cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14411048.v1
title: 2018 Phenology Grasshopper Distributions

description: Data and code for modeling grasshopper range distributions in future climates.<br>
DOI: 10.6084/m9.figshare.14740797
title: Consumption Attitudes and Trends of Vending Machine Foods at the University of Sharjah, UAE: A Cross-Sectional Study

description: This study aimed at assessing consumption trends and attitudes toward VM foods at the University of Sharjah, United Arab Emirates. A web-based survey was conducted among 1250 students, staff, and faculty members to assess the frequency of using VMs and the opinion and attitude toward VM foods.<br>
DOI: 10.5281/zenodo.4903958
title: Benchmark Datasets Incorporating Diverse Tasks, Sample Sizes, Material Systems, and Data Heterogeneity for Materials Informatics

description: This benchmark data is comprised of 50 different datasets for materials properties obtained from 16 previous publications. The data contains both experimental and computational data, data suited for regression as well as classification, sizes ranging from 12 to 6354 samples, and materials systems spanning the diversity of materials research. In addition to cleaning the data where necessary, each dataset was split into train, validation, and test splits. For datasets with more than 100 values, train-val-test splits were created, either with a 5-fold or 10-fold cross-validation method, depending on what each respective paper did in their studies. Datasets with less than 100 values had train-test splits created using the Leave-One-Out cross-validation method. For further information, as well as directions on how to access the data, please go to the corresponding GitHub repository: https://github.com/anhender/mse_ML_datasets/tree/v1.0
DOI: 10.5281/zenodo.4903957
title: Benchmark Datasets Incorporating Diverse Tasks, Sample Sizes, Material Systems, and Data Heterogeneity for Materials Informatics

description: This benchmark data is comprised of 50 different datasets for materials properties obtained from 16 previous publications. The data contains both experimental and computational data, data suited for regression as well as classification, sizes ranging from 12 to 6354 samples, and materials systems spanning the diversity of materials research. In addition to cleaning the data where necessary, each dataset was split into train, validation, and test splits. For datasets with more than 100 values, train-val-test splits were created, either with a 5-fold or 10-fold cross-validation method, depending on what each respective paper did in their studies. Datasets with less than 100 values had train-test splits created using the Leave-One-Out cross-validation method. For further information, as well as directions on how to access the data, please go to the corresponding GitHub repository: https://github.com/anhender/mse_ML_datasets/tree/v1.0
DOI: 10.5281/zenodo.4904182
title: Data archive for "Reconciling the Infrared and Microwave observations of Lunar South Pole: A study on subsurface temperature and regolith density"

description: These are the data related to our study "Reconciling the Infrared and Microwave observations of Lunar South Pole: A study on subsurface temperature and regolith density" in JGR-Planets. "CE2_MRM_South_Polar_Raw_Data.txt" : Brightness temperature data of lunar south polar region (≤-80°) obtained by CE-2 MRM. Others are data used to generate, or be displayed in, figures and plots in the paper.
DOI: 10.5281/zenodo.4444789
title: Data archive for "Reconciling the Infrared and Microwave observations of Lunar South Pole: A study on subsurface temperature and regolith density"

description: These are the data related to our study "Reconciling the Infrared and Microwave observations of Lunar South Pole: A study on subsurface temperature and regolith density" in JGR-Planets. "CE2_MRM_South_Polar_Raw_Data.txt" : Brightness temperature data of lunar south polar region (≤-80°) obtained by CE-2 MRM. Others are data used to generate, or be displayed in, figures and plots in the paper.
DOI: 10.5517/ccdc.csd.cc24lps7
title: CCDC 1984179: Experimental Crystal Structure Determination

description: Related Article: Federico Movilla, Juan M. Rey, Cristián Huck-Iriart, Florencia Di Salvo|2021|Cryst.Growth Des.|21|3487|doi:10.1021/acs.cgd.1c00274
DOI: 10.5517/ccdc.csd.cc2621hv
title: CCDC 2027756: Experimental Crystal Structure Determination

description: Related Article: Jinfang Zhang, Qian Xiang, Yuan Zhu, Junyi Yang, Yinglin Song, Chi Zhang|2021|Cryst.Growth Des.|21|3225|doi:10.1021/acs.cgd.0c01580
DOI: 10.5517/ccdc.csd.cc26nvf5
title: CCDC 2045827: Experimental Crystal Structure Determination

description: Related Article: Jinfang Zhang, Qian Xiang, Yuan Zhu, Junyi Yang, Yinglin Song, Chi Zhang|2021|Cryst.Growth Des.|21|3225|doi:10.1021/acs.cgd.0c01580
DOI: 10.5517/ccdc.csd.cc276x5k
title: CCDC 2062218: Experimental Crystal Structure Determination

description: Related Article: Felipe de la Cruz-Martínez, Marc Martínez de Sarasa Buchaca, Juan Fernández-Baeza, Luis F. Sánchez-Barba, Ana M. Rodríguez, Carlos Alonso-Moreno, José A. Castro-Osma, Agustín Lara-Sánchez|2021|Organometallics|40|1503|doi:10.1021/acs.organomet.1c00164
DOI: 10.5517/ccdc.csd.cc278d75
title: CCDC 2063646: Experimental Crystal Structure Determination

description: Related Article: Itaru Nakamura, Kazuki Masukawa, Yasuhiro Ishida, Masahiro Terada|2021|Org.Lett.|23|4127|doi:10.1021/acs.orglett.1c00995
DOI: 10.5517/ccdc.csd.cc27j778
title: CCDC 2071179: Experimental Crystal Structure Determination

description: Related Article: Michael K. Bellas, Lake V. MacKenzie, Adam J. Matzger|2021|Cryst.Growth Des.|21|3540|doi:10.1021/acs.cgd.1c00302
DOI: 10.5517/ccdc.csd.cc27j789
title: CCDC 2071180: Experimental Crystal Structure Determination

description: Related Article: Michael K. Bellas, Lake V. MacKenzie, Adam J. Matzger|2021|Cryst.Growth Des.|21|3540|doi:10.1021/acs.cgd.1c00302
DOI: 10.5517/ccdc.csd.cc27j79b
title: CCDC 2071181: Experimental Crystal Structure Determination

description: Related Article: Michael K. Bellas, Lake V. MacKenzie, Adam J. Matzger|2021|Cryst.Growth Des.|21|3540|doi:10.1021/acs.cgd.1c00302
DOI: 10.5517/ccdc.csd.cc27j7bc
title: CCDC 2071182: Experimental Crystal Structure Determination

description: Related Article: Michael K. Bellas, Lake V. MacKenzie, Adam J. Matzger|2021|Cryst.Growth Des.|21|3540|doi:10.1021/acs.cgd.1c00302
DOI: 10.34740/kaggle/dsv/2309388
title: Mobile apps with descriptions
DOI: 10.15131/shef.data.12151803
title: Modelling and scaling rules of high-shear wet granulation of pharmaceuticals (PhD project files)

description: Additional datasets which support the results published in PhD thesis (primary data, data processing, additional results)<br>Access is limited to the Department of Chemical and Biological Engineering of the University of Sheffield.
DOI: 10.5281/zenodo.4904781
title: mlpack.org datasets

description: These datasets are those used by mlpack.org and the mlpack organization for examples, pre-existing models, and tutorials. https://github.com/mlpack/mlpack/ https://github.com/mlpack/mlpack.org/ https://github.com/mlpack/examples/ https://github.com/mlpack/models/
description: added notebook example datasets under notebooks/
DOI: 10.6084/m9.figshare.12069993
title: OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach

description: OutFin is a novel dataset of outdoor location fingerprints, collected using two different smartphones. It comprises diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is unique in terms of its reference points' number, arrangement, and spacing. A thorough description of the collection environment is provided in the form of an interactive map, that display information, such as RP coordinates (both ground truth and smartphone estimated), pictures of the collection sites, and building height and ground elevation. <br>(A paper describing the dataset in detail is available at:https://doi.org/10.1038/s41597-021-00832-y)(The python scripts used to validate the dataset can also be obtained from GitHub: https://github.com/alhomayani/OutFin)
DOI: 10.6084/m9.figshare.12069993.v1
title: OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach

description: OutFin is a novel dataset of outdoor location fingerprints, collected using two different smartphones. It comprises diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is unique in terms of its reference points' number, arrangement, and spacing. A thorough description of the collection environment is provided in the form of an interactive map, that display information, such as RP coordinates (both ground truth and smartphone estimated), pictures of the collection sites, and building height and ground elevation. <br>(A paper describing the dataset in detail will soon be available.)(The python scripts used to validate the dataset can also be obtained from GitHub: https://github.com/alhomayani/OutFin)
DOI: 10.6084/m9.figshare.13010387
title: Cellular Network Measurements for Symbolic Indoor Positioning

description: For a description of the dataset please refer to our paper "<i>Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB</i>".
DOI: 10.15131/shef.data.13746469
title: Staphylococcus aureus cell wall structure and dynamics during host-pathogen interaction

description: This is the raw data supporting the findings (both main text and supplementary) for our manuscript "Staphylococcus aureus cell wall structure and dynamics during host-pathogen interaction". Each excel file contains the raw data for each figure. <br><br>Murine work was carried out according to UK law in the Animals (Scientific Procedures) Act 1986, under Project License P3BFD6DB9 (Staphylococcus aureus and other pathogens, pathogenesis to therapy, University of Sheffield Review Board).
DOI: 10.15131/shef.data.13746469.v1
title: Staphylococcus aureus cell wall structure and dynamics during host-pathogen interaction

description: This is the raw data supporting the findings (both main text and supplementary) for our manuscript "Staphylococcus aureus cell wall structure and dynamics during host-pathogen interaction". Each excel file contains the raw data for each figure. <br><br>Murine work was carried out according to UK law in the Animals (Scientific Procedures) Act 1986, under Project License P3BFD6DB9 (Staphylococcus aureus and other pathogens, pathogenesis to therapy, University of Sheffield Review Board).
DOI: 10.26007/a569-3877
title: Balloon Observation Platform for Planetary Science (BOPPS) 2014 Observations

description: This bundle collects all the observational data and documentation generated as part of the first Balloon Observation Platform for Planetary Science (BOPPS) flight to return planetary science data.
DOI: 10.26007/mah6-5r14
title: Comet Data Compilations from Published Sources

description: The collections in this archive bundle contain results that have been reported in the literature, or in some cases made available by private communication, for various comet properties. Individual collections focus on one or several closely related properties or targets of high interest.
DOI: 10.26007/2wyp-cm39
title: EPOXI MRI Observations Bundle

description: This bundle contains observations collected with the MRI instrument on the Deep Impact Flyby Spacecraft during the EPOXI mission.
DOI: 10.15131/shef.data.13636331
title: Data for the paper: "Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models"

description: Data used in the paper: "Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models"Oliviero, Owen, Reilly, Bellantuono, Dall'Ara<br>published in BMMB<br><br>We share here an example of ex vivo microCT image used for the assessment, and the results obtained from the different analyses.<br>Short description of the files included:<br>-microFE_results.xlsx includes:Stiffness estimated for each specimen with microFE models, and correlations with experimental data.Failure load estimated with microFE models, and correlations with experimental data, for each model type:Hexahedral Homogeneous modelsHexahedral models with subject-specific modulusTedrahedral Homogenous modelsTetrahedral models with subject-specific modulusHexahedral Heterogeneous modelsTetrahedral Homogeneous models <br>-microCT data:<br>Example of a segmented microCT image used as input for the microFE model (Specimen1, C57BL/6J, wild type, 16 weeks of age, right tibia).<br>In case the reader is interested in the whole database they should contact Dr Enrico Dall'Ara (e.dallara@sheffield.ac.uk).
DOI: 10.15131/shef.data.13636331.v1
title: Data for the paper: "Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models"

description: Data used in the paper: "Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models"Oliviero, Owen, Reilly, Bellantuono, Dall'Ara<br>published in BMMB<br><br>We share here an example of ex vivo microCT image used for the assessment, and the results obtained from the different analyses.<br>Short description of the files included:<br>-microFE_results.xlsx includes:Stiffness estimated for each specimen with microFE models, and correlations with experimental data.Failure load estimated with microFE models, and correlations with experimental data, for each model type:Hexahedral Homogeneous modelsHexahedral models with subject-specific modulusTedrahedral Homogenous modelsTetrahedral models with subject-specific modulusHexahedral Heterogeneous modelsTetrahedral Homogeneous models <br>-microCT data:<br>Example of a segmented microCT image used as input for the microFE model (Specimen1, C57BL/6J, wild type, 16 weeks of age, right tibia).<br>In case the reader is interested in the whole database they should contact Dr Enrico Dall'Ara (e.dallara@sheffield.ac.uk).
DOI: 10.26007/per7-4c11
title: Comet C/ISON (2012 S1) Ground-Based Observations Bundle

description: This bundle contains raw, processed, and derived imaging and spectroscopic data of comet ISON taken in 2013.
DOI: 10.5281/zenodo.3700214
title: Input data files for RSS-NET analysis of simulated GWAS summary statistics and B cell regulatory network

description: Details of these data files are provided in https://suwonglab.github.io/rss-net/wtccc_bcell. Contact:<code> xiangzhu[at]psu.edu </code>
DOI: 10.5281/zenodo.3700213
title: Input data files for RSS-NET analysis of simulated GWAS summary statistics and B cell regulatory network

description: Details of these data files are provided in https://suwonglab.github.io/rss-net/wtccc_bcell. Contact:<code> xiangzhu[at]psu.edu </code>
DOI: 10.15131/shef.data.12284306
title: Data for manuscript titled "A systematic approach to the scale separation problem in the development of multiscale models"

description: The following files are included: Patient60y.vtk, an anonymised data file (in VTK unstructured grid format) for a proximal femur, which is used as input to the multiscale model illustrated in §2.3 of the main manuscript The proximal femur data corresponds to a subject aged 60 years at the time of clinical presentationThe data file contains the proximal femur geometry, discretised using tetrahedral elements possessing heterogeneous element-wise volumetric bone mineral density (vBMD)Units for the fields in the data file are given in the Readme.txt file<br> Figs1and4.pvsm, a state file in the open-source viewer Paraview format. It contains the image manipulations needed to convert the data in Patient60y.vtk to that in Fig 1 of the main manuscriptIt also contains the code to convert the vBMD data to that corresponding to age 70 y, as shown in Fig 4 of the main manuscriptThis dataset contains data in only processed and anonymised form. It is derived from an original dataset that was collected under the project UK MRC grant G0601272. Ethics approval was obtained for this project from the relevant local ethics committee in Sheffield.
DOI: 10.15131/shef.data.12284306.v1
title: Data for manuscript titled "A systematic approach to the scale separation problem in the development of multiscale models"

description: The following files are included: Patient60y.vtk, an anonymised data file (in VTK unstructured grid format) for a proximal femur, which is used as input to the multiscale model illustrated in §2.3 of the main manuscript The proximal femur data corresponds to a subject aged 60 years at the time of clinical presentationThe data file contains the proximal femur geometry, discretised using tetrahedral elements possessing heterogeneous element-wise volumetric bone mineral density (vBMD)Units for the fields in the data file are given in the Readme.txt file<br> Figs1and4.pvsm, a state file in the open-source viewer Paraview format. It contains the image manipulations needed to convert the data in Patient60y.vtk to that in Fig 1 of the main manuscriptIt also contains the code to convert the vBMD data to that corresponding to age 70 y, as shown in Fig 4 of the main manuscriptThis dataset contains data in only processed and anonymised form. It is derived from an original dataset that was collected under the project UK MRC grant G0601272. Ethics approval was obtained for this project from the relevant local ethics committee in Sheffield.
DOI: 10.26007/ncj5-ey94
title: Groundbased Observations at Cerro Tololo Inter-American Observatory

description: This bundle collects data of small body observations taken at Cerro Tololo Inter-American Observatory.
DOI: 10.26007/hdez-ct96
title: Groundbased Observations at the NASA Infrared Telescope Facility (IRTF)

description: This bundle collects data from small body observations taken at IRTF and associated document files.
DOI: 10.26007/4wa4-h885
title: Groundbased Observations at Kitt Peak National Observatory (KPNO)

description: This bundle collects small body observation data taken at Kitt Peak National Observatory and associated document files.
DOI: 10.26007/rh7v-sm76
title: Groundbased Observations at McDonald Observatory

description: This bundle collects small bodies data taken at McDonald Observatory.
DOI: 10.26007/ysm1-bn15
title: Comet Data from the Solar and Heliospheric Observatory (SOHO)

description: This archive bundle contains collections of comet observations and derived results from the SOHO data archives, with related documentation.
DOI: 10.5281/zenodo.3698240
title: Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network

description: Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell. Contact:<code> xiangzhu[at]psu.edu </code>
DOI: 10.5281/zenodo.3698239
title: Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network

description: Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell. Contact:<code> xiangzhu[at]psu.edu </code>
DOI: 10.15131/shef.data.13194845
title: PRIEST study anonymised dataset

description: The PRIEST study used patient data from the early phases of the COVID-19 pandemic. The PRIEST study provided descriptive statistics of UK patients with suspected COVID-19 in an emergency department cohort, analysis of existing triage tools, and derivation and validation of a COVID-19 specific tool for adults with suspected COVID-19. <br>For more details please go to the study website:<br>https://www.sheffield.ac.uk/scharr/research/centres/cure/priest<br>Files contained in PRIEST study data repository <br>Main files include:<br>PRIEST.csv dataset contains 22445 observations and 119 variables. Data include initial presentation and follow-up, one row per participant.PRIEST_variables.csv contains variable names, values and brief description.<br><br>Additional files include:<br>Follow-up v4.0 PDF - Blank 30-day follow-up data collection toolPandemic Respiratory Infection Form v7 PDF - Blank baseline data collection toolPRIEST protocol v11.0_17Aug20 PDF - Study protocolPRIEST_SAP_v1.0_19jun20 PDF - Statistical analysis plan<br>The PRIEST data sharing plan follows a controlled access model as described in Good Practice Principles for Sharing Individual Participant Data from Publicly Funded Clinical Trials. Data sharing requests should be emailed to priest-study@sheffield.ac.uk. Data sharing requests will be considered carefully as to whether it is necessary to fulfil the purpose of the data sharing request. For approval of a data sharing request an approved ethical review and study protocol must be provided. <br> The PRIEST study was approved by NRES Committee North West - Haydock. REC reference: 12/NW/0303 <br>
DOI: 10.15131/shef.data.12344288
title: Data for 'Bumblebees Learn a Relational Rule but Switch to a Win-Stay/Lose-Switch Heuristic After Extensive Training'

description: Mapping animal performance in a behavioral task to underlying cognitive mechanisms and strategies is rarely straightforward, since a task may be solvable in more than one manner. Here, we show that bumblebees perform well on a concept-based visual discrimination task, but spontaneously switch from a concept-based solution to a simpler heuristic with extended training, all while continually increasing performance. Bumblebees were trained in an arena to find reward on displays with shapes of different sizes where they could not use low-level visual cues. One group of bees was rewarded at displays with bigger shapes and another group at displays with smaller shapes. Analysis of total choices shows bees increased their performance over 30 bouts to above chance. However, analyses of first and sequential choices suggest that after approximately 20 bouts, bumblebees changed to a win-stay/lose-switch strategy. Comparing bees’ behavior to a probabilistic model based on a win-stay/lose-switch strategy further supports the idea that bees changed strategies with extensive training. Analyses of unrewarded tests indicate bumblebees learned and retained the concept of relative size even after they had already switched to a win-stay, lost-shift strategy. We propose that the reason for this strategy switching may be due to cognitive flexibility and efficiency.