cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8266949
title: Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"

description: This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md. code This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs. result This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects. <br> Distribution of dfvc pairs.xlsx This file includes 6,926 DFVC pairs and their root causes. Tested framework versions.xlsx This file includes the framework versions tested and the Python versions that the framework versions are compatible with. Tested projects.xlsx This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.
DOI: 10.5281/zenodo.8266950
title: Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"

description: This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md. code This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs. result This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects. <br> Distribution of dfvc pairs.xlsx This file includes 6,926 DFVC pairs and their root causes. Tested framework versions.xlsx This file includes the framework versions tested and the Python versions that the framework versions are compatible with. Tested projects.xlsx This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.
DOI: 10.5281/zenodo.8339606
title: Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"

description: This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md. code This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs. result This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects. <br> Distribution of dfvc pairs.xlsx This file includes 6,926 DFVC pairs and their root causes. Tested framework versions.xlsx This file includes the framework versions tested and the Python versions that the framework versions are compatible with. Tested projects.xlsx This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.
DOI: 10.5281/zenodo.8339773
title: Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"

description: This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md. code This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs. result This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects. <br> Distribution of dfvc pairs.xlsx This file includes 6,926 DFVC pairs and their root causes. Tested framework versions.xlsx This file includes the framework versions tested and the Python versions that the framework versions are compatible with. Tested projects.xlsx This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.
DOI: 10.5519/qd.8j137mxh
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (343 records)
DOI: 10.26036/cnp0003432
title: Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity

description: Despite long-read sequencing enables to generate complete genomes of unculturable microbes, its high cost hinders its widespread application in large cohorts. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. We developed Pangaea to improve metagenome assembly using short-reads with physical or virtual barcodes. It adopts a deep-learning-based binning algorithm to assemble the co-barcoded reads with similar sequence contexts and abundances to improve assemblies of high- and medium-abundance microbes. Pangaea also leverages a multi-thresholding reassembly strategy to refine assembly for low-abundance microbes. We benchmarked Pangaea with linked-reads and a combination of short- and long-reads from mock communities and human gut metagenomes. Pangaea achieved significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea was also observed to generate three complete and circular NCMAGs on the human gut microbiomes.
DOI: 10.6084/m9.figshare.24119403
title: Two-layer flow phantom DCS dataset

description: This dataset contains the primary research data used in the analysis presented in the paper entitled "Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy" by Nakabayashi, M., Liu, S., Broti, N.M., Ichinose, M., and Ono, Y. (2023), published in Biomedical Optics Express.The dataset contains 1540 autocorrelation (g2) functions collected at three different source-detector separations (1, 2, 3 cm) in diffuse correlation spectroscopy (DCS). Each function represents the averaged waveform of stable observations over 60 instances, corresponding to one minute of measurement time. Measurements were made using a two-layer flow phantom. Each data set consists of three autocorrelation functions, the preset and measured flow rate combinations, and the estimated Blood Flow Index (BFI) values calculated using the homogeneous semi-infinite plate medium model. The time legend of the autocorrelation function is 2.5e-6:2.5e-6:5e-4 seconds.The dataset is in MATLAB *.mat file format and includes two components:<b>Dataset_set_intra40</b> - a 7 x 1540 matrix with the following rows:Row 1 - Shallow layer flow rate set at the pumpRow 2 - Deep layer flow rate set at the pumpRow 3 - Shallow layer flow rate measured at the phantom outletRow 4 - Deep layer flow rate measured at the phantom outletRow 5 - BFI estimated with DCS of 1 cm source-detector separationRow 6 - BFI estimated with DCS of 2 cm source-detector separationRow 7 - BFI estimated with DCS of 3 cm source-detector separation<b>ave_gtau_intra40</b> - a 10 x 501 x 1540 matrix, the first 3 x 501 data points represent autocorrelation functions obtained with DCS of 1, 2, and 3 cm source-detector separations. The length of each autocorrelation function is 501. The information in the subsequent 4 to 10 rows mirrors that of Dataset_set_intra40. In total, 3 x 1540 autocorrelation functions are recorded.For more detailed information and methodology, please refer to the published paper mentioned above.
DOI: 10.6084/m9.figshare.24119403.v1
title: Two-layer flow phantom DCS dataset

description: This dataset contains the primary research data used in the analysis presented in the paper entitled "Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy" by Nakabayashi, M., Liu, S., Broti, N.M., Ichinose, M., and Ono, Y. (2023), published in Biomedical Optics Express.The dataset contains 1540 autocorrelation (g2) functions collected at three different source-detector separations (1, 2, 3 cm) in diffuse correlation spectroscopy (DCS). Each function represents the averaged waveform of stable observations over 60 instances, corresponding to one minute of measurement time. Measurements were made using a two-layer flow phantom. Each data set consists of three autocorrelation functions, the preset and measured flow rate combinations, and the estimated Blood Flow Index (BFI) values calculated using the homogeneous semi-infinite plate medium model. The time legend of the autocorrelation function is 2.5e-6:2.5e-6:5e-4 seconds.The dataset is in MATLAB *.mat file format and includes two components:<b>Dataset_set_intra40</b> - a 7 x 1540 matrix with the following rows:Row 1 - Shallow layer flow rate set at the pumpRow 2 - Deep layer flow rate set at the pumpRow 3 - Shallow layer flow rate measured at the phantom outletRow 4 - Deep layer flow rate measured at the phantom outletRow 5 - BFI estimated with DCS of 1 cm source-detector separationRow 6 - BFI estimated with DCS of 2 cm source-detector separationRow 7 - BFI estimated with DCS of 3 cm source-detector separation<b>ave_gtau_intra40</b> - a 10 x 501 x 1540 matrix, the first 3 x 501 data points represent autocorrelation functions obtained with DCS of 1, 2, and 3 cm source-detector separations. The length of each autocorrelation function is 501. The information in the subsequent 4 to 10 rows mirrors that of Dataset_set_intra40. In total, 3 x 1540 autocorrelation functions are recorded.For more detailed information and methodology, please refer to the published paper mentioned above.
DOI: 10.6084/m9.figshare.24131103
title: <b>Essential Elements of Physical Fit</b><b>ness Analysis</b><b>in Male Adolescent Athletes using Machine Learning</b>

description: adolsecent physical fitness data.
DOI: 10.17909/ffv8-5279
title: Data from paper

description: The Chicago-Carnegie Hubble Program (CCHP) has obtained JWST imaging of the SN Ia host galaxies NGC 7250, NGC 4536, and NGC 3972.
DOI: 10.5517/ccdc.csd.cc260gj7
title: CCDC 2026238: Experimental Crystal Structure Determination

description: Related Article: Jing Wang, Shuping Xu, Houyu Zhang, Lei Chen, Shulin Guan, Weiqing Xu|2021|Cryst.Growth Des.|21|2699|doi:10.1021/acs.cgd.0c01586
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21214636
title: Statistical shape representation of the thoracic aorta: accounting for major branches of the aortic arch

description: Statistical shape modeling (SSM) is an emerging tool for risk assessment of thoracic aortic aneurysm. However, the head branches of the aortic arch are often excluded in SSM. We introduced an SSM strategy based on principal component analysis that accounts for aortic branches and applied it to a set of patient scans. Computational fluid dynamics were performed on the reconstructed geometries to identify the extent to which branch model accuracy affects the calculated wall shear stress (WSS) and pressure. Surface-averaged and location-specific values of pressure did not change significantly, but local WSS error was high near branches when inaccurately modeled.
DOI: 10.6084/m9.figshare.21214636.v1
title: Statistical shape representation of the thoracic aorta: accounting for major branches of the aortic arch

description: Statistical shape modeling (SSM) is an emerging tool for risk assessment of thoracic aortic aneurysm. However, the head branches of the aortic arch are often excluded in SSM. We introduced an SSM strategy based on principal component analysis that accounts for aortic branches and applied it to a set of patient scans. Computational fluid dynamics were performed on the reconstructed geometries to identify the extent to which branch model accuracy affects the calculated wall shear stress (WSS) and pressure. Surface-averaged and location-specific values of pressure did not change significantly, but local WSS error was high near branches when inaccurately modeled.
DOI: 10.34808/w2b8-p035
title: XRD-TiO2 and SiO2

description: Data contain results from XRD measurements of amorphous silica and TiO2 of antase and rutile phases. The commercial TiO2 named as P25 produced by Evonik was also analyzed.
DOI: 10.6084/m9.figshare.24130395
title: Datasets for neighbor-dependent gene expression analysis with CellNeighborEX

description: The following datasets were used for CellNeighborEX:<b>SSembryo_log_data.txt</b> is the pre-processed expression data file in the mouse embryo Slide-seq data (Puck_190926_03). <b>SSembryo_cell_id.txt</b> contains information on the bead barcodes in the embryo data. <b>SSembryo_gene_name.txt</b> is the list of top 2000 genes plus cell type markers for the embryo data.<b>SSembryo_RCTD.csv</b> is the data file produced based on the result of deconvolution tool RCTD for the embryo data.<b>SSembryo_abbrev.csv</b> includes the abbreviations for cell types in the embryo data.<b>SShippo_log_data.txt</b> is the pre-processed expression data file in the mouse hippocampus Slide-seq data (Puck_200115_08). <b>SShippo_cell_id.txt </b>contains information on the bead barcodes in the hippocampus data. <b>SShippo_gene_name.txt</b> is the list of top 2000 genes plus cell type markers for the hippocampus data.<b>SShippo_RCTD.csv</b> is the data file produced based on the result of deconvolution tool RCTD for the hippocampus data.<b>SShippo_abbrev.csv</b> includes the abbreviations for cell types in the hippocampus data.<b>SSliver_log_data.txt </b>is the pre-processed expression data file in the mouse liver cancer Slide-seq data (mouse_liver_met_2_rna_201002_04). <b>SSliver_cell_id.txt</b> contains information on the bead barcodes in the liver cancer data. <b>SSliver_gene_name.txt</b> is the list of top 2000 genes plus cell type markers for the liver cancer data.<b>SSliver_RCTD.csv</b> is the data file produced based on the result of deconvolution tool RCTD for the liver cancer data.
DOI: 10.6084/m9.figshare.23987895.v1
title: Identification of Fusarium Basal Rot Pathogens of Onion and Evaluation of Fungicides against the Pathogens

description: Onion (<i>Allium cepa</i> L.) is an economically important vegetable crop worldwide. However, various fungal diseases, including Fusarium basal rot (FBR), neck rot, and white rot, reduce onion production or bulb storage life. FBR caused by <i>Fusarium</i> species is among the most destructive onion diseases. In this study, we identified <i>Fusarium</i> species associated with FBR in Jeolla and Gyeongsang Provinces in South Korea and evaluated fungicides against the pathogens. Our morphological and molecular analyses showed that FBR in onions is associated with <i>Fusarium commune</i>, <i>Fusarium oxysporum</i>, and <i>Fusarium proliferatum</i>. We selected seven fungicides (fludioxonil, hexaconazole, mandestrobin, penthiopyrad, prochloraz-manganese, pydiflumetofen, and tebuconazole) and evaluated their inhibitory effects on mycelial growth of the pathogens at three different concentrations (0.01, 0.1, and 1 mg/mL). We found that prochloraz-manganese was highly effective, inhibiting 100% of the mycelial growth of the pathogens at all concentrations, followed by tebuconazole. Fludioxonil showed &lt; 50% inhibition at 1 mg/mL for the tested isolates.
DOI: 10.15151/esrf-es-1305996121
title: FInnish National Protein Crystallography Consortium

description: The Finnish BAG encompasses 24 groups active in crystallography in Finland. The projects vary widely and include membrane proteins and protein complexes, other proteins involved in disease and antibiotics and drug development and discovery (endoplasmic regulation and protein secretion, ADP-ribosylation and several oncogenic proteins including kinases, biocatalysis) and several pathogen proteins and various enzyme targets and e.g. studies on phytochrome photoreceptors and complexes involved in actin dynamics regulation. Overall the BAG serves the national needs for highly variable projects, with different biological and biotechnological applications. Several project will benefit from microfocus beam lines and tunable wavelength options for phasing, others benefit from quick robust screening by MASSIF.
DOI: 10.15151/esrf-es-1305996855
title: Belgium BAG

description: The Belgium BAG includes groups situated at four Flemish universities: Antwerpen, Brussels, Ghent and Leuven. The proposed projects are highly diverse, including viral, prokaryotic and eukaryotic proteins with a strong focus on targets and processes of biomedical relevance. Many projects deal with membrane proteins, cell surface receptors and protein-protein complexes. Central to the research in the BAG associated groups is the employment of integrative methods in structural biology combining X-ray crystallography, SAXS, and electron microscopy. Many crystals in the various proposed projects are small in size and radiation sensitive, requiring the use of the high brilliance MX beam lines, preferably with micro-focused X-rays.
DOI: 10.15151/esrf-es-1330257011
title: Beamline realignment X-ray nano-holotomography of diverse materials

description: Beamline realignment X-ray nano-holotomography of diverse materials
DOI: 10.15151/esrf-es-1324919892
title: Assessing the beam damage on reference AlN/SiC thin layers

description: Assessing the beam damage on reference AlN/SiC thin layers on EH3
DOI: 10.14469/hpc/13227
title: Endo_TS1_G16_PCM_dichloroethane_def2qzvpp_v_01, + dispersion correction, G = -823.651465, GN = 0.000003155

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13231
title: Endo_Reactant_G16_PCM_dichloroethane_def2qzvpp_v_01_ + dispersion correction, G = -823.661726, GN 0.000015873

description: Gaussian 16 (C01) calculation
DOI: 10.5517/ccdc.csd.cc2gjhp6
title: CCDC 2279978: Experimental Crystal Structure Determination

description: Related Article: Lu-Lu Ma, Nan An, Fu-An Guo, Hao Wang, Guo-Ping Yang, Yao-Yu Wang|2023|Inorg.Chem.|62|13698|doi:10.1021/acs.inorgchem.3c02308
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5519/qd.0pjvc7v3
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.8336963
title: Data set for early carpal tunnel syndrome diagnostic

description: This dataset represents the minimum dataset to replicate the results obtained in the article "A coupled electro-mechanical approach for early diagnosis of carpal tunnel syndrome" by Saveliy Peshin, Julia Karakulova, Alex G. Kuchumov. The dataset includes 3D models used in FEM calculations, Python-based code for a motion capture program, MatLab-based code for determining the conductance of a deformed median nerve, and MRI images used to construct 3D geometries. All folders have a README text file that contains a detailed description of usage.
DOI: 10.5281/zenodo.8336962
title: Data set for early carpal tunnel syndrome diagnostic

description: This dataset represents the minimum dataset to replicate the results obtained in the article "A coupled electro-mechanical approach for early diagnosis of carpal tunnel syndrome" by Saveliy Peshin, Julia Karakulova, Alex G. Kuchumov. The dataset includes 3D models used in FEM calculations, Python-based code for a motion capture program, MatLab-based code for determining the conductance of a deformed median nerve, and MRI images used to construct 3D geometries. All folders have a README text file that contains a detailed description of usage.
DOI: 10.6084/m9.figshare.24124863
title: Effect of variable light on plant and algae growth - Master database

description: Master database containing all information recorded about each effect size and study within the database.For more information, please see the explanation tab within the file.
DOI: 10.57841/casdc.0002183
title: N/P无卤素离子液体润滑剂的链长与摩擦学性能的关系
DOI: 10.6084/m9.figshare.24131190
title: Figure-1.CHAMBER-OA-O2C.npy

description: Data for Figure 1 of the paper.
DOI: 10.6084/m9.figshare.24131190.v1
title: Figure-1.CHAMBER-OA-O2C.npy

description: Data for Figure 1 of the paper.
DOI: 10.6084/m9.figshare.24131193
title: Figure-2.OA-MAKEUP.npy

description: Data for Figure 2 of the paper.
DOI: 10.6084/m9.figshare.24131196
title: Figure-3.PLUME-OUTPUTS.BASE.npy

description: Data for Figure 3 of the paper.
DOI: 10.6084/m9.figshare.24131196.v1
title: Figure-3.PLUME-OUTPUTS.BASE.npy

description: Data for Figure 3 of the paper.
DOI: 10.6084/m9.figshare.23627973
title: <strong>False Discovery Rate adjustment by the Benjamini-Hochberg procedure of supplementary table 3. </strong> <em>Longitudinal patterns of breastfeeding and its association adiposity and subjective indicators of satiety/appetite in the first 2 years of life.</em>

description: This dataset is part of the supplementary material in the manuscript entitled "Longitudinal patterns of breastfeeding and its association adiposity and subjective indicators of satiety/appetite in the first 2 years of life".
DOI: 10.6084/m9.figshare.23627973.v1
title: <strong>False Discovery Rate adjustment by the Benjamini-Hochberg procedure of supplementary table 3. </strong> <em>Longitudinal patterns of breastfeeding and its association adiposity and subjective indicators of satiety/appetite in the first 2 years of life.</em>

description: This dataset is part of the supplementary material in the manuscript entitled "Longitudinal patterns of breastfeeding and its association adiposity and subjective indicators of satiety/appetite in the first 2 years of life".
DOI: 10.6084/m9.figshare.24131205.v1
title: Figure-4.ATMOS-OUTPUTS.npy

description: Data for Figure 4 of the paper.
DOI: 10.6084/m9.figshare.24131205
title: Figure-4.ATMOS-OUTPUTS.npy

description: Data for Figure 4 of the paper.
DOI: 10.5281/zenodo.8198959
title: Annex G – Exposure Mineral Oil Aromatic Hydrocarbons (MOAH)

description: The annex contains the chronic dietary exposure estimations for MOAH.
description: EU; en ; xlsx; feedco@efsa.europa.eu
DOI: 10.5281/zenodo.8198960
title: Annex G – Exposure Mineral Oil Aromatic Hydrocarbons (MOAH)

description: The annex contains the chronic dietary exposure estimations for MOAH.
description: EU; en ; xlsx; feedco@efsa.europa.eu