cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2f002k
title: CCDC 2234327: Experimental Crystal Structure Determination

description: Related Article: Georgia A. Zissimou, Maria Manoli, Panayiotis A. Koutentis|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cyyy7
title: CCDC 2203540: Experimental Crystal Structure Determination

description: Related Article: Daniel G. Shlian, James Pelaez, David A. Vaccaro, Gerard Parkin|2022|J.Mol.Struct.|1276|134727|doi:10.1016/j.molstruc.2022.134727
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25452/figshare.plus.20443146
title: Multiphasic Value Biases: Raw Behaviour files and task code

description: Raw Behavioural data and task code from Corbett, Martinez-Rodriguez, Judd, O'Connell & Kelly (2022), Multiphasic Value Biases in Fast-Paced Decisions. <br> Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection and discrimination, but how value biases are exerted through these phases remains unknown. Here, 17 participants performed a motion direction discrimination task under high speed pressure, in which one alternative was more valuable than the other. By parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a “starting point”-advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup rate bias. This, in turn, was countered by a transient deflection toward the higher value action evoked by stimulus detection. A neurally-constrained process model featuring anticipatory urgency, biased detection, and accumulation of growing stimulus-discriminating evidence, successfully captured both behavior and motor preparation dynamics. Thus, an intricate interplay of distinct biasing mechanisms serves to prioritise time-constrained perceptual decisions. <br> Task code in RVBDots_final.m Raw behaviour files generated from task code. <br> Naming conventions: ParticipantRegimeSessionNoBlockNo.mat or Participant_RegimeBlockNo.mat <br> e.g. P1_H1.mat %P1, high coherence blocked, block 1 <br> or P3I31.mat %P3, interleaved, session 3, block 1 <br> Regimes: H: High coherence blocked. L: Low coherence blocked. I (or HL for P1): Interleaved. <br> Files with N in the filename before the block number are 'neutral' practice blocks with no value bias (not used in the analyses). <br> <br> <br>
DOI: 10.25452/figshare.plus.20443146.v1
title: Multiphasic Value Biases: Raw Behaviour files and task code

description: Raw Behavioural data and task code from Corbett, Martinez-Rodriguez, Judd, O'Connell & Kelly (2022), Multiphasic Value Biases in Fast-Paced Decisions. <br> Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection and discrimination, but how value biases are exerted through these phases remains unknown. Here, 17 participants performed a motion direction discrimination task under high speed pressure, in which one alternative was more valuable than the other. By parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a “starting point”-advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup rate bias. This, in turn, was countered by a transient deflection toward the higher value action evoked by stimulus detection. A neurally-constrained process model featuring anticipatory urgency, biased detection, and accumulation of growing stimulus-discriminating evidence, successfully captured both behavior and motor preparation dynamics. Thus, an intricate interplay of distinct biasing mechanisms serves to prioritise time-constrained perceptual decisions. <br> Task code in RVBDots_final.m Raw behaviour files generated from task code. <br> Naming conventions: ParticipantRegimeSessionNoBlockNo.mat or Participant_RegimeBlockNo.mat <br> e.g. P1_H1.mat %P1, high coherence blocked, block 1 <br> or P3I31.mat %P3, interleaved, session 3, block 1 <br> Regimes: H: High coherence blocked. L: Low coherence blocked. I (or HL for P1): Interleaved. <br> Files with N in the filename before the block number are 'neutral' practice blocks with no value bias (not used in the analyses). <br> <br> <br>
DOI: 10.25452/figshare.plus.20428227
title: Multiphasic Value Biases: Raw EEG Data

description: Raw EEG data from paper: <br> Corbett, Martinez-Rodriguez, Judd, O'Connell & Kelly (2022), Multiphasic Value Biases in Fast-Paced Decisions. <br> Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection and discrimination, but how value biases are exerted through these phases remains unknown. Here, 17 participants performed a motion direction discrimination task under high speed pressure, in which one alternative was more valuable than the other. By parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a “starting point”-advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup rate bias. This, in turn, was countered by a transient deflection toward the higher value action evoked by stimulus detection. <br> Raw 128 channel EEG (Biosemi BDF files). 8 external channels: 1- Above left eye 2- Below left eye 3- Left mastoid 4- Right mastoid 5- Left thumb EMG distal 6- Left thumb EMG proximal 7- Right thumb EMG distal 8- Right thumb EMG proximal <br> Naming convention: ParticipantRegimeBlockNo.bdf or Participant_RegimeBlockNo.bdf <br> e.g. P1_H1.bdf or P29L2.bdf <br> Regimes: H: High coherence blocked. L: Low coherence blocked. I (or HL for P1): Interleaved. <br> Files with N in the filename are 'neutral' practice blocks with no value bias (not used in the analyses). <br> Note P22 had 2 sessions for the H and L regimes. The second of these sessions has the naming format: <br> P22Regime3BlockNo.bdf, <br> where the '3' denotes session 3. <br> Task code (MATLAB/Psychtoolbox) available with the behavioural dataset. <br> Event trigger descriptions: <br> StartTask = 1 Fixation on = 4; Cue on = [8 9 7]; % [high-on-left, high-on-right, neutral] Dots on = [24 25]; % [dots left dots right] Response = [20 21]; % [left-button right-button]
DOI: 10.25452/figshare.plus.20428227.v1
title: Multiphasic Value Biases: Raw EEG Data

description: Raw EEG data from paper: <br> Corbett, Martinez-Rodriguez, Judd, O'Connell & Kelly (2022), Multiphasic Value Biases in Fast-Paced Decisions. <br> Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection and discrimination, but how value biases are exerted through these phases remains unknown. Here, 17 participants performed a motion direction discrimination task under high speed pressure, in which one alternative was more valuable than the other. By parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a “starting point”-advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup rate bias. This, in turn, was countered by a transient deflection toward the higher value action evoked by stimulus detection. <br> Raw 128 channel EEG (Biosemi BDF files). 8 external channels: 1- Above left eye 2- Below left eye 3- Left mastoid 4- Right mastoid 5- Left thumb EMG distal 6- Left thumb EMG proximal 7- Right thumb EMG distal 8- Right thumb EMG proximal <br> Naming convention: ParticipantRegimeBlockNo.bdf or Participant_RegimeBlockNo.bdf <br> e.g. P1_H1.bdf or P29L2.bdf <br> Regimes: H: High coherence blocked. L: Low coherence blocked. I (or HL for P1): Interleaved. <br> Files with N in the filename are 'neutral' practice blocks with no value bias (not used in the analyses). <br> Note P22 had 2 sessions for the H and L regimes. The second of these sessions has the naming format: <br> P22Regime3BlockNo.bdf, <br> where the '3' denotes session 3. <br> Task code (MATLAB/Psychtoolbox) available with the behavioural dataset. <br> Event trigger descriptions: <br> StartTask = 1 Fixation on = 4; Cue on = [8 9 7]; % [high-on-left, high-on-right, neutral] Dots on = [24 25]; % [dots left dots right] Response = [20 21]; % [left-button right-button]
DOI: 10.14457/nida.the.2017.82
title: วิเคราะห์หลักกฎหมาย ข้อยกเว้นการอ้างการกระทำด้วยความจำเป็น เปรียบเทียบกฎหมายไทย เยอรมัน และสหรัฐอเมริกา
DOI: 10.14457/nida.the.2009.153
title: อิทธิพลของวิธีปฏิบัติด้านทรัพยากรมนุษย์ต่อผลการปฏิบัติงานบุคลากรสายผู้สอนในสถาบันอุดมศึกษา
DOI: 10.5281/zenodo.3232935
title: Single Cell RNAseq of Mouse Testis

description: This is the processed data from the publication: "Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains" (https://doi.org/10.1101/393769) The raw data is avaliable at GEO: GSE113293 Associated software is at https://zenodo.org/badge/latestdoi/140632831 SDA_objects.zip contains key tables required for many functions, download this to use the shiny app. Contents: cell_data: data.table containing metadata of the cells. 80 columns including cell id, SDA component cell scores, Tsne coordinates, pseudotime, experimental group etc. data: a sparse matrix of normalised read counts (20322 cells by 19262 genes) gene_annotations: data.table containing gene locations, enrichment vs other tissues from bulk data, infertility gene status GO_enrich: data.table containing gene ontology enrichments for each component, the genes enriched, p.values and enrichment values principal_curves: list of output of princurve() containing the pseudotime trajetories SDAresults: output of SDA run loaded using SDAtools::load_results() Other R objects include: QC_count_matrix: Sparse matrix of raw count values for QC cells and genes cell_imputation_AUCs: data.table of PRAC AUC values for unimputed (train), mean cell, SDA (predict), ICA, PCA, NNMF, and MAGIC HocomocoV11_motifProbs_matrix: Matrix of regulation probabilities from MotifFinder using fixed motifs from HocomocoV11 database motifFinder_denovo: list of results of MotifFinder denovo motifs from promoters regions of genes from each component motifFinder_denovo_fixed: list of results of MotifFinder using 125 denovo motifs with fixed motif on all genes. tomtom_matched_motifs: data.table of TOMTOM matches of denovo motifs, plus metadata
description: R object files, to load in R use readRDS(), or the load2() function from the associated testisAtlas package.
description: {"references": ["https://doi.org/10.1101/393769"]}
DOI: 10.6084/m9.figshare.21834504
title: Recreation_survey_wales.xlsx

description: This dataset is about how people spen while they are going outdoor for recreation purposr. How they spend their money and time.
DOI: 10.6084/m9.figshare.21834504.v1
title: Recreation_survey_wales.xlsx

description: This dataset is about how people spen while they are going outdoor for recreation purposr. How they spend their money and time.
DOI: 10.6084/m9.figshare.21563766
title: Checklist of the coastal fishes of Lord Howe, Norfolk and Kermadec islands December 2022

description: The subtropical islands of the north Tasman Sea have a fascinating mix of tropical, subtropical and temperate marine fishes. Species diversity and the proportion of tropical species in the fauna decline from west to east (Lord Howe &gt; Norfolk &gt; Kermadec Islands) (Francis 1993, Francis & Duffy 2015). This checklist has been compiled as a resource for those wishing to understand and interact with the fish faunas of these islands, monitor biodiversity and coastal fish biogeography, and to assess changes in the faunas as a result of global climate change. <br> Coastal fishes are defined here as those species that occur within 50 m of the surface and 1 km of the shore, excluding oceanic pelagic species. Most species included in the checklist are reef fishes, but species living on or in sand, and in open water near the coast, are also included. Inclusion or exclusion of species is inevitably sometimes arbitrary, especially where information on the normal depth ranges of species is poor. <br> This checklist is updated and revised from earlier checklists of fishes from Lord Howe and Norfolk Islands (Francis 1993), Kermadec Islands (Francis & Duffy 2015) and a later complete checklist for all three islands (Francis 2019, 2022). Some species reported in the earlier lists have been omitted, mostly as a result of improved information on their depth ranges. Additions to the previous checklists and changed or verified specific names are noted, and the sources for these are documented. Generic and specific names follow those in Eschmeyer's 'Catalog of Fishes' (Fricke et al. 2022) unless a recent taxonomic revision has changed them. Species that range westward to mainland Australia and south-eastward to mainland New Zealand are shown in adjacent columns. <br> Users are invited to send me information and evidence (particularly images) of further new records for the islands' fish fauna. Also, please tell me about any problems with the content or access to the checklist. I will update this checklist periodically and give it a new version number to distinguish it from the previous versions. The d.o.i should point to the most recent version.
DOI: 10.5281/zenodo.7511256
title: PQG Numerical Tests Data

description: Files contain a collection of PQG data (*.mat) with different evaporation rates in the paper "Combining Stochastic Parameterized Reduced-Order Models with Machine Learning for Data Assimilation and Uncertainty Quantification with Partial Observations". Three data sets includes E=0.0.2, E=0.2, E=0.35.
DOI: 10.6084/m9.figshare.19695010
title: Supporting data of hillslope topography analyzed in the Gangdese Mountains

description: Logarithmic data of the horizontal hillslope length (<em>X</em>) and hilltop height (H<sub>T</sub>) of hillslope swaths analyzed in the Gangdese Mountaines. Blank line data indicates the ridge divide. Files are labeled in accordance with their marked locations.
DOI: 10.6084/m9.figshare.19695010.v1
title: Supporting data of hillslope topography analyzed in the Gangdese Mountains

description: Table S1 to S17 are natural hillslope profile data listing the primary data of hillslope profile, including the horizontal distance (X) and elevation (H) of each profile from the current site. Column headings are indicated by numbers of profiles prefixed by X and H, respectively. <br> Table S18 to S34 list logarithms of horizontal hillslope length (<em>L</em>) and hilltop height (HT) data of each site, which are used for linear fitting using least squares methods. Data at Hilltop line is marked by “-".
DOI: 10.6084/m9.figshare.19695010.v2
title: Supporting data of hillslope topography analyzed in the Gangdese Mountains

description: Logarithmic data of the horizontal hillslope length (<em>X</em>) and hilltop height (H<sub>T</sub>) of hillslope swaths analyzed in the Gangdese Mountaines. Blank line data indicates the ridge divide. Files are labeled in accordance with their marked locations.
DOI: 10.6084/m9.figshare.20545047
title: File 9

description: Metadata for 713 participants in the HFMD project study
DOI: 10.6084/m9.figshare.20545047.v8
title: A large scale 16S ribosomal RNA gene amplicon dataset of hand, foot and mouth patients and healthy individuals

description: The metadata and some analysis results of 16S rDNA sequencing data of HFMD patients and healthy individuals.
DOI: 10.6084/m9.figshare.21834975
title: Data for Figure 6_WRR

description: This dataset shows the various combination of parameters used to run 3D simulations and their corresponding 1D Damkohler numbers obtained after fitting with a 1D model. A linear power law relationship has been established between deposition rates and pore-scale parameters.
DOI: 10.6084/m9.figshare.21834975.v1
title: Data for Figure 6_WRR

description: This dataset shows the various combination of parameters used to run 3D simulations and their corresponding 1D Damkohler numbers obtained after fitting with a 1D model. A linear power law relationship has been established between deposition rates and pore-scale parameters.
DOI: 10.6084/m9.figshare.21834999
title: File 10

description: Metadata for 458 participants in the HFMD project study
DOI: 10.21227/4ne6-gs51
title: Optical constants of GSTS

description: (a) Refractive index and (b) extinction coefficient spectra of Ge2Sb2Te3S2 (GSTS) measured by spectroscopic ellipsometry in the wavelength range from 200 nm to 2500 nm.
DOI: 10.6084/m9.figshare.21835008
title: File 8

description: Species annotation information at genus level
DOI: 10.5281/zenodo.7511943
title: Dataset for "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory"

description: <pre>This dataset gathers all cross-spectral, spectral and bispectral data produced and used in the manuscript accepted for publication in Geophysical Research Letters: "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory", by K. Martins, P. Bonneton, O. de Viron, I. L. Turner, M. D. Harley and K. Splinter The sharing of the processed data is motivated by research reproductibility purposes and with the hope that it will foster efforts in improving the newly-proposed depth-inversion procedure. Three laboratory experiments are considered, namely the experiments reported in van Noorloos (2003), Michallet et al. (2011) and GLOBEX (e.g., see Ruessink et al., 2013). The processed data is organised in separate self-explanatory .mat files (generated with MATLAB software), gathering: - "cel_data" : wave phase velocities obtained from cross-spectral and cross-correlation analyses between adjacent wave gauges - "bulk_data" : range of bulk wave parameters computed across the different wave flumes - "bispectrum_data" : spectral and bispectral estimates computed across the different wave flumes The scripts for performing the depth-inversion as well as a for plotting the results are provided. The new Boussinesq depth-inversion procedure relies on the function "fun_compute_krms_terms.m", which is provided and originates from the bispectral analysis library accessible from the first author GitHub repository at https://github.com/ke-martins/bispectral-analysis. Acknowledgements: Kévin Martins greatly acknowledges the financial support from the European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement 887867 (lidBathy). We warmly thank Ap van Dongeren and Hervé Michallet for providing the raw data for the experiments described in van Noorloos (2003) and Michallet et al. (2011), respectively. The raw data from GLOBEX used in this research can be accessed on Zenodo at https://zenodo.org/record/4009405 and can be used under the Creative Commons Attribution 4.0 International license. The GLOBEX project was supported by the European Community’s Seventh Framework Programme through the Hydralab IV project, EC Contract 261520. References: Michallet, H., Cienfuegos, R., Barthélemy, E., & Grasso, F. (2011). Kinematics of waves propagating and breaking on a barred beach. European Journal of Mechanics - B/Fluids, 30 (6), 624 – 634. doi: 10.1016/j.euromechflu.2010.12.004 Ruessink, G. B., Michallet, H., Bonneton, P., Mouazé, D., Lara, J. L., Silva, P. A., & Wellens, P. (2013). GLOBEX: Wave dynamics on a gently sloping laboratory beach. In Coastal Dynamics ’13: Proceedings of the Seventh Conference on Coastal Dynamics, Arcachon, France. van Noorloos, J. C. (2003). Energy transfer between short wave groups and bound long waves on a plane slope (Master’s thesis, Delft University of Technology, Delft, The Netherlands). Retrieved from http://resolver.tudelft.nl/uuid:13616ff0-407d-43de-9954-ba707cd40d27</pre>