cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8399829
title: Dataset for 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations'

description: This dataset is associated with the research entitled: 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. It contains raw data from six low-cost sensor loggers and one anemometer, collected during an experiment consisting of a series of controlled releases conducted in October 2019 at the TADI (TotalEnergies Anomaly Detection Initiative) platform. <strong>Dataset Structure:</strong> - `time`: Timestamp, marking the exact time the data was collected.<br> - `CH4`: Methane concentration measured by the reference instrument in parts per million (ppm).<br> - `2611C`: Voltage variation measured by the Figaro TGS 2611C-00 MOS sensor in volts (V).<br> - `2600`: Voltage variation measured by the Figaro TGS 2600 MOS sensor in volts (V).<br> - `2611E`: Voltage variation measured by the Figaro TGS 2611E-00 MOS sensor in volts (V).<br> - `RH_DHT22`: Relative humidity measured by the DHT22 sensor in percentage (%).<br> - `RH_SHT75`: Relative humidity measured by the SHT75 sensor in percentage (%).<br> - `T_DHT22`: Air temperature measured by the DHT22 sensor in degrees Celsius (°C).<br> - `T_SHT75`: Air temperature measured by the SHT75 sensor in degrees Celsius (°C).<br> - `T_BMP180`: Air temperature measured by the BMP180 sensor in degrees Celsius (°C).<br> - `T_BMP280`: Air temperature measured by the BMP280 sensor in degrees Celsius (°C).<br> - `P_BMP180`: Atmospheric pressure measured by the BMP180 sensor in pascals (Pa).<br> - `P_BMP280`: Atmospheric pressure measured by the BMP280 sensor in pascals (Pa).<br> - `Release`: Number of the controlled release. <strong>Acknowledgment:</strong> When using this dataset, please reference the original research paper titled ‘Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. <strong>Contact Information:</strong> Olivier Laurent (olivier.laurent@lsce.ipsl.fr)
DOI: 10.5281/zenodo.8399828
title: Dataset for 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations'

description: This dataset is associated with the research entitled: 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. It contains raw data from six low-cost sensor loggers and one anemometer, collected during an experiment consisting of a series of controlled releases conducted in October 2019 at the TADI (TotalEnergies Anomaly Detection Initiative) platform. <strong>Dataset Structure:</strong> - `time`: Timestamp, marking the exact time the data was collected.<br> - `CH4`: Methane concentration measured by the reference instrument in parts per million (ppm).<br> - `2611C`: Voltage variation measured by the Figaro TGS 2611C-00 MOS sensor in volts (V).<br> - `2600`: Voltage variation measured by the Figaro TGS 2600 MOS sensor in volts (V).<br> - `2611E`: Voltage variation measured by the Figaro TGS 2611E-00 MOS sensor in volts (V).<br> - `RH_DHT22`: Relative humidity measured by the DHT22 sensor in percentage (%).<br> - `RH_SHT75`: Relative humidity measured by the SHT75 sensor in percentage (%).<br> - `T_DHT22`: Air temperature measured by the DHT22 sensor in degrees Celsius (°C).<br> - `T_SHT75`: Air temperature measured by the SHT75 sensor in degrees Celsius (°C).<br> - `T_BMP180`: Air temperature measured by the BMP180 sensor in degrees Celsius (°C).<br> - `T_BMP280`: Air temperature measured by the BMP280 sensor in degrees Celsius (°C).<br> - `P_BMP180`: Atmospheric pressure measured by the BMP180 sensor in pascals (Pa).<br> - `P_BMP280`: Atmospheric pressure measured by the BMP280 sensor in pascals (Pa).<br> - `Release`: Number of the controlled release. <strong>Acknowledgment:</strong> When using this dataset, please reference the original research paper titled ‘Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. <strong>Contact Information:</strong> Olivier Laurent (olivier.laurent@lsce.ipsl.fr)
DOI: 10.34740/kaggle/dsv/6595083
title: Mahanagar Yatayat Transport Dataset
DOI: 10.34740/kaggle/dsv/6595101
title: MoviesDatasets
DOI: 10.57745/2hqnte
title: VIREDA.zip

description: :unav
DOI: 10.57745/02w7nm
title: README.md

description: :unav
DOI: 10.57745/qki51u
title: eclairages.pdf

description: lighting
DOI: 10.57745/t2gcpo
title: jpt-aspai22.pdf

description: Publication to be referred when using the Vireda database
DOI: 10.25345/c5pr7n48g
title: MassIVE MSV000093013 - A Novel Approach to detect Extracellular Matrix Proteins in Serum
title: Commercial and pooled (n = 20) clinical HCC or Cirrhotic patient serum was digested with collagenase. Peptides were isolated via C18 solid phase extraction and 200 ng was injected in hexaplicates through a Bruker nanoElute LC system complexed with a Aurora C18 nanoUHPLC separation column before mass spectrometry analysis on a timsTOF Flex.
DOI: 10.21979/n9/jyka5p
title: Replication Data for: Low-Temperature Atomic Layer Deposited Electron Transport Layers for Co-Evaporated Perovskite Solar Cells

description: We explore the potentials and the characteristics of electron-transporting layers (ETL) grown by atomic layer deposition (ALD) at low temperature in co-evaporated perovskite solar cells (PSCs).
DOI: 10.5281/zenodo.8113986
title: snoRNA core proteins PAR-CLIP data hg38

description: In this dataset you can find bed file of snoRNA core binding proteins including dyskerin (DKC1), NOP58, NOP56 and fibrillarin (FBL) lifted from hg19 to hg38. NOP58 repA and repB have been merged as single file , as have FBL replicats. Data were made by Kishore et al. 2013 (http://genomebiology.com/2013/14/5/R45), and The Gene Expression Omnibus (GEO) is GSE43666.
DOI: 10.5281/zenodo.8113985
title: snoRNA core proteins PAR-CLIP data hg38

description: In this dataset you can find bed file of snoRNA core binding proteins including dyskerin (DKC1), NOP58, NOP56 and fibrillarin (FBL) lifted from hg19 to hg38. NOP58 repA and repB have been merged as single file , as have FBL replicats. Data were made by Kishore et al. 2013 (http://genomebiology.com/2013/14/5/R45), and The Gene Expression Omnibus (GEO) is GSE43666.
DOI: 10.5281/zenodo.8399272
title: Cryo-EM map anisotropy can be attenuated by map post-processing and a new method for its estimation. Example Maps

description: These are the post-processed maps displayed in the manuscript "Cryo-EM map anisotropy can be attenuated by map post-processing and a new method for its estimation"
DOI: 10.5281/zenodo.8399271
title: Cryo-EM map anisotropy can be attenuated by map post-processing and a new method for its estimation. Example Maps

description: These are the post-processed maps displayed in the manuscript "Cryo-EM map anisotropy can be attenuated by map post-processing and a new method for its estimation"
DOI: 10.5281/zenodo.8400019
title: Data & figures: Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation

description: These are the model data, key figures, and Python code generated during the project titled “Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation." This project was undertaken during a 3-month research scholarship at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, funded by the Helmholtz Visiting Researcher Grant, a program promoted by the Helmholtz Information and Data Science Academy (HIDA). Statistical methods pertain to the coupling of sea surface temperature and Antarctic sea-ice interactions. These methods can be applied to observations, reanalysis, and earth system model data
DOI: 10.5281/zenodo.8400018
title: Data & figures: Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation

description: These are the model data, key figures, and Python code generated during the project titled “Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation." This project was undertaken during a 3-month research scholarship at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, funded by the Helmholtz Visiting Researcher Grant, a program promoted by the Helmholtz Information and Data Science Academy (HIDA). Statistical methods pertain to the coupling of sea surface temperature and Antarctic sea-ice interactions. These methods can be applied to observations, reanalysis, and earth system model data
DOI: 10.5281/zenodo.8400057
title: Data & figures: Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation

description: These are the model data, key figures, and Python code generated during the project titled “Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation." This project was undertaken during a 3-month research scholarship at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, funded by the Helmholtz Visiting Researcher Grant, a program promoted by the Helmholtz Information and Data Science Academy (HIDA). Statistical methods pertain to the coupling of sea surface temperature and Antarctic sea-ice interactions. These methods can be applied to observations, reanalysis, and earth system model data
DOI: 10.5281/zenodo.8165051
title: DKC1 eCLIP data

description: eCLIP bedfile of DKC1 from ENCODE consortium
DOI: 10.5519/qd.un7rzy18
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5335628 records)
DOI: 10.5281/zenodo.8165050
title: DKC1 eCLIP data

description: eCLIP bedfile of DKC1 from ENCODE consortium
DOI: 10.5281/zenodo.8400056
title: ChecklistOfWGFlora

description: A working checklist of the flora of the Western Ghats compiled from published studies and data repositories.
DOI: 10.5281/zenodo.8400055
title: ChecklistOfWGFlora

description: A working checklist of the flora of the Western Ghats compiled from published studies and data repositories.
DOI: 10.5519/qd.5nsa4rt6
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5066/p9w9mhcj
title: Model code to build and run an integrated population model for the endangered Cape Sable Seaside Sparrow

description: The Cape Sable Seaside Sparrow (CSSS; Ammospiza maritima mirabilis) is an endangered species that has experienced population declines of more than 60% since the 1990s. The CSSS is restricted in geographic extent to a relatively small area of remaining marl prairie in the southern Florida Everglades and remains in six subpopulation areas (named A-F). There has been over two decades of field research conducted on the CSSS across its six subpopulations, but a statistically robust analysis of the bird?s demographic parameters utilizing all data has yet to be completed. To address this gap, we conducted demographic analysis of the CSSS population using an integrated population model (IPM). The IPM provides a flexible framework to incorporate three separate CSSS data types (range-wide counts, mark-recapture, productivity) into a single model to estimate demographic processes and population trajectories. We calculated: (1) annual estimates of annual population size, survival, and productivity; (2) coefficient estimates of hydrologic and environmental variables on survival and productivity; (3) estimates of annual population growth and its correlation with demographic rates; and (4) a population viability analysis (PVA) that includes predicted population size and demographic rates 10 years into the future, and probabilities of extinction and quasi-extinction. This data release contains the code to run the IPM and PVA in addition to providing the parameter estimates from the model.
DOI: 10.5281/zenodo.7970567
title: Retail-786k Dataset

description: This dataset is a <strong>large-scale</strong> dataset for `<strong>`visual entity matching''</strong>. Entity Matching (EM) indicates the identification of semantic groupings. As it occurs like the identification of consumer products in images in retail applications. A product is a retail item on sale that is uniquely identified by the internationally standardized Global Trade Item Number (GTIN). Entities are then defined as semantic groupings of products. The dataset consists of <strong>786,179 imag</strong>es labeled with 3,298 different entities. The images are split into sub-sets of 748,715 training images and 37,464 test images. The images of the dataset are cropped from scanned advertisement leaflets, collected over several years from different European retailers. Two versions of the dataset are provided: once with the longer edge fixed to 512 and another one fixed to 256. The comprehensive dataset description can be found in the corresponding paper: https://arxiv.org/abs/2309.17164
DOI: 10.5281/zenodo.7970566
title: Retail-786k Dataset

description: This dataset is a <strong>large-scale</strong> dataset for `<strong>`visual entity matching''</strong>. Entity Matching (EM) indicates the identification of semantic groupings. As it occurs like the identification of consumer products in images in retail applications. A product is a retail item on sale that is uniquely identified by the internationally standardized Global Trade Item Number (GTIN). Entities are then defined as semantic groupings of products. The dataset consists of <strong>786,179 imag</strong>es labeled with 3,298 different entities. The images are split into sub-sets of 748,715 training images and 37,464 test images. The images of the dataset are cropped from scanned advertisement leaflets, collected over several years from different European retailers. Two versions of the dataset are provided: once with the longer edge fixed to 512 and another one fixed to 256. The comprehensive dataset description can be found in the corresponding paper: https://arxiv.org/abs/2309.17164
DOI: 10.5281/zenodo.8400079
title: Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market

description: This release is associated with a paper entitled "A Novel Framework for the Day-Ahead Market Clearing Process Featuring the Participation of Distribution System Operators and a Hybrid Pricing Mechanism". In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market. Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem. Finally, we include information regarding the transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions of the IEEE 14-bus and the IEEE 34-bus, respectively. Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable (wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system's elements are provided in this document.
DOI: 10.5281/zenodo.8400078
title: Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market

description: This release is associated with a paper entitled "A Novel Framework for the Day-Ahead Market Clearing Process Featuring the Participation of Distribution System Operators and a Hybrid Pricing Mechanism". In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market. Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem. Finally, we include information regarding the transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions of the IEEE 14-bus and the IEEE 34-bus, respectively. Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable (wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system's elements are provided in this document.
DOI: 10.5281/zenodo.8393431
title: Rockfish chromosome reference assemblies (Kolora et al 2021)

description: Rockfish chromosome reference assemblies from Kolora et al 2021. These are also uploaded on NCBI however NCBI renamed the contigs making them incompatible with the annotation GFFs. These references have the original contig names and are thus compatible with the annotation GFFs found here https://doi.org/10.5281/zenodo.5534983.
DOI: 10.5281/zenodo.8393432
title: Rockfish chromosome reference assemblies (Kolora et al 2021)

description: Rockfish chromosome reference assemblies from Kolora et al 2021. These are also uploaded on NCBI however NCBI renamed the contigs making them incompatible with the annotation GFFs. These references have the original contig names and are thus compatible with the annotation GFFs found here https://doi.org/10.5281/zenodo.5534983.
DOI: 10.5281/zenodo.8393732
title: Rockfish chromosome reference assemblies (Kolora et al 2021)

description: Rockfish chromosome reference assemblies from Kolora et al 2021. These are also uploaded on NCBI however NCBI renamed the contigs making them incompatible with the annotation GFFs. These references have the original contig names and are thus compatible with the annotation GFFs found here https://doi.org/10.5281/zenodo.5534983.
DOI: 10.22002/f7a8r-n5x53
title: A-Band Absorption Spectrum of the ClSO Radical: Electronic Structure of the Sulfinyl Group

description: The data set contains the experimental and theoretical absolute cross section of ClSO from the paper: Wen Chao, Gregory H. Jones, Mitchio Okumura, Carl J. Percival,  Frank A. F. Winiberg, "A-Band Absorption Spectrum of the ClSO Radical: Electronic Structure of the Sulfinyl Group"
https://doi.org/10.1021/acs.jpca.3c04977
DOI: 10.5517/ccdc.csd.cc23cgwv
title: CCDC 1947447: Experimental Crystal Structure Determination

description: Related Article: Jiayong Zhang, Zhengjie He|2023|J.Org.Chem.|88|13102|doi:10.1021/acs.joc.3c01313
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.cc23cgxw
title: CCDC 1947448: Experimental Crystal Structure Determination

description: Related Article: Jiayong Zhang, Zhengjie He|2023|J.Org.Chem.|88|13102|doi:10.1021/acs.joc.3c01313
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.cc2byxrz
title: CCDC 2173712: Experimental Crystal Structure Determination

description: Related Article: Mattéo Cayla, Dorian Sonet, Emilien Tarayre, Romain Bapt, Brigitte Bibal|2023|J.Org.Chem.|88|13067|doi:10.1021/acs.joc.3c01252
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.cc2dyngh
title: CCDC 2233037: Experimental Crystal Structure Determination

description: Related Article: Kanghua Rui, Haorui Gu, Alemayehu Gashaw Woldegiorgis, Xufeng Lin|2023|Org.Lett.|25|6654|doi:10.1021/acs.orglett.3c02476
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.cc2d732v
title: CCDC 2211356: Experimental Crystal Structure Determination

description: Related Article: Angela Punzi, Yasi Dai, Carlo N. Dibenedetto, Ernesto Mesto, Emanuela Schingaro, Tobias Ullrich, Marinella Striccoli, Dirk M. Guldi, Fabrizia Negri, Gianluca M. Farinola, Davide Blasi|2023|J.Am.Chem.Soc.|145|20229|doi:10.1021/jacs.3c05251
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.cc2g7743
title: CCDC 2271064: Experimental Crystal Structure Determination

description: Related Article: Timur O. Zanakhov, Ekaterina E. Galenko, Mikhail S. Novikov, Alexander F. Khlebnikov|2023|J.Org.Chem.|88|13191|doi:10.1021/acs.joc.3c01413
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.cc2g7765
title: CCDC 2271066: Experimental Crystal Structure Determination

description: Related Article: Timur O. Zanakhov, Ekaterina E. Galenko, Mikhail S. Novikov, Alexander F. Khlebnikov|2023|J.Org.Chem.|88|13191|doi:10.1021/acs.joc.3c01413
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.