cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/chwoiviiaoazdc-uhfffaoysa-l/chmo0002541
title: high-resolution electrospray ionisation time-of-flight mass spectrometry (HR-ESI-TOF-MS) ()
title: InChI=1S/C20H26N6.2ClH.Fe/c1-23-11-12-24(2)19(23)21-16-9-5-7-15-8-6-10-17(18(15)16)22-20-25(3)13-14-26(20)4;;;/h5-10H,11-14H2,1-4H3;2*1H;/q;;;+2/p-2

description: dataset for high-resolution electrospray ionisation time-of-flight mass spectrometry (HR-ESI-TOF-MS) HR-MS (ESI+, MeCN), m/z (%): isotopic distribution calculated for [C20H26Cl2FeN6]+ [M]+: 474.0987 (6) [C20H2635Cl254FeN6]+, 475.1020 (1) [C1913CH2635Cl254FeN6]+, 476.0940 (100) [C20H2635Cl2FeN6]+, 477.0973 (22) [C1913CH2635Cl2FeN6]+, 478.0910 (64) [C20H2635Cl37ClFeN6]+, 479.0944 (14) [C1913CH2635Cl37ClFeN6]+, 480.0881 (10) [C20H2637Cl2FeN6]+, 481.0914 (2) [C1913CH2637Cl2FeN6]+; found: 474.0975 (2), 476.0936 (100), 477.0964 (21), 478.0912 (68), 479.0933 (13), 480.0890 (9).
DOI: 10.14272/ybcyhbwfsbwzba-uhfffaoysa-l.1
title: [Fe(TMGpy)2Cl]Cl
title: InChI=1S/2C11H18N4.2ClH.Fe/c2*1-14(2)11(15(3)4)13-9-10-7-5-6-8-12-10;;;/h2*5-8H,9H2,1-4H3;2*1H;/q;;;;+2/p-2

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367]. The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]: InChI descriptor[cheminf_000113]: InChI=1S/2C11H18N4.2ClH.Fe/c2*1-14(2)11(15(3)4)13-9-10-7-5-6-8-12-10;;;/h2*5-8H,9H2,1-4H3;2*1H;/q;;;;+2/p-2, and canonical SMILES descriptor[cheminf_000007]: CN(C(=NCc1ccccn1)N(C)C)C.CN(C(=NCc1ccccn1)N(C)C)C.Cl[Fe+].[Cl-], and by the IUPAC name[cheminf_000107]: . The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]: The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-35297 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]: Melting point descriptor[CHEMINF_000256]: Boiling point descriptor[CHEMINF_000257]: Refractive index descriptor[CHEMINF_000253]: The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]: CHMO:0000763 | attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) CHMO:0001075 | elemental analysis (EA) CHMO:0000159 | single crystal X-ray diffraction (single-crystal X-ray diffraction) CHMO:0000473 | atmospheric pressure chemical ionisation mass spectrometry (APCI MS) The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: Used ontologies: CHEBI - Chemical Entities of Biological Interest CHEMINF - chemical information ontology (information entities about chemical entities) CHMO - Chemical Methods Ontology OBI - Ontology for Biomedical Investigations
DOI: 10.14272/ybcyhbwfsbwzba-uhfffaoysa-l/chmo0000159
title: single crystal X-ray diffraction (single-crystal X-ray diffraction) ()
title: InChI=1S/2C11H18N4.2ClH.Fe/c2*1-14(2)11(15(3)4)13-9-10-7-5-6-8-12-10;;;/h2*5-8H,9H2,1-4H3;2*1H;/q;;;;+2/p-2

description: dataset for single crystal X-ray diffraction (single-crystal X-ray diffraction)
DOI: 10.14272/ybcyhbwfsbwzba-uhfffaoysa-l/chmo0001075
title: elemental analysis (EA) ()
title: InChI=1S/2C11H18N4.2ClH.Fe/c2*1-14(2)11(15(3)4)13-9-10-7-5-6-8-12-10;;;/h2*5-8H,9H2,1-4H3;2*1H;/q;;;;+2/p-2

description: dataset for elemental analysis (EA) EA (C22H36Cl2FeN8): Calculated C 48.99%; H 6.73%; N 20.78%. Found C 48.73%; H 6.66%; N 21.16%.
DOI: 10.14272/ybcyhbwfsbwzba-uhfffaoysa-l/chmo0000763
title: attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) ()
title: InChI=1S/2C11H18N4.2ClH.Fe/c2*1-14(2)11(15(3)4)13-9-10-7-5-6-8-12-10;;;/h2*5-8H,9H2,1-4H3;2*1H;/q;;;;+2/p-2

description: dataset for attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) IR (ATR, ṽ) = 3016 (vw), 2983 (vw), 2947 (vw), 2890 (vw), 2874 (vw), 2832 (vw), 1602 (w), 1577 (w), 1570 (m), 1536 (vs), 1477 (m), 1463 (w), 1453 (w), 1437 (m), 1427 (m), 1421 (w), 1408 (w), 1393 (vs), 1370 (m), 1291 (w), 1257 (vw), 1234 (w), 1158 (s), 1147 (w), 1111 (vw), 1096 (vw), 1069 (w), 1063 (w), 1056 (w), 1019 (w), 1012 (w), 1009 (w), 981 (vw), 914 (w), 896 (vw), 856 (vw), 846 (vw), 802 (m), 777 (vs), 744 (vw), 727 (w), 643 (w), 618 (w), 579 (w), 576 (w), 553 (vw), 486 (vw) cm–1.
DOI: 10.14272/ybcyhbwfsbwzba-uhfffaoysa-l/chmo0000473
title: atmospheric pressure chemical ionisation mass spectrometry (APCI MS) ()
title: InChI=1S/2C11H18N4.2ClH.Fe/c2*1-14(2)11(15(3)4)13-9-10-7-5-6-8-12-10;;;/h2*5-8H,9H2,1-4H3;2*1H;/q;;;;+2/p-2

description: dataset for atmospheric pressure chemical ionisation mass spectrometry (APCI MS) MS (APCI+, MeOH), m/z (%): isotopic distribution calculated for [C22H36Cl2FeN8]+ [M]+: 536.1831 (6) [C22H3635Cl254FeN8]+, 537.1864 (2) [C2113CH3635Cl254FeN8]+, 538.1784 (100) [C22H3635Cl2FeN8]+, 539.1818 (24) [C2113CH3635Cl2FeN8]+, 540.1754 (64) [C22H3635Cl37ClFeN8]+, 541.1788 (15) [C2113CH3635Cl37ClFeN8]+, 542.1725 (10) [C22H3637Cl2FeN8]+, 543.1758 (2) [C2113CH3637Cl2FeN8]+, 544.1792 (&lt;1) [C2013C2H3637Cl2FeN8]+; found: 538.1787 (100), 539.1831 (8), 540.1761 (47).
DOI: 10.25829/r7n9-mh52
title: Tree inventory in MyDiv plots in 2021

description: Tree inventory data can be used to calculate forest productivity. This dataset is the individual tree measurements in 2021 in all 80 MyDiv plots.
DOI: 10.5281/zenodo.8339094
title: Dataset from 'Klever, L., Islam, J., Võ, M., & Billino, J. (2023). Aging attenuates the memory advantage for unexpected objects in real-world scenes. Heliyon, e20241, doi: 10.1016/j.heliyon.2023.e20241

description: We provide two files; one data file contains data on which analyses are based, the other one gives the column labels for the data file. ___________________________________________<br> For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de
description: This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 222641018 – SFB/TRR 135 TP B5 / C7
DOI: 10.5281/zenodo.8339093
title: Dataset from 'Klever, L., Islam, J., Võ, M., & Billino, J. (2023). Aging attenuates the memory advantage for unexpected objects in real-world scenes. Heliyon, e20241, doi: 10.1016/j.heliyon.2023.e20241

description: We provide two files; one data file contains data on which analyses are based, the other one gives the column labels for the data file. ___________________________________________<br> For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de
description: This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 222641018 – SFB/TRR 135 TP B5 / C7
DOI: 10.25829/20wz-dc69
title: Tree inventory in MyDiv plots in 2015

description: Tree inventory data can be used to calculate forest productivity. This dataset is the individual tree measurements (including tree basal diameter, DBH, height, characteristics, tree status etc.) in 2015 in all 80 MyDiv plots.
DOI: 10.6084/m9.figshare.24155052
title: <b>The first high-quality chromosome-level genome assembly of the silver pomfret (</b><b><i>Pampus argenteus</i></b><b>)</b>

description: Files of chromosome assembly, genome annotation and software settings for the manuscript entitled "The first high-quality chromosome-level genome assembly of the silver pomfret (Pampus argenteus)".
DOI: 10.6084/m9.figshare.24155160
title: Supplementary Material for: Trends in accuracy and appropriateness of alopecia areata information obtained from a popular online large language model, ChatGPT

description: Patients with alopecia areata (AA) may access a wide range of sources for information about AA, including the recently developed ChatGPT. Assessing the quality of health information provided by these sources is crucial, as patients are utilizing them in increasing numbers. This study aimed to evaluate appropriateness and accuracy of responses to common patient questions about AA generated by ChatGPT. Responses generated by ChatGPT 3.5 and ChatGPT 4.0 to 25 questions addressing common patient concerns were assessed by multiple attending dermatologists in an academic center for appropriateness and accuracy. Appropriateness of responses by both models for use in two hypothetical contexts: 1) for patient-facing general information websites, and 2) for electronic health record (EHR) message drafts were measured. This study found the accuracy across all responses was 4.41 out of 5. Accuracy scores of responses ChatGPT 3.5 responses had a mean of 4.29, whereas those generated by ChatGPT 4.0 had mean accuracy score of 4.53. Assessments ranged from 100% of responses rated as appropriate for the general question category to 79% questions about management for an EHR message draft. Raters largely preferred responses generated by ChatGPT 4.0 vs. ChatGPT 3.5. Reviewer agreement was found to be moderate across all questions, with a 53.7% agreement and Fleiss’ κ co-efficient of 0.522 (p-value &lt; 0.001). The large language model ChatGPT outputted mostly appropriate information for common patient concerns. While not all responses were accurate, the trend toward improvement with newer iterations suggests potential future utility for patients and dermatologists.
DOI: 10.6084/m9.figshare.24155082
title: Supplementary file 1 A-F.xlsx

description: Supplementary information in tabular format
DOI: 10.5258/soton/d1933
title: Data in support of the publication 'What medical students think about measurement of their well-being: cross-sectional survey and qualitative interviews'

description: Data from a cross-sectional survey (n=118) of Medical students in all Medicine programmes at the University of Southampton undertaken in 2021, with data on why, when, how, what and by whom wellbeing should be measured.
DOI: 10.6084/m9.figshare.24147462.v1
title: synthetic3clusters_rippleData

description: Simulated data of sharp-wave ripple using a tri-modal frequency distribution
DOI: 10.6084/m9.figshare.24147462
title: synthetic3clusters_rippleData

description: Simulated data of sharp-wave ripple using a tri-modal frequency distribution
DOI: 10.6084/m9.figshare.24147450
title: syntheticNoisyUniform_rippleData

description: Simulated data of sharp-wave ripples using a uniform distribution of frequencies and adding noise to the waveform
DOI: 10.6084/m9.figshare.24147450.v1
title: syntheticNoisyUniform_rippleData

description: Simulated data of sharp-wave ripples using a uniform distribution of frequencies and adding noise to the waveform
DOI: 10.6084/m9.figshare.24147423
title: syntheticUniform_rippleData

description: Simulated data of sharp-wave ripples using a uniform distribution of frequencies
DOI: 10.6084/m9.figshare.24147414
title: freeelyMoving_rippleData

description: freely moving mice ripple data along with features
DOI: 10.6084/m9.figshare.24147396
title: headFixed_rippleData

description: head fixed mice SWR dataset along computed features
DOI: 10.6084/m9.figshare.20589597.v2
title: Using Artificial Intelligence (AI)? Risk and Opportunity Perception of AI Predict People’s Willingness to Use AI

description: Dataset study1 and study2
DOI: 10.34740/kaggle/dsv/6492282
title: AutoXGB
DOI: 10.34740/kaggle/dsv/6492283
title: OnlineFree
DOI: 10.18112/openneuro.ds004078.v1.1.1
title: A synchronized multimodal neuroimaging dataset to study brain language processing
DOI: 10.5258/soton/d2781
title: Dataset in support of the Southampton doctoral thesis 'The use of non-violent resistance interventions for challenging behaviours exhibited by young people'

description: This dataset contains: Collected data - this data set is the responses of of 40 participants who completed four different measures (The Brief Parental Self-efficacy scale (BPSES; Woolgar, 2013); The Parental Stress Scale (PSS; Berry & Jones, 1995); The Parental Strengths and Difficulties Questionnaires (pSDQ; Goodman, 1997); The Parenting Scale (PS; Arnold et al., 1993). The data is collected as part of routine procedure during a parent group intervention. Participants were males and females with children who exhibited challenging behaviours. The data is stored on Windows Excel and was analysed in SPSS for Mac version 29.0.0 (IBM Corp., 2022). Ethical approval was granted for the use of the data for the purpose of this thesis (ERGO: 77381.A1).
DOI: 10.14459/2023mp1720279
title: High resolution dataset for flow past arbitrary bodies

description: This dataset comprises the high-resolution (768 x 512) flowfields for incompressible flow past arbitrarily shaped bodies at low Reynolds number (100, approx.) obtained using FoamExtend based immersed boundary method. The dataset is divided into 3 parts viz. (a) Training dataset containing 50 experiments having 100 frames each (27.5 GB, approx), (b) testing datset containing 50 additional experiments having 300 frames each (82.5 GB, approx), and (c) total dataset (i.e., 100 experiemnts) having 1500 frames each (825 GB, approx). Part (a) forms the training dataset used to train the differentiable physics-assisted neural network (DPNN) whereas part (b) forms the testing dataset for the trained DPNN based model. Finally, part (c) serves as additional dataset for further validation and benchmarking. The dataset only includes the stastically stable velocity flowfields (i.e., time 150s onwards spaced at 0.1s) along with shape mask. The 100 experiments comprises of 100 different geometric configurations (i.e, arbitrary bodies placed in an equilateral triangle position), each at a unique spacing ratio. In addition, the coordinates of the underlying bodies are also provided separately.
DOI: 10.5258/soton/d2785
title: Dataset in support of the Southampton doctoral thesis 'The role of attachment, help-seeking, and acceptance in people with psychosis'

description: Quantitative data, 61 participants, two time points, this data was completed betweeen October 2022 until March 2023 via Prolific. SPSS will be needed to open the dataset file.
DOI: 10.6084/m9.figshare.24155142
title: <b>Age Structure and Body Size of The </b><b>Tibetan Toad</b><b><i> </i></b><b>(</b><b><i>Bufo tibetanus</i></b><b>) Populations from Two Different </b><b>Altitudes </b><b>in China</b>

description: Data used in Xu et al. <b>Age Structure and Body Size of The </b><b>Tibetan Toad</b><b><i> </i></b><b>(</b><b><i>Bufo tibetanus</i></b><b>) Populations from Two Different Altitudes</b><b> </b><b>in China. Ecology and Evolution.</b>
DOI: 10.6084/m9.figshare.24155142.v1
title: <b>Age Structure and Body Size of The </b><b>Tibetan Toad</b><b><i> </i></b><b>(</b><b><i>Bufo tibetanus</i></b><b>) Populations from Two Different </b><b>Altitudes </b><b>in China</b>

description: Data used in Xu et al. <b>Age Structure and Body Size of The </b><b>Tibetan Toad</b><b><i> </i></b><b>(</b><b><i>Bufo tibetanus</i></b><b>) Populations from Two Different Altitudes</b><b> </b><b>in China. Ecology and Evolution.</b>
DOI: 10.6084/m9.figshare.24155232.v1
title: DLECZ_WGS84.tif

description: Distance Low Elevation Coastal Zone Europe (DLECZ). This layer shows the coastal zone in Europe defined by all land below 10 m elevation that is hydrologically connected to the sea, extended by a 20 km distance zone. The layer is referenced to WGS84 at a resolution of 3 arcsec.
DOI: 10.6084/m9.figshare.24155187
title: Acceleration data (subject 1-50)

description: This is the acceleration dataset for subjects 1-50.
DOI: 10.6084/m9.figshare.24155235
title: KEGG_build_2023-09-18

description: An archived version of the KEGG data directory for use in anvi'o. Can be set up on a computer with an anvi'o installation using the command `anvi-setup-kegg-kofams --kegg-archive KEGG_build_2023-09-18_a2b5bde358bb.tar.gz` (specify a --kegg-data-dir to avoid overriding the default KEGG data directory, if one exists).<br>The default KEGG snapshot for anvi'o v8.Contains MODULES.db version 4.<br>Hash value of the database is a2b5bde358bb
DOI: 10.6084/m9.figshare.24155235.v1
title: KEGG_build_2023-09-18

description: An archived version of the KEGG data directory for use in anvi'o. Can be set up on a computer with an anvi'o installation using the command `anvi-setup-kegg-kofams --kegg-archive KEGG_build_2023-09-18_a2b5bde358bb.tar.gz` (specify a --kegg-data-dir to avoid overriding the default KEGG data directory, if one exists).<br>The default KEGG snapshot for anvi'o v8.Contains MODULES.db version 4.<br>Hash value of the database is a2b5bde358bb
DOI: 10.6084/m9.figshare.24155280
title: Acceleration data (subject 51-100)

description: This is the acceleration dataset for subjects 51-100.
DOI: 10.5967/m0bz645x
title: Flood Intermittency and Delta Islands
DOI: 10.5066/f7kp8124
title: Sediment and geomorphology data for Walnut Creek, Iowa

description: This data release consists of 9 comma-delimited ascii files that contain 1) ten-minute discharge values, 2) mean daily discharge and computed sediment loads, 3) grain size percentiles, 4) radionuclide source sample results, 5) radionuclide target sample results,6) laboratory sample splits, 7) elemental analysis results of samples, 8) suspended-sediment measurements, and 9) turbidity measurements.
DOI: 10.18434/t4/1503156
title: Data Set: Assessment of Martensitic Transformation Paths Based on Transformation Potential Calculations

description: This data set supports the technical paper and supporting information documents
            accepted to Steel Research International (https://doi.org/10.1002/srin.201800370). The
            technical paper presents transformation potential calculations for two different
            martensitic transformation paths and compares these to experimentally measured textures
            in a SAE 201 stainless steel. The data set provides: scripts used to calculate
            martensitic transformations, analyze data, and create figures; and pole figure data
            files used to calculate orientation distribution functions (ODF) included in the paper.
            The authors hope that providing this data set will facilitate reuse and expansion, as
            well as making research more reproducible.
DOI: 10.21334/npolar.2017.2f52acd2
title: Ocean and sea ice circulation model results from Svalbard area (ROMS)

description: Model simulation fields (currents, temperature, salinity, sea ice cover fraction, sea ice thickness) from 800 m x 800 m simulations in the Svalbard area.
Standard model output from ROMS (v3.5) provided as daily netCDF files.
Model boundary forcing from 4 km x 4 km resolution ROMS simulations.
DOI: 10.3334/cdiac/otg.ldeo_corr
title: Corrections applied to the LDEO underway pCO2 measurements made aboard the R/V Gould, R/V Palmer and USCGC Healy
DOI: 10.5067/aqua/modis/l3m/chl/2014
title: MODIS-Aqua Level 3 Mapped Chlorophyll Data Version 2014
DOI: 10.5281/zenodo.6560159
title: TIMIT-TTS: a Text-to-Speech Dataset for Synthetic Speech Detection

description: With the rapid development of deep learning techniques, the generation and counterfeiting of multimedia material are becoming increasingly straightforward to perform. At the same time, sharing fake content on the web has become so simple that malicious users can create unpleasant situations with minimal effort. Also, forged media are getting more and more complex, with manipulated videos (e.g., deepfakes where both the visual and audio contents can be counterfeited) that are taking the scene over still images.<br> The multimedia forensic community has addressed the possible threats that this situation could imply by developing detectors that verify the authenticity of multimedia objects. However, the vast majority of these tools only analyze one modality at a time.<br> This was not a problem as long as still images were considered the most widely edited media, but now, since manipulated videos are becoming customary, performing monomodal analyses could be reductive. Nonetheless, there is a lack in the literature regarding multimodal detectors (systems that consider both audio and video components). This is due to the difficulty of developing them but also to the scarsity of datasets containing forged multimodal data to train and test the designed algorithms. In this paper we focus on the generation of an audio-visual deepfake dataset.<br> First, we present a general pipeline for synthesizing speech deepfake content from a given real or fake video, facilitating the creation of counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS) and Dynamic Time Warping (DTW) techniques to achieve realistic speech tracks. Then, we use the pipeline to generate and release TIMIT-TTS, a synthetic speech dataset containing the most cutting-edge methods in the TTS field. This can be used as a standalone audio dataset, or combined with DeepfakeTIMIT and VidTIMIT video datasets to perform multimodal research. Finally, we present numerous experiments to benchmark the proposed dataset in both monomodal (i.e., audio) and multimodal (i.e., audio and video) conditions.<br> This highlights the need for multimodal forensic detectors and more multimodal deepfake data. For the initial version of TIMIT-TTS <strong>v1.0</strong> Arxiv: https://arxiv.org/abs/2209.08000 TIMIT-TTS Database v1.0: https://zenodo.org/record/6560159
DOI: 10.6084/m9.figshare.6936227
title: Station-based Growing Season Length values across Europe calculated using EEMD and ETCCDI methods

description: Growing Season Length (GSL) indices have been calculated at stations across Europe using both the ETCCDI definition and a definition based on an EEMD time-series decomposition using daily mean temperature values. These files contain values of the GSL at European stations, taken from the ECA&D repository, and at three sites (Central England Temperature [CET], Stockholm and St. Peterburg) which provide data, over a much longer period (1772 in the case of the CET series). These data are used in a paper published in the International Journal of Climatology (doi:10.1002/joc.5913).
DOI: 10.3334/cdiac/otg.ndp088(v2014)
title: Global Ocean Surace Water Partial Pressure of CO2 Database: Measurements Performed During 1957-2014 (Version 2014)