cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/wthm-c116-3082
title: B'nai Brith Messenger, 1929-10-04/1929-12-27

description: Ten issues of "B'nai Brith Messenger" combined with "California Jewish Review," 1929 October 04/1929 December 27. The disbound set of issues, which is missing one issue from its run, has the title at the top center, location (I. W. Hellman Building, Los Angeles, California, USA) and phone number at the right, and statement, at the left, that the paper was the only English-language Jewish newspaper in Los Angeles. Below are the volume and number (Vol. 31, Numbers 40-52), city, state, and date, and single copy and subscription rates. Each issue is twelve pages and contains local, national, and international (particularly matters pertaining to Zionism and Palestine) news, as well as business, social, entertainment, sports, and other features. There are some photographs, mainly of individuals, and many advertisements in the issues. Item 1.1 comprises the run from July 19. In fair condition, the run generally has brittleness, surface soiling, browning, looseness from the disbinding, losses along edges, and edge tears.
DOI: 10.25549/wthm-c116-3087
title: B'nai Brith Messenger, 1929-10-04/1929-12-27

description: Ten issues of "B'nai Brith Messenger" combined with "California Jewish Review," 1929 October 04/1929 December 27. The disbound set of issues, which is missing one issue from its run, has the title at the top center, location (I. W. Hellman Building, Los Angeles, California, USA) and phone number at the right, and statement, at the left, that the paper was the only English-language Jewish newspaper in Los Angeles. Below are the volume and number (Vol. 31, Numbers 40-52), city, state, and date, and single copy and subscription rates. Each issue is twelve pages and contains local, national, and international (particularly matters pertaining to Zionism and Palestine) news, as well as business, social, entertainment, sports, and other features. There are some photographs, mainly of individuals, and many advertisements in the issues. Item 1.1 comprises the run from July 19. In fair condition, the run generally has brittleness, surface soiling, browning, looseness from the disbinding, losses along edges, and edge tears.
DOI: 10.6084/m9.figshare.14478630.v1
title: Additional file 11 of Identification of polycistronic transcriptional units and non-canonical introns in green algal chloroplasts based on long-read RNA sequencing data

description: Additional file 11: Table S8. Prediction of intronic ORFs of atpF and ccsA in Bryopsidales.
DOI: 10.6084/m9.figshare.14478713.v1
title: Additional file 11 of Five long non-coding RNAs establish a prognostic nomogram and construct a competing endogenous RNA network in the progression of non-small cell lung cancer

description: Additional file 11 : Supplementary Table 5. Differentially expressed RANs in our analysis.
DOI: 10.5281/zenodo.7892171
title: NsEllipse datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

description: Datasets with simulations of the incompressible flow around an elliptical cylinder as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:<br> "Multi-scale rotation-equivariant graph neural networks for<br> unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679) The datasets are:<br> - train/NsEllipse<br> - test/NsEllipseLowRe<br> - test/NsEllipseHighRe<br> - test/NsEllipseThin<br> - test/NsEllipseThick<br> - test/NsEllipseNarrow<br> - test/NsEllipseWide<br> - test/NsEllipseAoA To cite these datasets, use the following reference: Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022). @article{lino2022multi,<br> author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},<br> title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},<br> journal = {Physics of Fluids},<br> volume = {34},<br> year = {2022},<br> url = {https://doi.org/10.1063/5.0097679},<br> }<br>
DOI: 10.5281/zenodo.7892170
title: NsEllipse datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

description: Datasets with simulations of the incompressible flow around an elliptical cylinder as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:<br> "Multi-scale rotation-equivariant graph neural networks for<br> unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679) The datasets are:<br> - train/NsEllipse<br> - test/NsEllipseLowRe<br> - test/NsEllipseHighRe<br> - test/NsEllipseThin<br> - test/NsEllipseThick<br> - test/NsEllipseNarrow<br> - test/NsEllipseWide<br> - test/NsEllipseAoA To cite these datasets, use the following reference: Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022). @article{lino2022multi,<br> author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},<br> title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},<br> journal = {Physics of Fluids},<br> volume = {34},<br> year = {2022},<br> url = {https://doi.org/10.1063/5.0097679},<br> }<br>
DOI: 10.5281/zenodo.7483992
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2019

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2019. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2020</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2019 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7483991
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2019

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2019. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2020</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2019 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.25345/c59g5gq26
title: MassIVE MSV000091866 - Embryonic and larval exposure to propylparaben induces developmental and long_term neurotoxicity in the zebrafish model
title: Parabens are preservatives, antimicrobials, and yeast inhibitors found in cosmetics, processed foods, and medications. Because of their capacity to influence or disrupt the endocrine system, parabens have been designated as endocrine disrupting chemicals. However, the harmful repercussions on the central nervous system, especially during development, by one of the most common parabens, propyl paraben (PrP), are yet unknown. In this study, the neurodevelopmental effects of PrP and long_term neurotoxicity in the zebrafish model were investigated using an integrated approach. Zebrafish embryos were exposed to two different concentrations of PrP (10 and 1000 ug/L) for four days post fertilization (dpf), with the former concertation being human-relevant. Immediately after the PrP exposure, zebrafish larvae were examined for their behavioral phenotypes (open_field behavior, startle response, and circadian rhythmicity) and expression levels of relevant brain markers (cyp19a1b, pax6a, shank3a, and gad1b). We also examined the long_term behavioral and cognitive impacts on sociability, cerebral functional asymmetry, and thigmotaxis on 30 dpf and 60 dpf juveniles. Moreover, in 60 dpf specimens, long_term effects were tested using proteomic analysis and gene expression analysis. The results demonstrated that early_life exposure to PrP promotes developmental and persistent neurobehavioral alterations in the zebrafish model, affecting protein levels associated with brain development and metabolism and compromising behavioral functions.
DOI: 10.5281/zenodo.7900194
title: boltenia_villosa_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7900193
title: boltenia_villosa_spades_01.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/5609830
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.6543497
title: Including Data Management in Research Culture Increases the Reproducibility of Scientific Results

description: <strong>General Information:</strong> This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30 This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data. <strong>Run the code:</strong> To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". Alternatively, you can import the .ipyab file in the colab [2] and run it. <br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br>
DOI: 10.5281/zenodo.6543496
title: Including Data Management in Research Culture Increases the Reproducibility of Scientific Results

description: <strong>General Information:</strong> This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30 This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data. <strong>Run the code:</strong> To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". Alternatively, you can import the .ipyab file in the colab [2] and run it. <br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br>
DOI: 10.5281/zenodo.7900081
title: PointFaces: Point Cloud Dataset of Facial Landmarks and Blendshapes

description: The dataset is based on 1000 facial landmark captures performed on the FFHQ facial image dataset.<br> The 3D blendshape models are sculpted based on the MediaPipe canonical model.
DOI: 10.5281/zenodo.7900080
title: PointFaces: Point Cloud Dataset of Facial Landmarks and Blendshapes

description: The dataset is based on 1000 facial landmark captures performed on the FFHQ facial image dataset.<br> The 3D blendshape models are sculpted based on the MediaPipe canonical model.
DOI: 10.5281/zenodo.7900236
title: Reference Inclusion

description: Analysis spreadsheet for inclusion or not of works in the systematic mapping
DOI: 10.5281/zenodo.7900235
title: Reference Inclusion

description: Analysis spreadsheet for inclusion or not of works in the systematic mapping
DOI: 10.5281/zenodo.7900231
title: AAPM TG 119 test

description: A test dataset of TG 119 to the AAPM test community page
DOI: 10.5281/zenodo.7900232
title: AAPM TG 119 test

description: A test dataset of TG 119 to the AAPM test community page
DOI: 10.34740/kaggle/dsv/5609865
title: Bee Dataset BUT-1
DOI: 10.5281/zenodo.7900278
title: evasterias_troschelii_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7900277
title: evasterias_troschelii_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7892480
title: Landing Gear System Models

description: The dataset contains a translated Simulink model, of the landing gear system introduced by Boniol and Wiels (2014) and Boniol et al. (2017). Additionally, corresponding SAML and NuSMV models are included, which were created from the Simulink model using tool support.
description: {"references": ["Boniol, F., Wiels, V. (2014). The Landing Gear System Case Study. In: Boniol, F., Wiels, V., Ait Ameur, Y., Schewe, KD. (eds) ABZ 2014: The Landing Gear Case Study. ABZ 2014. Communications in Computer and Information Science, vol 433. Springer, Cham. https://doi.org/10.1007/978-3-319-07512-9_1", "Boniol, F., Wiels, V., A\u00eft-Ameur, Y. et al. The landing gear case study: challenges and experiments. Int J Softw Tools Technol Transfer 19, 133\u2013140 (2017). https://doi.org/10.1007/s10009-016-0431-4"]}
DOI: 10.5281/zenodo.7892479
title: Landing Gear System Models

description: The dataset contains a translated Simulink model, of the landing gear system introduced by Boniol and Wiels (2014) and Boniol et al. (2017). Additionally, corresponding SAML and NuSMV models are included, which were created from the Simulink model using tool support.
description: {"references": ["Boniol, F., Wiels, V. (2014). The Landing Gear System Case Study. In: Boniol, F., Wiels, V., Ait Ameur, Y., Schewe, KD. (eds) ABZ 2014: The Landing Gear Case Study. ABZ 2014. Communications in Computer and Information Science, vol 433. Springer, Cham. https://doi.org/10.1007/978-3-319-07512-9_1", "Boniol, F., Wiels, V., A\u00eft-Ameur, Y. et al. The landing gear case study: challenges and experiments. Int J Softw Tools Technol Transfer 19, 133\u2013140 (2017). https://doi.org/10.1007/s10009-016-0431-4"]}
DOI: 10.5281/zenodo.7900296
title: melibe_leonina_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7900295
title: melibe_leonina_spades_01.fasta.gz

description: .
DOI: 10.6073/pasta/24a0dfdcaa342e58059a37862b41ec85
title: Daily Meteorological data from East Peak, Sabana, and Bisley the Luquillo Experimental Forest

description: This data publication contains daily means from laser ceilometer data from Sabana, ozone data from Bisley, and meteorological data collected from Bisley and the mountain top station of East Peak, all located on the Luquillo Experimental Forest (El Yunque National Forest) in Puerto Rico. Atmospheric data include: mean cloud observed frequency, mean lowest height cloud (cloud base), mean daytime mixing layer height observed frequency, mean lowest mixing layer height from hours 7am and 7pm only, and mean daytime mixing layer height collected from February 2013 through April 2021. Also included is mean ozone amount collected from April 2008 through early April 2021. The cloud and mixing layer frequency and low values were calculated using the Automated Surface Observing System (ASOS) method employed at airports in the area. Weather data include high elevation East Peak mean northeast wind speed (wind rose quartiles 45° to 90°), mean southeast wind speed (wind rose quartiles 90° to 135°), and mean total wind speed measured from October 2009 through 2020 (and a few months in 2021). Additional weather data collected from January 2002 through mid October 2020 include mid elevation Bisley mean precipitation, mean relative humidity, and mean temperature. A subset of these data are here: https://doi.org/10.2737/RDS-2022-0050 please use this DOI when citing this dataset.
DOI: 10.6084/m9.figshare.22770140
title: Intestinal microbial data.zip

description: Intestinal microbial data
DOI: 10.6084/m9.figshare.22770140.v1
title: Intestinal microbial data.zip

description: Intestinal microbial data
DOI: 10.5281/zenodo.7899943
title: Unlabeled Sentinel 2 time series dataset (training, T31UEP): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

description: This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles). In this repo,<strong> only data from the S2 tile T31UEP</strong> are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924 <strong>Global unlabeled dataset description</strong> Dataset name S2 tiles ROI size Temporal extent Train T30TXT,T30TYQ,T30TYS,T30UVU, T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP 1024*1024 2018-2020 Val T30TYR,T30UWU,T31TEK,T31UER 256*256 2016-2019
DOI: 10.5281/zenodo.7899942
title: Unlabeled Sentinel 2 time series dataset (training, T31UEP): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

description: This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles). In this repo,<strong> only data from the S2 tile T31UEP</strong> are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924 <strong>Global unlabeled dataset description</strong> Dataset name S2 tiles ROI size Temporal extent Train T30TXT,T30TYQ,T30TYS,T30UVU, T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP 1024*1024 2018-2020 Val T30TYR,T30UWU,T31TEK,T31UER 256*256 2016-2019