cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8200101
title: Submitted forecasts and analysis code for "Predicting spring phenology in deciduous broadleaf forests: NEON Phenology Forecasting Community Challenge"

description: Submitted forecasts for the 2021 Ecological Forecasting Initiative NEON Phenology Forecast Challenge and the analysis code for the accompanying manuscript.
DOI: 10.5281/zenodo.8200100
title: Submitted forecasts and analysis code for "Predicting spring phenology in deciduous broadleaf forests: NEON Phenology Forecasting Community Challenge"

description: Submitted forecasts for the 2021 Ecological Forecasting Initiative NEON Phenology Forecast Challenge and the analysis code for the accompanying manuscript.
DOI: 10.5281/zenodo.8191241
title: Market Power in an Emerging Market Economy: Evidence from Philippine Publicly Listed Firms

description: The STATA data file uploaded is used in the paper: "Market Power in an Emerging Market Economy: Evidence from Philippine Publicly Listed Firms." The dataset contains annual financial statements of 285 non-bank firms listed in the Philippine Stock Exchange from 2001 to 2019.
DOI: 10.5281/zenodo.8191240
title: Market Power in an Emerging Market Economy: Evidence from Philippine Publicly Listed Firms

description: The STATA data file uploaded is used in the paper: "Market Power in an Emerging Market Economy: Evidence from Philippine Publicly Listed Firms." The dataset contains annual financial statements of 285 non-bank firms listed in the Philippine Stock Exchange from 2001 to 2019.
DOI: 10.5519/qd.08ohehl9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.23808600
title: Table S1_RAS-STCs

description: <i>Inflammatory genes with significantly higher or lower 5mC levels in RAS-STCs compared to Normal-STCs</i>
DOI: 10.6084/m9.figshare.23808600.v1
title: Table S1_RAS-STCs

description: <i>Inflammatory genes with significantly higher or lower 5mC levels in RAS-STCs compared to Normal-STCs</i>
DOI: 10.5517/cc8jrcw
title: CCDC 254428: Experimental Crystal Structure Determination

description: Related Article: Yun Gong|2009|CSD Communication|||
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.23808612
title: Table S2_RAS-STC

description: <i>Inflammatory genes with significantly higher or lower 5hmC levels in RAS-STCs compared to Normal-STCs</i>
DOI: 10.6084/m9.figshare.23808612.v1
title: Table S2_RAS-STC

description: <i>Inflammatory genes with significantly higher or lower 5hmC levels in RAS-STCs compared to Normal-STCs</i>
DOI: 10.5281/zenodo.8190202
title: PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images

description: We release two datasets we used in the paper "PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images". These two datasets are from aerial images and LiDAR, the detailed information can be found on Github : https://github.com/whuwuteng/PSMNet-FusionX3. These datasets can be also used for Deep learning stereo-dense matching.
DOI: 10.5281/zenodo.8200023
title: PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images

description: We release two datasets we used in the paper "PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images". These two datasets are from aerial images and LiDAR, the detailed information can be found on Github : https://github.com/whuwuteng/PSMNet-FusionX3. These datasets can be also used for Deep learning stereo-dense matching.
DOI: 10.5281/zenodo.8190201
title: PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images

description: We release two datasets we used in the paper "PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images". These two datasets are from aerial images and LiDAR, the detailed information can be found on Github : https://github.com/whuwuteng/PSMNet-FusionX3. These datasets can be also used for Deep learning stereo-dense matching.
DOI: 10.6084/m9.figshare.23808627
title: RAS-STC inflammation (Table S1)

description: Table S1
DOI: 10.6084/m9.figshare.23808627.v1
title: RAS-STC inflammation (Table S1)

description: Table S1
DOI: 10.5281/zenodo.7613817
title: Replication Package for Paper "How Early Participation Determines Long-Term Sustained Activity in GitHub Projects"

description: This replication package can be used for replicating results in the paper. It contains 1) a dataset of 290,255 repositories; and 2) Python scripts for training and interpreting models. We recommend manually setup the required environment in a commodity Linux machine with at least 1 CPU Core, 8GB Memory and 100GB empty storage space. We conduct development and execute all our experiments on a Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage. We use GHTorrent to restore historical states of 290,255 repositories with more than 57 commits, 4 PRs, 1 issue, 1 fork and 2 stars. The raw data of repositories (collected in their first 1,3,5 months(s)) are stored in `Replication Package/data/prodata_1.pkl`, `Replication Package/data/prodata_3.pkl`, and `Replication Package/data/prodata_5.pkl`. The contribution of features resulting from LIME model is stored in `Replication Package/data/limeres_m3_t2_k1.pkl`. `Replication Package/data/X_test_m3_t2_k1.pkl` and `Replication Package/data/y_test_m3_t2_k1.pkl` store the test dataset for the LIME model. You can run `Replication Package/fitdata.py` to get the results in Table 2 and 3, run `Replication Package/draw_compare_variable.py` to get Figure 2 and run `Replication Package/allvari_statistics.py` to get Table 4. In `Replication Package/Variable_comparison_with_different_parameter.pdf`, we show the LIME results under different parameters. In `Replication Package/sample_pros.csv`, we also provide the list of randomly selected repositories in Section 3.1.
DOI: 10.5281/zenodo.7613816
title: Replication Package for ESEC/FSE 2023 Paper "How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?"

description: This replication package can be used for replicating results in the paper. It contains 1) a dataset of 290,255 repositories; and 2) Python scripts for training and interpreting models. The GitHub repository of the paper is available at https://github.com/mcxwx123/Sustainable_projects. We recommend manually setup the required environment in a commodity Linux machine with at least 1 CPU Core, 8GB Memory and 100GB empty storage space. We conduct development and execute all our experiments on a Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage. We use GHTorrent to restore historical states of 290,255 repositories with more than 57 commits, 4 PRs, 1 issue, 1 fork and 2 stars. The raw data of repositories (collected in their first 1,3,5 months(s)) are stored in `Replication Package/data/prodata_1.pkl`, `Replication Package/data/prodata_3.pkl`, and `Replication Package/data/prodata_5.pkl`. The contribution of features resulting from LIME model is stored in `Replication Package/data/limeres_m3_t2_k1.pkl`.<br> `Replication Package/data/X_test_m3_t2_k1.pkl` and `Replication Package/data/y_test_m3_t2_k1.pkl` store the test dataset for the LIME model. You can run `Replication Package/fitdata.py` to get the results in Table 3 and 4, run `Replication Package/draw_compare_variable.py` to get Figure 2 and run `Replication Package/allvari_statistics.py` to get Table 5. In `Replication Package/Variable_comparison_with_different_parameter.pdf`, we show the LIME results under different parameters. In `Replication Package/sample_pros.csv`, we also provide the list of randomly selected repositories in Section 3.1.<br> The explanations for collecting the variables, the examples of variable effects on project sustainability, and the hyperparameter setting of the machine learning models are provided in the README.md file.
DOI: 10.5281/zenodo.8098994
title: Replication Package for ESEC/FSE 2023 Paper "How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?"

description: This replication package can be used for replicating results in the paper. It contains 1) a dataset of 290,255 repositories; and 2) Python scripts for training and interpreting models. The GitHub repository of the paper is available at https://github.com/mcxwx123/Sustainable_projects. We recommend manually setup the required environment in a commodity Linux machine with at least 1 CPU Core, 8GB Memory and 100GB empty storage space. We conduct development and execute all our experiments on a Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage. We use GHTorrent to restore historical states of 290,255 repositories with more than 57 commits, 4 PRs, 1 issue, 1 fork and 2 stars. The raw data of repositories (collected in their first 1,3,5 months(s)) are stored in `Replication Package/data/prodata_1.pkl`, `Replication Package/data/prodata_3.pkl`, and `Replication Package/data/prodata_5.pkl`. The contribution of features resulting from LIME model is stored in `Replication Package/data/limeres_m3_t2_k1.pkl`.<br> `Replication Package/data/X_test_m3_t2_k1.pkl` and `Replication Package/data/y_test_m3_t2_k1.pkl` store the test dataset for the LIME model. You can run `Replication Package/fitdata.py` to get the results in Table 3 and 4, run `Replication Package/draw_compare_variable.py` to get Figure 2 and run `Replication Package/allvari_statistics.py` to get Table 5. In `Replication Package/Variable_comparison_with_different_parameter.pdf`, we show the LIME results under different parameters. In `Replication Package/sample_pros.csv`, we also provide the list of randomly selected repositories in Section 3.1.<br> The explanations for collecting the variables, the examples of variable effects on project sustainability, and the hyperparameter setting of the machine learning models are provided in the README.md file.
DOI: 10.6084/m9.figshare.23808636
title: RAS-STC inflammation 2

description: Table S2
DOI: 10.6084/m9.figshare.23808636.v1
title: RAS-STC inflammation 2

description: Table S2
DOI: 10.5281/zenodo.8196892
title: PETALS

description: The sources and datasets of PETALS.
DOI: 10.5281/zenodo.8196891
title: PETALS

description: The sources and datasets of PETALS.
DOI: 10.5281/zenodo.8199999
title: BASiC Dataset (Raw Data)

description: <strong>UAV’s Sensor’s Failures Dataset: Biomisa Arducopter Sensory Critique (BASiC): </strong>Raw Data Logs
DOI: 10.5281/zenodo.8199998
title: BASiC Dataset (Raw Data)

description: <strong>UAV’s Sensor’s Failures Dataset: Biomisa Arducopter Sensory Critique (BASiC): </strong>Raw Data Logs
DOI: 10.6084/m9.figshare.23808668
title: A Cautionary Note about Having the Right Mixture Model but Classifying the Wrong People

description: Mixture models can be used for explanation or individual prediction and classification. In practice, researchers are often tempted to make the class membership manifest by classifying cases according to their class of maximum posterior probability and using the “observed” class membership directly or as a variable in follow-up analyses to predict distal outcomes. This study revisits the issue of correct class assignment in latent profile analysis by providing an example where the number of classes is known (3-classes), sampling variability is eliminated, and precise estimates of classification indices are provided. This pseudo-population study design assumes the data-generating mechanism is known and provides a “best-case” scenario for evaluating correct class assignment. We use a variety of classification indices and graphical displays to show that correct classification may be poor despite relatively high entropy and overall correct class assignment metrics (e.g., percent correct). Our study serves as a reminder of the risks associated with trying to make latent class memberships manifest.
DOI: 10.6084/m9.figshare.23808668.v1
title: A Cautionary Note about Having the Right Mixture Model but Classifying the Wrong People

description: Mixture models can be used for explanation or individual prediction and classification. In practice, researchers are often tempted to make the class membership manifest by classifying cases according to their class of maximum posterior probability and using the “observed” class membership directly or as a variable in follow-up analyses to predict distal outcomes. This study revisits the issue of correct class assignment in latent profile analysis by providing an example where the number of classes is known (3-classes), sampling variability is eliminated, and precise estimates of classification indices are provided. This pseudo-population study design assumes the data-generating mechanism is known and provides a “best-case” scenario for evaluating correct class assignment. We use a variety of classification indices and graphical displays to show that correct classification may be poor despite relatively high entropy and overall correct class assignment metrics (e.g., percent correct). Our study serves as a reminder of the risks associated with trying to make latent class memberships manifest.
DOI: 10.6084/m9.figshare.23808651
title: SURFACE AREA ANALYSIS.docx

description: surface analysis of titanium implant surface treated with diode laser with different powers
DOI: 10.6084/m9.figshare.23808645.v1
title: Hydrophilicity analysis .docx

description: the effect of different diode laser powers on the hydrophilicity of the implant surface.<br>
DOI: 10.6084/m9.figshare.23808645
title: Hydrophilicity analysis .docx

description: the effect of different diode laser powers on the hydrophilicity of the implant surface.<br>
DOI: 10.5281/zenodo.7625142
title: Primary data – 13C Solid-State Buildup and Microwave Sweep Profiles in a DNP Polarizer

description: These primary datasets consist of: 1. 13C polarization buildup data in solid-state 2. 13C microwave profiles in solid state Please consult the Archive Guide.
DOI: 10.5281/zenodo.8199624
title: Dataset exploring the use of quasi-harmonic approximation to understand the thermal properties of Bi2Se3

description: This data set contains input and output files for DFT calculations on Bi<sub>2</sub>Se<sub>3</sub> for a number of different fixed unit cells with calculations performed using VASP and Phonopy. At each fixed volume, optimisations and phonon calculations have been performed. These have been used to understand the thermal properties of the material using the quasi-harmonic approximation.
DOI: 10.5281/zenodo.8199623
title: Dataset exploring the use of quasi-harmonic approximation to understand the thermal properties of Bi2Se3

description: This data set contains input and output files for DFT calculations on Bi<sub>2</sub>Se<sub>3</sub> for a number of different fixed unit cells with calculations performed using VASP and Phonopy. At each fixed volume, optimisations and phonon calculations have been performed. These have been used to understand the thermal properties of the material using the quasi-harmonic approximation.
DOI: 10.5281/zenodo.8200168
title: CELLOIDS: pH-responsive DOPC Giant Unilamellar Vesicles with and without Linoleic Acid

description: As part of the CELLOIDS project, one of our main research focuses is exploring the feasibility of making the microrobot's membrane responsive to a pH gradient. Specifically, we investigated the incorporation of linoleic acid into Giant unilamellar vesicles composed of DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine). The provided dataset compiles the diameters of liposomes prepared with DOPC (3.18 mM), DOPC:LA (linoleic acid) at a molar ratio of 100:1, and DOPC:LA at a molar ratio of 50:1. These liposomes were placed in buffers at both pH 6 and pH 7, simulating conditions similar to the tumor microenvironment. Our objective was to assess the stability of these liposomes under these conditions. In detail, we carefully prepared liposomes with varying molar ratios of DOPC and linoleic acid to determine the optimal amount of linoleic acid that induces membrane deformation while maintaining the structural integrity of the liposomes in acidic solutions. Subsequently, we subjected the liposomes to testing at the specified concentrations and compared their behavior under two different pH values. Finally, we observed the samples using a phase-contrast microscope immediately after the addition of the solutions and after a one-hour incubation period.
DOI: 10.5281/zenodo.8199855
title: Annotation Data: Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology

description: <strong>Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology</strong> Genome annotation data <strong>Genome Browser:</strong> https://plantgenomics.ncc.unesp.br/gen.php?id=Asub
DOI: 10.5281/zenodo.8199854
title: Annotation Data: Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology

description: <strong>Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology</strong> Genome annotation data <strong>Genome Browser:</strong> https://plantgenomics.ncc.unesp.br/gen.php?id=Asub
DOI: 10.5281/zenodo.8200152
title: Cognitive, behavioral, and psychological phenotypes in small fiber neuropathy: a case-control study - Submitted

description: Clinical dataset containing pseudoanonymized data on a cohort of small fiber neuropathy patients included in the study at title
DOI: 10.5281/zenodo.8200151
title: Cognitive, behavioral, and psychological phenotypes in small fiber neuropathy: a case-control study - Submitted

description: Clinical dataset containing pseudoanonymized data on a cohort of small fiber neuropathy patients included in the study at title
DOI: 10.5281/zenodo.7029644
title: Geostationary Satellite Surface Gaseous Pollutants Dataset - CO

description: High spatio-temporal resolution CO dataset of China's Surface based on the Geostationary satellite Himawari-8 (2015.09 - 2021.08)
DOI: 10.6084/m9.figshare.21256365.v1
title: Fine-structure relaxation

description: Collisional relaxation rates for N+ and H and O2+ and H. Data presented in LAMDA format.
DOI: 10.7910/dvn/vfszxb
title: Caracterização dos processos de sorção de agrotóxicos em resíduos plásticos em sistemas de água doce em escala de laboratório, mesocosmo e amostras ambientais

description: Experimental data generated during the execution of Project # 2021/12484-9 sponsored by FAPESP in the Department of Analytical Chemistry at the State University of Campinas. Data will become available after the publication of a scientific paper that contains figures generated from this dataset.
DOI: 10.5517/ccdc.csd.cc293jdc
title: CCDC 2118552: Experimental Crystal Structure Determination

description: Related Article: Sagarmani Rasaily, Debesh Sharma, Sajan Pradhan, Nilankar Diyali, Shivanand Chettri, Bikram Gurung, Sudarsan Tamang, Anand Pariyar|2022|Inorg.Chem.|61|13685|doi:10.1021/acs.inorgchem.2c00270
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: 10.5517/ccdc.csd.cc2dx9yl
title: CCDC 2231750: Experimental Crystal Structure Determination

description: Related Article: M. K. Gond, Nilesh Rai, Brijesh Chandra, Vibhav Gautam, Somenath Garai, R. J. Butcher, M. K. Bharty|2023|Dalton Trans.|52|10213|doi:10.1039/D3DT01388J
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: 10.5517/ccdc.csd.cc2f6rdt
title: CCDC 2240816: Experimental Crystal Structure Determination

description: Related Article: Andrey Yu. Sosorev, Igor I. Ponomarev, Dmitry I. Dominskiy, Konstantin A. Lyssenko, Olga D. Parashchuk, Vasily A. Trukhanov, Vladislav G. Konstantinov, Nikita O. Dubinets, Dmitry Yu. Paraschuk|2023|Phys.Chem.Chem.Phys.(PCCP)|25|19562|doi:10.1039/D3CP01582C
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: 10.5281/zenodo.8196541
title: Raw Data Examples for Close-Range Photogrammetry of Replicative Experiments on Ground Stone Tools

description: The data presented in this study pertains to the experimental collection built to verify the applicability of close-range photogrammetry to ground stone tools (GSTs). GS17 is a sandstone slab collected from the Fiora River in Manciano, Italy, where sandstone of Miocene formation outcrops. This stone is primarily composed of quartz grains embedded in abundant carbonate cement. It was utilized as a GST in replicative experiments, paired with an active tool from the same provenience, and subjected to 2 hours of<em> Rumex crispus</em> achenes grinding. Photogrammetric techniques based on Structure-from-Motion and Multi-View Stereo reconstruction were employed to generate the 3D models of the tool at different stages of the replicative use. We recorded the geometry of the GST before (referred to as T<sub>0</sub>) and after use (T<sub>4</sub>) to later compare and assess the change in object geometry during the transformation of vegetal resources, thereby facilitating our understanding of archaeological tools. This dataset comprises the pictures required for the elaboration of the GS17 models at T<sub>0</sub> and T<sub>4</sub>, as well as the data necessary for calibration.
description: This data are part of G.S. PhD research that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 754511.
description: {"references": ["Sorrentino et al. 2023. PLOS ONE, DOI:10.1371/journal.pone.0289807"]}