cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3796362.v2
title: Example mu1hat trajectories for two participants

description: Example mu1hat trajectories are provided for two example participants:1. Participant 2 from the Placebo group2. Participant 16 from the ACh- group.<br>Data are 1200 x 4 x 4, where trajectories across 1200 trials are provided for each of the 16 possible transitions between consecutive stimuli (arranged in a 4x4 matrix).
DOI: 10.6084/m9.figshare.3796356
title: HGF Example Trajectories and Response Model Betas for Placebo Participants

description: Example HGF parameter trajectories across 1200 trials for Placebo Participant 2.Trajectories of delta_1 (x.da1traj), epsilon_3 (x.e3traj) and mu_3 (x.mu3traj) across 1200 trials are provided for:1. the truly occurring transitions2. all of the 16 possible transitions (in a 4x4 matrix) <br>x.PlaceboBetas contains Response Model Betas for the Placebo group. Data are participant x beta, where:Column 1 = beta1Column 2 = beta2Column 3 = beta3Column 4 = beta4<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses of the Placebo Response Model Betas.
DOI: 10.6084/m9.figshare.3796356.v2
title: HGF Example Trajectories and Response Model Betas for Placebo Participants

description: Example HGF parameter trajectories across 1200 trials for Placebo Participant 2.Trajectories of delta_1 (x.da1traj), epsilon_3 (x.e3traj) and mu_3 (x.mu3traj) across 1200 trials are provided for:1. the truly occurring transitions2. all of the 16 possible transitions (in a 4x4 matrix) <br>x.PlaceboBetas contains Response Model Betas for the Placebo group. Data are participant x beta, where:Column 1 = beta1Column 2 = beta2Column 3 = beta3Column 4 = beta4<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses of the Placebo Response Model Betas.
DOI: 10.6084/m9.figshare.3796356.v1
title: HGF Example Trajectories and Placebo Betas

description: Example HGF parameter trajectories across 1200 trials for Placebo Participant 2.Trajectories of delta_1 (x.da1traj), epsilon_3 (x.e3traj) and mu_3 (x.mu3traj) across 1200 trials are provided for:1. the truly occurring transitions2. all of the 16 possible transitions (in a 4x4 matrix) <br>x.PlaceboBetas contains Response Model Betas for the Placebo group. Data are participant x beta, where:Column 1 = beta1Column 2 = beta2Column 3 = beta3Column 4 = beta4<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses of the Placebo Response Model Betas.
DOI: 10.6084/m9.figshare.3796353
title: Model-agnostic analyses of logRT data

description: Model-agnostic analyses of logRT data. Data are participant x true transition probability.<br>For x.AllCorrectTrials, x.LateEarlyDifference and x.LateEarlyDifference_millisecond:Column 1 = high probability transition (p=0.70 or p=0.85)Column 2 = mid probability transition (p=0.20 or p=0.25)Column 3 = low probability transition (p=0.05)NaN = missing data<br>x.DrugID gives the row-wise drug identifier for each participant, where:1 = Placebo2 = DA-3 = ACh-4 = NA-<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses.
DOI: 10.6084/m9.figshare.3796353.v2
title: Model-agnostic analyses of logRT data

description: Model-agnostic analyses of logRT data. Data are participant x true transition probability.<br>For x.AllCorrectTrials, x.LateEarlyDifference and x.LateEarlyDifference_millisecond:Column 1 = high probability transition (p=0.70 or p=0.85)Column 2 = mid probability transition (p=0.20 or p=0.25)Column 3 = low probability transition (p=0.05)NaN = missing data<br>x.DrugID gives the row-wise drug identifier for each participant, where:1 = Placebo2 = DA-3 = ACh-4 = NA-<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses.
DOI: 10.6084/m9.figshare.3796353.v1
title: Model agnostic logRT analyses

description: Model-agnostic analyses of logRT data. Data are participant x true transition probability.<br>For x.AllCorrectTrials, x.LateEarlyDifference and x.LateEarlyDifference_millisecond:Column 1 = high probability transition (p=0.70 or p=0.85)Column 2 = mid probability transition (p=0.20 or p=0.25)Column 3 = low probability transition (p=0.05)NaN = missing data<br>x.DrugID gives the row-wise drug identifier for each participant, where:1 = Placebo2 = DA-3 = ACh-4 = NA-<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses.
DOI: 10.6084/m9.figshare.3796314
title: Reaction Times under different transition matrices

description: Mean logRT data for 124 participants under three different types of transition matrix (TM):1. x.FirstOrderTMs2. x.AlternatingTMs3. x.ZeroOrderTMs<br>These data are participant x transition probability, where:Column 1 = high probability transition (p=0.7 or p=0.85)Column 2 = mid probability transition (p=0.2 or p=0.25)Column 3 = low probability transition (p=0.05)NaN = missing data<br>x.DrugID gives the row-wise drug identifier for each participant, where:1 = Placebo2 = DA-3 = ACh-4 = NA-<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses.<br>x.FirstOrderTM2_SixteenTransitions_Placebo provides mean logRT data for each Placebo participant and for each of the 16 possible transitions. Data are participant x transition.<br>
DOI: 10.6084/m9.figshare.3796314.v2
title: Reaction Times under different transition matrices

description: Mean logRT data for 124 participants under three different types of transition matrix (TM):1. x.FirstOrderTMs2. x.AlternatingTMs3. x.ZeroOrderTMs<br>These data are participant x transition probability, where:Column 1 = high probability transition (p=0.7 or p=0.85)Column 2 = mid probability transition (p=0.2 or p=0.25)Column 3 = low probability transition (p=0.05)NaN = missing data<br>x.DrugID gives the row-wise drug identifier for each participant, where:1 = Placebo2 = DA-3 = ACh-4 = NA-<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses.<br>x.FirstOrderTM2_SixteenTransitions_Placebo provides mean logRT data for each Placebo participant and for each of the 16 possible transitions. Data are participant x transition.<br>
DOI: 10.6084/m9.figshare.3796314.v1
title: RTs under different transition matrices

description: Mean logRT data for 124 participants under three different types of transition matrix (TM):1. x.FirstOrderTMs2. x.AlternatingTMs3. x.ZeroOrderTMs<br>These data are participant x transition probability, where:Column 1 = high probability transition (p=0.7 or p=0.85)Column 2 = mid probability transition (p=0.2 or p=0.25)Column 3 = low probability transition (p=0.05)NaN = missing data<br>x.DrugID gives the row-wise drug identifier for each participant, where:1 = Placebo2 = DA-3 = ACh-4 = NA-<br>x.DeltaAlertness and x.BodyWeight were used as covariates in the analyses.<br>x.FirstOrderTM2_SixteenTransitions_Placebo provides mean logRT data for each Placebo participant and for each of the 16 possible transitions. Data are participant x transition.<br>
DOI: 10.6084/m9.figshare.3796305
title: Data asset framework (DAF) survey results 2016

description: This fileset includes a guidance document with the full DAF question set, and the csv dataset arising from six Data Asset Framework (DAF) surveys. These were completed in July and August 2016 by a total of 1,185 researchers at the following UK higher education institutions: » The University of Cambridge » Lancaster University » Plymouth University » The Royal College of Music (RCM) » University of St Andrews » Eight members of the Consortium for Research Excellence, Support and Training (CREST)<sup><sup>[1]</sup></sup> The surveys were developed and administered by Research Consulting, as part of a consultation phase to inform the Jisc Research Data Shared Service project. The anonymised dataset is being released in full to allow re-use by others. Full details on the survey methodology and an analysis of the results are available at: https://doi.org/10.5281/zenodo.177856<br>A toolkit providing guidance to higher education institutions (HEIs) on the implementation of a Data Asset Framework (DAF) survey is availabe here: <br>https://doi.org/10.5281/zenodo.177876 <br> <br> [1] Buckinghamshire New University, University of Chichester, Glyndwr University, Harper Adams University, Leeds College of Art, Newman University, St Mary’s University, Twickenham and York St John University
DOI: 10.6084/m9.figshare.3796305.v3
title: Data asset framework (DAF) survey results 2016

description: This fileset includes a guidance document with the full DAF question set, and the csv dataset arising from six Data Asset Framework (DAF) surveys. These were completed in July and August 2016 by a total of 1,185 researchers at the following UK higher education institutions: » The University of Cambridge » Lancaster University » Plymouth University » The Royal College of Music (RCM) » University of St Andrews » Eight members of the Consortium for Research Excellence, Support and Training (CREST)<sup><sup>[1]</sup></sup> The surveys were developed and administered by Research Consulting, as part of a consultation phase to inform the Jisc Research Data Shared Service project. The anonymised dataset is being released in full to allow re-use by others. Full details on the survey methodology and an analysis of the results will be published by Jisc later in 2017. <br> [1] Buckinghamshire New University, University of Chichester, Glyndwr University, Harper Adams University, Leeds College of Art, Newman University, St Mary’s University, Twickenham and York St John University
DOI: 10.6084/m9.figshare.3796302
title: SparkText_SampleDataset_29437Fulltexts.zip

description: This is a sample dataset that includes 29,437 full text articles for testing SparkText (SparkText: biomedical text mining on big data framework).<br>
DOI: 10.6084/m9.figshare.3796302.v1
title: New draft item

description: This is a sample dataset that includes 29,437 full text articles for testing SparkText (SparkText: biomedical text mining on big data framework).<br>
DOI: 10.6084/m9.figshare.3796290
title: SparkText_SampleDataset_19681Abstracts.zip

description: This is a sample dataset that includes 19,681 abstracts for testing SparkText (SparkText: biomedical text mining on big data framework).<br>
DOI: 10.6084/m9.figshare.3796290.v1
title: SparkText_SampleDataset_19681Abstracts.zip

description: This is a sample dataset that includes 19,681 abstracts for testing SparkText (SparkText: biomedical text mining on big data framework).<br>
DOI: 10.6084/m9.figshare.3796209
title: SPAD state-of-the-art PDP and DCR comparison

description: The data set shows the state-of-the-art comparison of SPAD PDP and DCR, presented in "A Substrate Isolated CMOS SPAD Enabling Wide Spectral Response and Low Electrical Crosstalk", C. Veerappan and E. Charbon. By using this data, one is obliged to cite the paper.<br><br>The data is saved in MATLAB figure format, and all points can be extracted.<br><br>
DOI: 10.6084/m9.figshare.3796209.v1
title: SPAD state-of-the-art PDP and DCR comparison

description: The data set shows the state-of-the-art comparison of SPAD PDP and DCR, presented in "A Substrate Isolated CMOS SPAD Enabling Wide Spectral Response and Low Electrical Crosstalk", C. Veerappan and E. Charbon. By using this data, one is obliged to cite the paper.<br><br>The data is saved in MATLAB figure format, and all points can be extracted.<br><br>
DOI: 10.6084/m9.figshare.3796206
title: SlideShare presentations, citations, users and trends - paper data and documents

description: Queries, results and content analysis information from the paper: Thelwall, M. & Kousha, K. (in press). SlideShare presentations, citations, users and trends: A professional site with academic and educational uses.<em> Journal of the Association for Information Science and Technology</em>.<br>The file SlideShare5000randomFromEachYear.txt contains 5000 random SlideShare presentation URLs from each year.<br>The file SlideShare10Sept2015queries.xlsx contains the results for the single Bing query: "Published on Sep 10, 2015" site:slideshare.net<br>The file Content_analysis_task_-_slideshare.zip contains the documents and results of the content analysis classification.
DOI: 10.6084/m9.figshare.3796206.v2
title: SlideShare presentations, citations, users and trends - paper data and documents

description: Queries, results and content analysis information from the paper: Thelwall, M. & Kousha, K. (in press). SlideShare presentations, citations, users and trends: A professional site with academic and educational uses.<em> Journal of the Association for Information Science and Technology</em>.<br>The file SlideShare5000randomFromEachYear.txt contains 5000 random SlideShare presentation URLs from each year.<br>The file SlideShare10Sept2015queries.xlsx contains the results for the single Bing query: "Published on Sep 10, 2015" site:slideshare.net<br>The file Content_analysis_task_-_slideshare.zip contains the documents and results of the content analysis classification.
DOI: 10.6084/m9.figshare.3796206.v1
title: SlideSharePaperExtraFiles.zip

description: Queries, results and content analysis information from the paper: Thelwall, M. & Kousha, K. (in press). SlideShare presentations, citations, users and trends: A professional site with academic and educational uses.<em> Journal of the Association for Information Science and Technology</em>.<br>The file SlideShare5000randomFromEachYear.txt contains 5000 random SlideShare presentation URLs from each year.<br>The file SlideShare10Sept2015queries.xlsx contains the results for the single Bing query: "Published on Sep 10, 2015" site:slideshare.net<br>The file Content_analysis_task_-_slideshare.zip contains the documents and results of the content analysis classification.
DOI: 10.6084/m9.figshare.3796092
title: Solid stress and elastic energy as measures of tumour mechanopathology

description: Dataset of the figures in paper published by Nature Biomedical Engineering, entitled "Solid stress and elastic energy as measures of tumour mechanopathology."<br><br><br>
DOI: 10.6084/m9.figshare.3796092.v1
title: data_nBME_solid_stress_v1.xlsx

description: Dataset of the figures in paper published by Nature Biomedical Engineering, entitled "Solid stress and elastic energy as measures of tumour mechanopathology."<br><br><br>
DOI: 10.6084/m9.figshare.3796092.v2
title: data_nBME_solid_stress_v1

description: Dataset of the figures in paper published by Nature Biomedical Engineering, entitled "Solid stress and elastic energy as measures of tumour mechanopathology."<br><br><br>
DOI: 10.6084/m9.figshare.3795966
title: Code for MRes in Bioengineering

description: This code has been used in the MRes in Bioengineering project at Imperial College London. The project was about machine learning for decoding speech processing in the brain through otoacoustic emissions (OAEs). All the data are generated by using Matlab environment. The codes include DPOAE legacy, linear and non-linear support vector machine and the data of OAEs.<br>This code, especially related to the experiment, is not in any case in a distributed version!<br>For further info, kindly contact r.aldian15@imperial.ac.uk
DOI: 10.6084/m9.figshare.3795966.v2
title: Code for MRes in Bioengineering

description: This code has been used in the MRes in Bioengineering project at Imperial College London. The project was about machine learning for decoding speech processing in the brain through otoacoustic emissions (OAEs). All the data are generated by using Matlab environment. The codes include DPOAE legacy, linear and non-linear support vector machine and the data of OAEs.<br>This code, especially related to the experiment, is not in any case in a distributed version!<br>For further info, kindly contact r.aldian15@imperial.ac.uk
DOI: 10.6084/m9.figshare.3795966.v1
title: Code for MRes in Bioengineering

description: This code has been used in the MRes in Bioengineering project at Imperial College London. The project was about machine learning for decoding speech processing in the brain through otoacoustic emissions (OAEs). All the data are generated by using Matlab environment. The codes include DPOAE legacy, linear and non-linear support vector machine and the data of OAEs.<br>This code, especially related to the experiment, is not in any case in a distributed version!<br>For further info, kindly contact r.aldian15@imperial.ac.uk
DOI: 10.6084/m9.figshare.3795954
title: Tip Enhanced Raman Scattering in bilayer MoS2

description: Tip Enhanced Raman Scattering in bilayer MoS<sub>2</sub>The poster was presented in Graphene Week 2016, 13-17 June 2016, Warsaw Poland
DOI: 10.6084/m9.figshare.3795918
title: Polymer/ graphene "pastry" for flexible touch screens

description: The poster was presented in Graphene 2016, April 19-22, Genova Italy
DOI: 10.6084/m9.figshare.3795918.v1
title: Polymer/ graphene "pastry" for flexible touch screens

description: The poster was presented in Graphene 2016, April 19-22, Genova Italy
DOI: 10.6084/m9.figshare.3795888
title: Results summary for Shape Optimization with Surface-Mapped CPPNs

description: Raw data presented in: Richards, D. & Amos, M. (2016) Shape Optimization with Surface-Mapped CPPNs. Accepted for Publication in IEEE Transactions on Evolutionary Computation. <br>Data shows results of two experiments. The first experiment uses ten runs of 160 generations using surface-mapped CPPNs with NEAT. The second experiment presents 20 runs of 200 generations over 8 different parameterisations using surface-mapped CPPNs with NEAT.
DOI: 10.6084/m9.figshare.3795888.v1
title: Results summary for Shape Optimization with Surface-Mapped CPPNs

description: Raw data presented in: Richards, D. & Amos, M. (2016) Shape Optimization with Surface-Mapped CPPNs. Accepted for Publication in IEEE Transactions on Evolutionary Computation. <br>Data shows results of two experiments. The first experiment uses ten runs of 160 generations using surface-mapped CPPNs with NEAT. The second experiment presents 20 runs of 200 generations over 8 different parameterisations using surface-mapped CPPNs with NEAT.
DOI: 10.6084/m9.figshare.3795840
title: Study1 Data

description: Two CSV files<br>1. Coding of all images2. Coding specific to narrative images
DOI: 10.6084/m9.figshare.3795840.v2
title: Study1 Data

description: Two CSV files<br>1. Coding of all images2. Coding specific to narrative images
DOI: 10.6084/m9.figshare.3795840.v1
title: PhD Data: Study 1 (Chapter 5)

description: Two CSV files<br>1. Coding of all images2. Coding specific to narrative images
DOI: 10.6084/m9.figshare.3795834
title: figshare

description: Alignments, Maximum Likelihood tree, Bayesian Tree, Beast analyses, Bayesian Analysis of Macroevolutionary Mixtures (BAMM) for 97 sequences of the 18S rRNA from marine diatoms.
DOI: 10.6084/m9.figshare.3795834.v1
title: figshare

description: Alignments, Maximum Likelihood tree, Bayesian Tree, Beast analyses, Bayesian Analysis of Macroevolutionary Mixtures (BAMM) for 97 sequences of the 18S rRNA from marine diatoms.
DOI: 10.6084/m9.figshare.3795825
title: Predicting hotspots of human-elephant conflict to inform mitigation strategies in Xishuangbanna, southwest China.csv

description: This dataset contains human elephant conflict data collected by authors of a paper published in PLosOne, titled "Predicting hotspots of human-elephant conflict to inform mitigation strategies in Xishuangbanna, southwest China".These data were collected in cooperation with the following four institutions: the Local Forest Administration, the Nature Reserves, Community Committees and the insurance company “Yunnan branch, China Pacific property insurance company”. The data includes information on the characteristic of the households affected by conflicts with Asian elephants, and number of affected events per household during year 2008 and 2012. Please refer to this published paper for more infomration on data collection and analysis.
DOI: 10.6084/m9.figshare.3795825.v1
title: AE_conflict data in Yunnan_PlosONE paper.csv

description: This dataset contains human elephant conflict data collected by authors of a paper published in PLosOne, titled "Predicting hotspots of human-elephant conflict to inform mitigation strategies in Xishuangbanna, southwest China".These data were collected in cooperation with the following four institutions: the Local Forest Administration, the Nature Reserves, Community Committees and the insurance company “Yunnan branch, China Pacific property insurance company”. The data includes information on the characteristic of the households affected by conflicts with Asian elephants, and number of affected events per household during year 2008 and 2012. Please refer to this published paper for more infomration on data collection and analysis.
DOI: 10.6084/m9.figshare.3795816.v1
title: New draft item

description: In order to gain a clearer understanding of community-driven standardization efforts and how NIH might best<br>relate to them, BD2K held a workshop on Frameworks for Community-based Standards Efforts in<br>September 2013. This report - authored by the lead organizers and co-chairs of this workshop - elaborate on the findings,<br>indicating directions that would help move forward the BD2K vision to allow data and other digital<br>research products to interact and work with each other.
DOI: 10.4225/03/57c69fa1213ee
title: Global warming, poverty, and hunger – do we care about the global health issues of sustainability? [ Manuscript]

description: It is widely accepted in the scientific community, the international political community, and among the global “policy wonks” that issues of sustainability, including global warming, poverty and hunger are the major concerns for the 21 st Century. To translate those concerns into remedial action requires a broad base of popular support. There is survey evidence of significant popular concern; whether answering “yes” to a survey question translates into something more meaningful is harder to gauge. An alternative measure of commitment, or a genuine appreciation of the scale of the problem might be reflected in information seeking behaviour: on line searches. A review of global, google search trend data from January 2004 to February 2016 shows declining interest in major issues of global sustainability, and persistent and possibly growing fascination with celebrities, entertainment, and consumer goods. While not definitive, the trends should give the global health community and policy makers pause.<br><br>There are three files:<br>1. The manuscript as a PDF<br>2. The data set as an RData file, with each set of trends as a separate dataframe<br>3. The reproduction of the plot (100dpi)<br><br>
DOI: 10.6084/m9.figshare.3795564
title: Dataset for the paper titled "The statistical determinants of the speed of motor learning"

description: This is the raw data for a 2016 PLoS Computational Biology paper authored by Kang He, You Liang, Mona Fisher Bittmann, Farnaz Abdollah, Konrad Kording and Kunlin Wei.
DOI: 10.4225/03/57c62531c1751
title: Data for: "Helping outgroup climate change refugees: The role of ingroup directed anger"

description: Ingroup-directed anger data. This data is made available in aggregate format only, in accordance with the ethics approval under which it was collected.