/m9.figshare.11950236.v1
title: Dryness index map and flux tower sites

description: This dataset contains a dryness index map of the boreal biome derived from CRU TS version 4.03 and information on flux tower sites used in Helbig et al., Nature Climate Change, 2020.
DOI: 10.6084/m9.figshare.11950170
title: Projected changes in growing season vapour pressure deficit

description: This dataset contains projections of changes in vapour pressure deficit for the growing season (May to September). Mean ensemble projections are from CMIP5 simulations of eight Earth system models. For further details, see Helbig et al., Nature Climate Change, 2020.
DOI: 10.6084/m9.figshare.11950170.v1
title: Projected changes in growing season vapour pressure deficit

description: This dataset contains projections of changes in vapour pressure deficit for the growing season (May to September). Mean ensemble projections are from CMIP5 simulations of eight Earth system models. For further details, see Helbig et al., Nature Climate Change, 2020.
DOI: 10.25375/uct.11950107
title: Children, Technology and Play (CTAP) Survey

description: The School of Education at the University of Cape Town (UCT) investigated children’s learning through digital play. The aim of the study was to explore the intersection between child play, technology, creativity and learning among children aged between 3 and 11 years. The study also identified skills and dispositions children develop through both digital and non-digital play. The data shared emerged from a survey of parents of children in the stated age group, with particular reference to the parents views on children's play practices, including time parents spent playing with their children, concerns parents had on time children spend playing on various technologies, types of play children in South Africa engaged in and the concerns of parents when children played with some electronic devices. The following data files are shared:<br>SA - Survey - Children, Technology and Play (CTAP) - Google Forms.pdfDescriptive Stats 2020.1.9 -Children Technology and Play SURVEY.xlsxParent Survey RAW PUBLIC DATA 2020.2.29 - Children Technology and Play Project.xlsxParent Survey RAW PUBLIC DATA 2020.2.29 - Children Technology and Play Project.csvParent Survey REPORT DATA 2020.2.29 - Children Technology and Play Project.xlsxParent Survey REPORT DATA 2020.2.29 - Children Technology and Play Project.csvParent Survey RAW and REPORT DATA SYNTAX 2020.2.29 - Children Technology and Play Project.sps<br>NOTE: This survey was adapted from Marsh, J. Stjerne Thomsen, B., Parry, B., Scott, F. Bishop, J.C., Bannister, C., Driscoll, A., Margary, T., Woodgate, A., (2019) Children, Technology and Play. UK Survey Questions. LEGO Foundation.
DOI: 10.6084/m9.figshare.11949735
title: Eddy-induced heat flux in the South China Sea

description: The dataset are eddy-induced heat flux in the South China Sea during 2000-2015, which is available daily on 0.25° spatial resolution. The dataset includes following varaibles: lh and sh are latent and sensible heat, respectively. <i>t<sub>a</sub></i> and <i>q<sub>a</sub></i> are the air temperature and specific humidity at the reference height of 2 m above the sea surface. <i>ws </i>is the wind speed at the reference height of 10 m. <i>q<sub>s</sub></i> is the saturation humidity. sst is sea surface temperature.<br>All the above variables are calculated within mesoscale eddies in the South China Sea during 2000-2015.
DOI: 10.6084/m9.figshare.11949735.v2
title: Eddy-induced heat flux in the South China Sea

description: The dataset are eddy-induced heat flux in the South China Sea during 2000-2015, which is available daily on 0.25° spatial resolution. The dataset includes following varaibles: lh and sh are latent and sensible heat, respectively. <i>t<sub>a</sub></i> and <i>q<sub>a</sub></i> are the air temperature and specific humidity at the reference height of 2 m above the sea surface. <i>ws </i>is the wind speed at the reference height of 10 m. <i>q<sub>s</sub></i> is the saturation humidity. sst is sea surface temperature.<br>All the above variables are calculated within mesoscale eddies in the South China Sea during 2000-2015.
DOI: 10.6084/m9.figshare.11949690
title: Dynamic budget allocation for fertilizers and soil organic matter in a field experiment of semi-subsistence farmers in Zambia

description: This dataset is from a semi-computerized framed field experiment with semi-subsistence farmers in Zambia. The experiment investigates decision-making with conflicting production objectives in a dynamic setting. We ask whether semi-subsistence farmers allocate their budget to farm activities that immediately increase food production but compromise sustainable long-term production or whether the allocate their budget to farm activities that increase food production in the future and tolerate smaller harvests today.The experiments were conducted in August 2016 in 7 villages around Mumbwa, in Zambia’s Central province, where the main language is Tonga. Subjects were recruited from semi-subsistence farm communities. All subjects were real decision-makers on farms; however, they had no previous experience in related experiments. A total of 15 experiments were conducted that in total comprised 75 subjects. None of the subjects participated in more than one experiment.
DOI: 10.6084/m9.figshare.11949690.v1
title: Dynamic budget allocation for fertilizers and soil organic matter in a field experiment of semi-subsistence farmers in Zambia

description: This dataset is from a semi-computerized framed field experiment with semi-subsistence farmers in Zambia. The experiment investigates decision-making with conflicting production objectives in a dynamic setting. We ask whether semi-subsistence farmers allocate their budget to farm activities that immediately increase food production but compromise sustainable long-term production or whether the allocate their budget to farm activities that increase food production in the future and tolerate smaller harvests today.The experiments were conducted in August 2016 in 7 villages around Mumbwa, in Zambia’s Central province, where the main language is Tonga. Subjects were recruited from semi-subsistence farm communities. All subjects were real decision-makers on farms; however, they had no previous experience in related experiments. A total of 15 experiments were conducted that in total comprised 75 subjects. None of the subjects participated in more than one experiment.
DOI: 10.6084/m9.figshare.11949735.v1
title: Eddy-induced heat flux in the South China Sea

description: The dataset are eddy-induced heat flux in the South China Sea during 2000-2015, which is available daily on 0.25° spatial resolution. The dataset includes following varaibles: lh and sh are latent and sensible heat, respectively. <i>t<sub>a</sub></i> and <i>q<sub>a</sub></i> are the air temperature and specific humidity at the reference height of 2 m above the sea surface. <i>ws </i>is the wind speed at the reference height of 10 m. <i>q<sub>s</sub></i> is the saturation humidity. sst is sea surface temperature.<br>All the above varables are calculated within mesoscale eddies in the South China Sea during 2000-2015.
DOI: 10.6084/m9.figshare.11949633
title: Supplementary material.doc

description: 《<b>Differential mitochondrial proteomic analysis of A549 cells infected with avian influenza virus subtypes H5 and H9</b>》<b>补充材料(Table)</b>
DOI: 10.6084/m9.figshare.11949633.v1
title: Supplementary material.doc

description: 《<b>Differential mitochondrial proteomic analysis of A549 cells infected with avian influenza virus subtypes H5 and H9</b>》<b>补充材料(Table)</b>
DOI: 10.6084/m9.figshare.11949546
title: DeNoia_Telesca_Vendrami_etal.txt

description: Microsatellite data associated to the publication: "Population genetic structure is unrelated to shell shape, thickness and organic content in European populations of the soft-shell clam <i>Mya arenaria</i>". Data submitted in STRUCTURE format.<br>
DOI: 10.6084/m9.figshare.11949546.v1
title: DeNoia_Telesca_Vendrami_etal.txt

description: Microsatellite data associated to the publication: "Population genetic structure is unrelated to shell shape, thickness and organic content in European populations of the soft-shell clam <i>Mya arenaria</i>". Data submitted in STRUCTURE format.<br>
DOI: 10.6084/m9.figshare.11949408
title: Coral data from Kadmat

description: This datafile comprises stable isotopic ratios for <i>Porites</i> coral from Kadmat Island
DOI: 10.6084/m9.figshare.11949408.v1
title: Coral data from Kadmat

description: This datafile comprises stable isotopic ratios for <i>Porites</i> coral from Kadmat Island
DOI: 10.6084/m9.figshare.11949279.v1
title: covid19.zip

description: This database includes confirmed cases of COVID-19 in line list format. It spans the timeframe between 1 December 2019 to 5 February 2020.
DOI: 10.6084/m9.figshare.11949147
title: Mumbai Patna Endline data

description: Data contains TB care pathways of patients after Public-Private interface Agency (PPIA) intervention in Mumbai and Patna
DOI: 10.6084/m9.figshare.11949279.v2
title: covid19.zip

description: This database includes confirmed cases of COVID-19 in line list format. It spans the timeframe between 1 December 2019 to 5 February 2020.
DOI: 10.6084/m9.figshare.11949279.v4
title: Epidemiological data from the COVID-19 outbreak, real-time case information

description: This database includes confirmed cases of COVID-19 in line list format. It spans the timeframe between 1 December 2019 to 5 February 2020.
DOI: 10.6084/m9.figshare.11949117
title: Mumbai Patna Baseline data

description: Data contains TB care pathways of patients before Public-Private interface Agency (PPIA) intervention in Mumbai and Patna<br>
DOI: 10.6084/m9.figshare.11949078
title: Social stress in female Columbian ground squirrels: density-independent effects of kin contribute to variation in fecal glucocorticoid metabolites

description: Fecal cortisol metabolites and life history information on female Columbian ground squirrels. Data from publication Sosa, Dobson, Bordier, Neuhaus, Saraux, Bosson, Palme, Boonstra & Viblanc. 2020. Behavioral Ecology and Sociobiology.
DOI: 10.6084/m9.figshare.11949078.v1
title: Social stress in female Columbian ground squirrels: density-independent effects of kin contribute to variation in fecal glucocorticoid metabolites

description: Fecal cortisol metabolites and life history information on female Columbian ground squirrels. Data from publication Sosa, Dobson, Bordier, Neuhaus, Saraux, Bosson, Palme, Boonstra & Viblanc. 2020. Behavioral Ecology and Sociobiology.
DOI: 10.5522/04/11949063
title: Data and code for Figures for the article “Real-time 3D movement correction for two-photon imaging in behaving animals”

description: Code and data to generate figures found in Griffiths et al, 2020 "Real-time 3D movement correction for two-photon imaging in behaving animals". Simply download all the files and add them to you matlab path. You can then run the script corresponding to the figures of interest. Scripts are named plot_figure_#.m and will load data from the subfolder with the same name. More information is available in each script. For figures that used patch or volume imaging, the data provided usually corresponds to extracted data. When computing the performance of our Real Time 3D Movement Correction (RT-3DMC), we often use a metric called UDE (Uncorrected Displacement Error), which corresponds to an independent measurement of the remaining movement after RT-3DMC, as estimated by a subpixel registration algorithm [1]. The UDE values indicate how much residual movement is present (in the absence or presence of RT-3DMC) when the animal is behaving (running/licking/swimming).
DOI: 10.6084/m9.figshare.11949048
title: Gene-RD-provenance_CTL_linkset

description: In this dataset provides a CyTargetlinker (a Cytoscape app) compatible linkset to link monogenic, rare diseases (OMIM identifier) with a known genetic cause (HGNC, Ensembl) supplemented with manually extracted provenance of both the disease and the discovery of the underlying genetic cause of the disease (PMID).
DOI: 10.6084/m9.figshare.11949042
title: Target similarity

description: Pair-wise drug-target similarity based on the protein sequence scores among all small-molecule drugs in Drugbank database
DOI: 10.6084/m9.figshare.11949042.v1
title: Target similarity

description: Pair-wise drug-target similarity based on the protein sequence scores among all small-molecule drugs in Drugbank database
DOI: 10.6084/m9.figshare.11948910
title: Chemical similarity scores among all drug-pairs

description: Pair-wise chemical similarity scores among all small-molecule drugs in Drugbank database
DOI: 10.6084/m9.figshare.11948916
title: Metadata record for: Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework

description: This dataset contains key characteristics about the data described in the Data Descriptor Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.11948904
title: DrugSimDB

description: This is the main DrugSimDB file containing drug-pair information with significant combined similarity measures.
DOI: 10.6084/m9.figshare.11948916.v1
title: Metadata record for: Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework

description: This dataset contains key characteristics about the data described in the Data Descriptor Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.11948901
title: drugBank_Sider_mapping.csv

description: This file maps Drug names from the Drugbank database with their adverse side-effects annotated in the SIDER database
DOI: 10.6084/m9.figshare.11948901.v1
title: drugBank_Sider_mapping.csv

description: This file maps Drug names from the Drugbank database with their adverse side-effects annotated in the SIDER database
DOI: 10.6084/m9.figshare.11948910.v1
title: Chemical similarity scores among all drug-pairs

description: Pair-wise chemical similarity scores among all small-molecule drugs in Drugbank database
DOI: 10.6084/m9.figshare.11948904.v1
title: DrugSimDB

description: This is the main DrugSimDB file containing drug-pair information with significant combined similarity measures.
DOI: 10.6084/m9.figshare.11948904.v2
title: DrugSimDB

description: This is the main DrugSimDB file containing drug-pair information with significant combined similarity measures.
DOI: 10.6084/m9.figshare.11948904.v3
title: DrugSimDB

description: This is the main DrugSimDB file containing drug-pair information with significant combined similarity measures.
DOI: 10.6084/m9.figshare.11948904.v4
title: DrugSimDB

description: This is the main DrugSimDB file containing drug-pair information with significant combined similarity measures.
DOI: 10.6084/m9.figshare.11948904.v5
title: DrugSimDB

description: This is the main DrugSimDB file containing drug-pair information with significant combined similarity measures.
DOI: 10.6084/m9.figshare.11948904.v6
title: DrugSimDB

description: This is the main DrugSimDB file containing drug-pair information with significant combined similarity measures.