cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4049959
title: FIB-tomography data of porous Zr-oxide fabricated with varying sintering conditions

description: This dataset contains 3D data acquired with FIB-tomography from porous Zr-oxide. The pore structures of the ceramic materials were investigated in context with optimization of transport properties (aimed for low viscous flow/permeability and high electric conduction via liquid electrolyte in the pores), so that the Zr-oxide is suitable for application as liquid junction (diaphragm) in pH-sensors. Further details on the scientific background are given in Holzer et al., 2016, Materials & Design, 99, 314–327. (http://doi.org/10.1016/j.matdes.2016.03.034). The tomographs represent 9 ceramic materials that were produced with different sintering temperatures (1250, 1300, 1325 and 1350°C) and different sintering times (1, 2 and 3 hours). The study includes quantitative descriptions of relevant microstructure characteristics (porosity, tortuosity, constrictivity, hydraulic radius) and corresponding effective transport prorperties (permeability, conductivity). Each tomograph is presented as stack of 2D-tiff-images in two different versions: as gray-scale images (raw data) and as binarized images (segmented, with solid=black and pores=white). The voxel resolution is 10x10x10 nm for all 18 image stacks. More details on the image data (e.g. voxel nr, image window size, data size) are given in the downloadable file '2_3D_Data_Info.xlsx'.
description: &gt; Funding: Swiss Commission for Technology and Innovation (CTI), Project Nr. 16851.1 PFNM-NM &gt; Project Database: https://www.zhaw.ch/no_cache/en/research/research-database/project-detailview/projektid/2106/ &gt; Authors Personal Homepages: https://www.zhaw.ch/en/about-us/person/holz/ https://www.zhaw.ch/index.php?id=9118 https://www.researchgate.net/profile/Lorenz_Holzer https://scholar.google.com/citations?hl=de&user=9Wr7IccAAAAJ
DOI: 10.6084/m9.figshare.12851349.v3
title: Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects

description: Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those using a large randomized experiment (215 million observations). Compared with the experiment, naive observational estimators overstate peer effects by over 300% and commonly available variables (e.g., demographics) offer little bias reduction. Adjusting for a measure of prior behaviors closely related to the focal behavior reduces this bias by 91%, while models adjusting for over 3700 past behaviors provide additional bias reduction, reducing bias by over 97%, which is statistically indistinguishable from unbiasedness. This demonstrates how detailed records of behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of peer effects in rare or new behaviors. More generally, these results show how large, high-dimensional datasets and statistical learning can be used to improve causal inference. Supplementary materials for this article are available online.
DOI: 10.26300/70m7-p205
title: Care Interventions for People Living With Dementia (PLWD) and Their Caregivers
DOI: 10.5281/zenodo.4050092
title: Silvicultural and economic dataset for even-aged and coppice-with-standards managements in France.

description: Data set from DEFIFORBOIS project "Development and sustainability of the forest-wood sector in the Centre region", PSDR 4 project Centre-Val de Loire Region, France. This study has been carried out also with financial support from the French National Research Agency (ANR) in the frame of the Investments for the future Programme, within the Cluster of Excellence COTE (ANR-10-LABX-45) through the Project LUCAS.
DOI: 10.5281/zenodo.4050091
title: Silvicultural and economic dataset for even-aged and coppice-with-standards managements in France.

description: Data set from DEFIFORBOIS project "Development and sustainability of the forest-wood sector in the Centre region", PSDR 4 project Centre-Val de Loire Region, France. This study has been carried out also with financial support from the French National Research Agency (ANR) in the frame of the Investments for the future Programme, within the Cluster of Excellence COTE (ANR-10-LABX-45) through the Project LUCAS.
DOI: 10.7910/dvn/ytxahr/mmrnet
title: Supplementary_DATA3.tab

description: :unav
DOI: 10.7910/dvn/ytxahr/dfwajh
title: Supplementary_DATA2.tab

description: :unav
DOI: 10.7910/dvn/ytxahr/1yfjyz
title: Supplementary_DATA1.tab

description: :unav
DOI: 10.7910/dvn/ytxahr/fqbil2
title: Supplementary_Materials.pdf

description: :unav
DOI: 10.6084/m9.figshare.9785867
title: Data of manuscript: Exploring individual variation in associative learning abilities through an operant conditioning task in wild baboons

description: Learning processes analyses Ind: the individual member of the troop Trial: trial number Resp: every choice of kernel individuals had throughout the trial (either 1 / 0) corr.pile: the number of palatable kernels available after each choice of kernel incorre pile: the number of unpalatable kernels available after each choice of kernel Number: the number of kernel (1-40) Troop: the study troop (either J or L) Fst.Choice: the first choice of kernels they had in each trial Age: age class of the individual (either Ad / Juv) Sex: the sex of the individual (either M / F) Rank: the relative rank of each individual Vigil: The number of vigilance intances observed in each trial Boldness: boldness index for each individual TT(s): total time of the trial in seconds.Dom: Occurence of interruptions during each trial (either Y / N) Prop: the calculated proportion of remaining kernels that were palatable after each choice
DOI: 10.6084/m9.figshare.9785867.v1
title: Data of manuscript: Exploring individual variation in associative learning abilities through an operant conditioning task in wild baboons

description: Learning processes analyses Ind: the individual member of the troop Trial: trial number Resp: every choice of kernel individuals had throughout the trial (either 1 / 0) corr.pile: the number of palatable kernels available after each choice of kernel incorre pile: the number of unpalatable kernels available after each choice of kernel Number: the number of kernel (1-40) Troop: the study troop (either J or L) Fst.Choice: the first choice of kernels they had in each trial Age: age class of the individual (either Ad / Juv) Sex: the sex of the individual (either M / F) Rank: the relative rank of each individual Vigil: The number of vigilance intances observed in each trial Boldness: boldness index for each individual TT(s): total time of the trial in seconds.Dom: Occurence of interruptions during each trial (either Y / N) Prop: the calculated proportion of remaining kernels that were palatable after each choice
DOI: 10.6084/m9.figshare.13000151
title: data

description: data used in the evaluation
DOI: 10.5285/be8af49f-cdf1-443a-9c78-6a59c9d5be68
title: Electromagnetic ion cyclotron electron diffusion coefficients calculated from CRRES data using a new approach

description: The data provided is the underlying data used for creating the plots in Ross et al 2020. The research leading to these results has received funding from the National Environment Research Council Highlight Topic grant NE/P01738X/1 (Rad-Sat), National Environment Research Council grant NE/R016445/1 and NE/R016038/1, and the STFC grant ST/S000496/1
description: The wave measurements are from the CRRES (Meredith et al 2014) and plasma densities are from Meredith et al 2002. The diffusion coefficients are calculated using PADIE (Glauert et al 2005) and the model run is using BAS-RBM (Glauert et al 2014)
description: Code: BAS-RBM (Glauert et al 2014) Code: PADIE (Glauert et al 2005) Satellite: CRRES: fluxgate magnetometer (Singer et al. 1992); Plasma Wave Experiment (Anderson et al. 1992).
description: The observations are all from existing papers.
DOI: 10.34740/kaggle/dsv/1166450
title: ISWC 2020: COVID-19 Literature Knowledge Graph
DOI: 10.5281/zenodo.4050130
title: Dinosaurus dataset

description: A 500-pack sample was collected. For each pack, the person who ate it (anonymized) as well as the three dinosaurs found were logged.
DOI: 10.5281/zenodo.4041488
title: MyFood Dataset

description: MyFood Dataset is an image database for segmenting images of Brazilian foods. Composed of 9 classes: rice, beans, boiled egg, fried egg, pasta, salad, roasted meat, apple and chicken breast. With an average of 125 images per class and a total of 1250 images, with a ratio of 60-20-20 for the training, validation and testing sets, respectively. All images have their respective food segmentation masks, according to the folder structure: <strong>Note</strong>: mask_1.png corresponds to the mask of img_1.png, and so on. <strong>MyFoodDataset/</strong> <strong> - train/</strong> <strong> img_1.png </strong> <strong> img_2.png</strong> <strong> …</strong> <strong> - train_ann/</strong> <strong> mask_1.png</strong> <strong> mask_2.png</strong> <strong> …</strong> <strong> - val/</strong> <strong> img_1.png</strong> <strong> img_2.png</strong> <strong> …</strong> <strong> - val_ann/</strong> <strong> mask_1.png</strong> <strong> mask_2.png</strong> <strong> …</strong> <strong> - test/</strong> <strong> img_1.png</strong> <strong> img_2.png</strong> <strong> …</strong> <strong> - test_ann/</strong> <strong> mask_1.png</strong> <strong> mask_2.png</strong> <strong> …</strong>
DOI: 10.5281/zenodo.4041487
title: MyFood Dataset

description: MyFood Dataset is an image database for segmenting images of Brazilian foods. Composed of 9 classes: rice, beans, boiled egg, fried egg, pasta, salad, roasted meat, apple and chicken breast. With an average of 125 images per class and a total of 1250 images, with a ratio of 60-20-20 for the training, validation and testing sets, respectively. All images have their respective food segmentation masks, according to the folder structure: <strong>Note</strong>: mask_1.png corresponds to the mask of img_1.png, and so on. <strong>MyFoodDataset/</strong> <strong> - train/</strong> <strong> img_1.png </strong> <strong> img_2.png</strong> <strong> …</strong> <strong> - train_ann/</strong> <strong> mask_1.png</strong> <strong> mask_2.png</strong> <strong> …</strong> <strong> - val/</strong> <strong> img_1.png</strong> <strong> img_2.png</strong> <strong> …</strong> <strong> - val_ann/</strong> <strong> mask_1.png</strong> <strong> mask_2.png</strong> <strong> …</strong> <strong> - test/</strong> <strong> img_1.png</strong> <strong> img_2.png</strong> <strong> …</strong> <strong> - test_ann/</strong> <strong> mask_1.png</strong> <strong> mask_2.png</strong> <strong> …</strong>
DOI: 10.5281/zenodo.3901619
title: Data for "Photon-recoil Imaging: Expanding the view of nonlinear x-ray physics"

description: Datasets for the figures presented in the main manuscript of the publication "Photon-recoil Imaging: Expanding the view of nonlinear x-ray physics" . The dataset Fig2A-red-dots.dat represents also the red dots data in Figure 3A. Fig2B-data.dat and Fig2C-data.dat contain histogram data with each entry (line) representing the coordinates (x,y) in mm on the detector where a corresponding Ne* atom was detected.<br> With these data Fig 2B and 2C of the publication can be reproduced by using the Wolfram Mathematica command lines: Test855 = Import["Fig2C-data.dat"]<br> DensityHistogram[Test855, {100, 100},<br> PlotRange -&gt; {{-10, 5}, {0, 25}},<br> ColorFunction -&gt; ColorData[{"SolarColors", "Reverse"}],<br> AspectRatio -&gt; 5/3,(* Background\[Rule]LightGreen,*)<br> FrameTicksStyle -&gt; Directive[Black, 20], ChartLegends -&gt; Automatic] All other data sets contain the data points of the corresponding figure of the publication, with the first entry in each line representing the x- and the second one the y-coordinate value and, where applicable the error bar.
DOI: 10.5281/zenodo.3901618
title: Data for "Photon-recoil Imaging: Expanding the view of nonlinear x-ray physics"

description: Datasets for the figures presented in the main manuscript of the publication "Photon-recoil Imaging: Expanding the view of nonlinear x-ray physics" . The dataset Fig2A-red-dots.dat represents also the red dots data in Figure 3A. Fig2B-data.dat and Fig2C-data.dat contain histogram data with each entry (line) representing the coordinates (x,y) in mm on the detector where a corresponding Ne* atom was detected.<br> With these data Fig 2B and 2C of the publication can be reproduced by using the Wolfram Mathematica command lines: Test855 = Import["Fig2C-data.dat"]<br> DensityHistogram[Test855, {100, 100},<br> PlotRange -&gt; {{-10, 5}, {0, 25}},<br> ColorFunction -&gt; ColorData[{"SolarColors", "Reverse"}],<br> AspectRatio -&gt; 5/3,(* Background\[Rule]LightGreen,*)<br> FrameTicksStyle -&gt; Directive[Black, 20], ChartLegends -&gt; Automatic] All other data sets contain the data points of the corresponding figure of the publication, with the first entry in each line representing the x- and the second one the y-coordinate value and, where applicable the error bar.
DOI: 10.25386/genetics.12436649.v1
title: Supplemental Material for Hillis et al., 2020

description: Supplemental material for "Genetic Basis of Aerobically Supported Voluntary Exercise: Results from a Selection Experiment with House Mice." This contains supplemental figures, complete raw data sets for SNP and haplotype analyses, results for SNP and haplotype analyses, and R and SAS code for conducting SNP and haplotype analyses. Additionally, files and tables relevant to results and discussion, are included.
DOI: 10.25386/genetics.12436649
title: Supplemental Material for Hillis et al., 2020

description: Supplemental material for "Genetic Basis of Aerobically Supported Voluntary Exercise: Results from a Selection Experiment with House Mice." This contains supplemental figures, complete raw data sets for SNP and haplotype analyses, results for SNP and haplotype analyses, and R and SAS code for conducting SNP and haplotype analyses. Additionally, files and tables relevant to results and discussion, are included.
DOI: 10.5281/zenodo.4050145
title: Dinosaurus dataset

description: A 500-pack sample was collected. For each pack, the person who ate it (anonymized) as well as the three dinosaurs found were logged.
DOI: 10.5281/zenodo.4050129
title: Dinosaurus dataset

description: A 500-pack sample was collected. For each pack, the person who ate it (anonymized) as well as the three dinosaurs found were logged.
DOI: 10.18112/openneuro.ds003058.v1.0.1
title: Assesment of the visual stimuli properties in P300 paradigm
DOI: 10.5523/bris.2ypb34qlp93ox2spy3k3ql4trd
title: Data from LiN codoping paper (Carbon 2020)

description: Data from the CRYSTAL17 simulations of LixNy doping of diamond, published in the ASI special issue of Carbon, 2020.
DOI: 10.7910/dvn/dcdkzq/del4su
title: doi_10.7910_DVN_0ABVEM.json

description: A metadata file of a Dataverse-based repository's published dataset, provided here as a sample.
DOI: 10.7910/dvn/dcdkzq/kbfzpk
title: harvard_dataset_metadata_2019.12.08_22.20.46.7z

description: To open the 7zip file, download 7-Zip at https://www.7-zip.org
DOI: 10.7910/dvn/dcdkzq/m1rcq1
title: language.tab

description: Sample file, one of the 23 CSV files in this dataset's compressed file.
DOI: 10.7910/dvn/dcdkzq/xhs9qi
title: harvard_dataset_metadata_2019.12.08_22.20.46-1.7z

description: :unav
DOI: 10.7910/dvn/dcdkzq/fc33hd
title: dataverse_repository_metadata.rar

description: This archive contains 38 directories, one for each of the 38 Dataverse-based repositories whose dataset metadata is accessible using Dataverse APIs. Each directory contains a text file and a sub-directory. The text file contains the persistent IDs of the published datasets in a Dataverse-based repository. The sub-directory contains a JSON file for each of the datasets whose PIDs are listed in the text file. The JSON files contain dataset metadata in Dataverse's "JSON" metadata export. This file contains 123,267 files and 76 directories or sub-directories. Uncompressed, the .rar file is about 2 GB on disk.
DOI: 10.7910/dvn/dcdkzq/bqlqzk
title: dataset_pids_Harvard_Dataverse_2020.05.21_04.27.00.txt

description: A text file listing the persistent identifiers of a Dataverse-based repository's published datasets, provided here as a sample.
DOI: 10.7910/dvn/dcdkzq/e0v0bt
title: DaRUS_2020.06.24_22.09.16.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/3lctwt
title: University_of_Manitoba_Dataverse_2020.06.26_02.25.27.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/hnc9sb
title: UAL_Dataverse_2020.06.26_02.19.19.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/8q4pwe
title: Pontificia_Universidad_Católica_del_Perú_2020.06.26_01.55.52.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/y59zyv
title: Repositório_de_Dados_de_Pesquisa_da_UFABC_2020.06.26_01.58.55.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/fiwrtw
title: data.sciencespo_2020.06.25_18.49.17.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/n5fdmj
title: VTTI_2020.06.26_02.28.05.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/fidr9s
title: Università_degli_Studi_di_Milano_2020.06.26_02.26.11.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/nlius9
title: Institute_of_Russian_Literature_Dataverse_2020.06.26_01.42.21.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/wsgyah
title: DataverseNL_2020.06.25_18.55.14.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/be7vhb
title: NIOZ_Dataverse_2020.06.26_01.47.38.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/31c405
title: Dartmouth_Dataverse_2020.06.24_22.08.55.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/p1lyf7
title: ACSS_Dataverse_2020.06.24_21.43.35.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/nkg08e
title: AUSSDA_Dataverse_2020.06.24_21.44.43.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/yfoimj
title: DataverseNO_2020.06.25_19.10.19.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/qig9gk
title: Peking_University_2020.06.26_01.48.15.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/jekqr4
title: Data_Suds_2020.06.25_18.48.36.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/q8lkur
title: ICWSM_2020.06.26_01.30.58.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/wdtpcx
title: Repositorio_de_Datos_de_Investigación_Universidad_del_Rosario_2020.06.26_01.58.45.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/v400fb
title: UCLA_Dataverse_2020.06.26_02.25.01.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/h7yhxy
title: Libra_Data_2020.06.26_01.42.53.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/6eqoa8
title: Harvard_Dataverse_2020.05.21_04.27.00.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/m5dnyy
title: Open_Data_@_UCLouvain_2020.06.26_01.47.59.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/ouilsn
title: MELDATA_2020.06.26_01.43.48.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/8b2ot4
title: Ifsttar_Dataverse_2020.06.26_01.31.05.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/sq3rtq
title: CIMMYT_Research_Data_2020.06.24_21.55.32.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/2efbck
title: Göttingen_Research_Online_2020.06.25_19.44.56.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/imae7q
title: DataRepositoriUM_2020.06.25_18.53.31.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/j94s9q
title: NIE_Data_Repository_2020.06.26_01.46.07.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/34nhui
title: HeiDATA_2020.06.26_01.27.32.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/qyfcgs
title: Jülich_DATA_2020.06.26_01.42.47.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/0laejj
title: UNB_Libraries_Dataverse_2020.06.26_02.25.16.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/la048g
title: Johns_Hopkins_University_2020.06.26_01.42.30.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/mpohgu
title: DR-NTU_(Data)_2020.06.25_19.21.40.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/wjr5bl
title: IISH_Dataverse_2020.06.26_01.31.21.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/g9bpsg
title: QDR_Main_Collection_2020.06.26_01.56.39.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/h2moo2
title: Scholars_Portal_2020.06.26_01.59.49.rar

description: :unav
DOI: 10.7910/dvn/dcdkzq/6dzorw
title: metadatablocks.rar

description: This .rar file contains a folder for all metadatablocks of each of the 37 installations. The folder names include the Dataverse software version used by the installation when the installation's metadatablocks and dataset metadata were downloaded.
DOI: 10.7910/dvn/dcdkzq/63wapg
title: metadatablocks.tab

description: This tabular file contains the metadatablock names, field names and subfield names (if the field is a compound field) of the 37 Dataverse installations' metadatablocks. Useful for analysis of the metadatablocks of the 37 Dataverse installations.
DOI: 10.7910/dvn/dcdkzq/f5zfze
title: metadatablocks.tab

description: This tabular file contains the metadatablock names, field names and subfield names (if the field is a compound field) of the 35 Dataverse installations' metadatablocks. Useful for analysis of the metadatablocks of the 35 Dataverse installations.
DOI: 10.7910/dvn/dcdkzq/md8lki
title: metadatablocks.rar

description: This .rar file contains a folder for all metadatablocks of each of the 35 installations. The folder names include the Dataverse software version used by the installation when the installation's metadatablocks and dataset metadata were downloaded.
DOI: 10.7910/dvn/dcdkzq/p1cqjh
title: Repositorio_de_Datos_de_Investigación_Universidad_del_Rosario_2020.06.26_01.58.45.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/jo6fvd
title: MELDATA_2020.06.26_01.43.48.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/llr0km
title: Universita_degli_Studi_di_Milano_2020.06.26_02.26.11.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/qgle4r
title: Libra_Data_2020.06.26_01.42.53.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/z8nqfe
title: AUSSDA_Dataverse_2020.06.24_21.44.43.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/lpwpeg
title: Data_Suds_2020.06.25_18.48.36.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/mljtt2
title: Institute_of_Russian_Literature_Dataverse_2020.06.26_01.42.21.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/auygam
title: CIMMYT_Research_Data_2020.06.24_21.55.32.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/yoxmat
title: DataverseNL_2020.06.25_18.55.14.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/46xcoo
title: VTTI_2020.06.26_02.28.05.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/ggyep7
title: DR-NTU_(Data)_2020.06.25_19.21.40.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/ybgcwa
title: DataverseNO_2020.06.25_19.10.19.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/upmkjf
title: UNB_Libraries_Dataverse_2020.06.26_02.25.16.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/pbpluy
title: Repositorio_de_Dados_de_Pesquisa_da_UFABC_2020.06.26_01.58.55.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/kbpkie
title: Peking_University_2020.06.26_01.48.15.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/gm8g2n
title: DaRUS_2020.06.24_22.09.16.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/gnd3td
title: QDR_Main_Collection_2020.06.26_01.56.39.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/mu5wpn
title: UCLA_Dataverse_2020.06.26_02.25.01.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/9ihwll
title: Gottingen_Research_Online_2020.06.25_19.44.56.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/qdrseh
title: Julich_DATA_2020.06.26_01.42.47.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/ww9dvo
title: DataRepositoriUM_2020.06.25_18.53.31.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/gawnlq
title: Harvard_Dataverse_2020.05.21_04.27.00.zip

description: :unav
DOI: 10.7910/dvn/dcdkzq/l7wov5
title: NIE_Data_Repository_2020.06.26_01.46.07.zip

description: :unav