cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24496528.v1
title: Untitled Item<b>NUCLEAR GENE INTROGRESSION</b><b>S </b><b>IN HYBRID POPULATIONS OF WATER FROG </b><b>PELOPHYLAX ESCULENTUS</b><b> COMPLEX: GEOGRAPHICAL ANALYSIS OF THE PHENOMENON AND ITS INTERPRETATION</b>

description: The dataset contains information on 159 frog water samples from different river systems in Ukraine. For each sample the belonging to the river system, geographical coordinates, species composition are indicated. For marsh frog samples, the presence or absence of introgression of alien genetic material is additionally indicated. For samples of hybrid frogs, the presence or absence of recombination of parental genomes is indicated.
DOI: 10.5281/zenodo.4549620
title: In situ smartphone radiometry of Lake Balaton, MONOCLE H2020 project

description: This dataset includes in situ radiometric data collected from Lake Balaton and Kis-Balaton, Hungary during July 3rd – July 5th, 2019 within the framework of Horizon 2020 MONOCLE project.


The Python code used to analyse the data and produce the summary tables, and which should be used to read the data in, can be found here: https://github.com/burggraaff/smartphone-water-colour


The data are structured as follows:


"Balaton_2019070x" - These folders contain the RAW and JPEG smartphone images, sorted by station, time, and smartphone. Each low-level subfolder includes the RAW and JPEG images and a CSV file with the derived radiance, R_rs, and other values.


"Discarded_data" - These folders contain RAW and JPEG smartphone images that were not used, organised in the same way as "Balaton_2019070x". They are included here for completeness. Each subfolder contains a file explaining why the data were not used.


"Greycard" - This folder contains the RAW smartphone images used to characterise the angular response of the grey card. Also included are the derived mean/uncertainty values in .NPY format.


"BALATON_2019_STATION_LOG" - This worksheet contains the station log of the MONOCLE field campaign at Lake Balaton in 2019, during which our field data were taken.


"balaton_xxx_18pct.csv" - These CSV files contain summaries of the data derived from the smartphone RAW images, including radiance, R_rs, etc. They are most easily read in using the Python code linked above. These files were generated by stacking the individual CSV files from each station/time/smartphone. The filename indicates the smartphone and data type.


"balaton_Samsung_Galaxy_S8_raw_replicates.csv" - This CSV file contains the relative uncertainty (R/G/B and band ratios, in %) and absolute uncertainty (hue angle and FU) in replicate Galaxy S8 measurements, as described in the paper.


"greycard_data_Maine.csv" - This CSV file contains the data used to determine the spectral response of the grey card. It includes spectral measurements of the surface irradiance on a white panel and the grey card, and the downwelling irradiance measured with a cosine collector.


"README.txt" - Description of the data.


"So-Rad_Balaton2019.csv" - This CSV file contains the (ir)radiance data and R_rs from the So-Rad on 3 July 2019, processed using the 3C method, as described in the paper.


"wisp_Balaton_20190703_20190705_table.csv" - This CSV file contains the (ir)radiance data and R_rs from the WISP-3 on 3-5 July 2019, processed using the Mobley method, as described in the paper.
DOI: 10.5281/zenodo.4549621
title: In situ smartphone radiometry of Lake Balaton, MONOCLE H2020 project

description: This dataset includes in situ radiometric data collected from Lake Balaton and Kis-Balaton, Hungary during July 3rd – July 5th, 2019 within the framework of Horizon 2020 MONOCLE project.


The Python code used to analyse the data and produce the summary tables, and which should be used to read the data in, can be found here: https://github.com/burggraaff/smartphone-water-colour


The data are structured as follows:


"Balaton_2019070x" - These folders contain the RAW and JPEG smartphone images, sorted by station, time, and smartphone. Each low-level subfolder includes the RAW and JPEG images and a CSV file with the derived radiance, R_rs, and other values.


"Discarded_data" - These folders contain RAW and JPEG smartphone images that were not used, organised in the same way as "Balaton_2019070x". They are included here for completeness. Each subfolder contains a file explaining why the data were not used.


"Greycard" - This folder contains the RAW smartphone images used to characterise the angular response of the grey card. Also included are the derived mean/uncertainty values in .NPY format.


"BALATON_2019_STATION_LOG" - This worksheet contains the station log of the MONOCLE field campaign at Lake Balaton in 2019, during which our field data were taken.


"balaton_xxx_18pct.csv" - These CSV files contain summaries of the data derived from the smartphone RAW images, including radiance, R_rs, etc. They are most easily read in using the Python code linked above. These files were generated by stacking the individual CSV files from each station/time/smartphone. The filename indicates the smartphone and data type.


"balaton_Samsung_Galaxy_S8_raw_replicates.csv" - This CSV file contains the relative uncertainty (R/G/B and band ratios, in %) and absolute uncertainty (hue angle and FU) in replicate Galaxy S8 measurements, as described in the paper.


"greycard_data_Maine.csv" - This CSV file contains the data used to determine the spectral response of the grey card. It includes spectral measurements of the surface irradiance on a white panel and the grey card, and the downwelling irradiance measured with a cosine collector.


"README.txt" - Description of the data.


"So-Rad_Balaton2019.csv" - This CSV file contains the (ir)radiance data and R_rs from the So-Rad on 3 July 2019, processed using the 3C method, as described in the paper.


"wisp_Balaton_20190703_20190705_table.csv" - This CSV file contains the (ir)radiance data and R_rs from the WISP-3 on 3-5 July 2019, processed using the Mobley method, as described in the paper.
DOI: 10.5281/zenodo.4559501
title: In situ bio-optical data in Lake Balaton, MONOCLE H2020 project

description: This dataset includes in situ bio-optical and biogeochemical data collected from Lake Balaton, Hungary during July 1<sup>st</sup> – July 5<sup>th</sup>, 2019 within the framework of Horizon 2020 MONOCLE project. Specifically, the following parameters are provided in the dataset: Chlorophyll-a concentration (Chl-a) Total Suspended Matter concentration (TSM) Absorption of Coloured Dissolved Organic Matters (CDOM) Turbidity Remote-sensing reflectance (Rrs) from TriOS Subsurface irradiance reflectance (R0-) from WISP Particulate absorption coefficient (apd) Particulate scattering coefficient (bp) Particulate attenuation coefficient (cpd) Particulate backscattering coefficient (bbp) Aerosol optical thickness (AOT) Downwelling irradiance (Es) from HyperOCR Data are provided in either Excel or CSV file, “NA” or “nan” indicates no data available.
DOI: 10.5517/ccdc.csd.cc21f2j3
title: CCDC 1889404: Experimental Crystal Structure Determination

description: Related Article: Ziao Zong, Guimei Huang, Jinmiao Wang, Lulu Wang, Mengxia Ma, Yaqin Pang, Junli Wang, Feng Guo, Chuanbin Fan|2023|Polyhedron|246|116684|doi:10.1016/j.poly.2023.116684
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.cc25zqzw
title: CCDC 2025539: Experimental Crystal Structure Determination

description: Related Article: Dandan Wang, Long Wang, Xing Guo, Xiankang Zhang, Juan Ma, Zhengxin Kang, Zhong-Yuan Li, Lijuan Jiao, Erhong Hao|2023|Org.Lett.|25|7650|doi:10.1021/acs.orglett.3c02962
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.cc2by6p6
title: CCDC 2173028: Experimental Crystal Structure Determination

description: Related Article: Wenzhuo Zhao, Pengpeng Yuan, Qi Zhang, Nan Liu, Yulai Wang, Peng Wang, Thomas Huhn, Zhongduo Yang, Tiankun Zhao|2023|Results in Chemistry|6|101161|doi:10.1016/j.rechem.2023.101161
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.cc2dbd49
title: CCDC 2214520: Experimental Crystal Structure Determination

description: Related Article: Yan Li, Chenglong Yu, Yiyue Wang, Tao Sun, Kai Wang, Shijie Xian, Yifan Liu|2023|Polyhedron|246|116666|doi:10.1016/j.poly.2023.116666
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.cc2fjn0n
title: CCDC 2250321: Experimental Crystal Structure Determination

description: Related Article: Yan Li, Chenglong Yu, Yiyue Wang, Tao Sun, Kai Wang, Shijie Xian, Yifan Liu|2023|Polyhedron|246|116666|doi:10.1016/j.poly.2023.116666
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.cc2fmxzz
title: CCDC 2253482: Experimental Crystal Structure Determination

description: Related Article: Bowen Li, Zhenming Ding, Zhimin Wu, Meihua Liu, Shaofeng Chen, Dongcheng Chen, Zhigao Li, Long Sang, Yu Liu, Lili Lin, Weiguo Zhu, Xiaobo Wan|2023|Chem.Eng.Journal|476|146484|doi:10.1016/j.cej.2023.146484
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.cc2g5pq2
title: CCDC 2269594: Experimental Crystal Structure Determination

description: Related Article: Armands Sebris, Matas Guzauskas, Malek Mahmoudi, Dmytro Volyniuk, Juozas V. Grazulevicius, Anatoly Mishnev, Irina Novosjolova, Māris Turks, Gediminas Jonusauskas, Kaspars Traskovskis|2023|J.Mater.Chem.C|11|14608|doi:10.1039/D3TC02681G
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.cc2g5pzb
title: CCDC 2269602: Experimental Crystal Structure Determination

description: Related Article: Armands Sebris, Matas Guzauskas, Malek Mahmoudi, Dmytro Volyniuk, Juozas V. Grazulevicius, Anatoly Mishnev, Irina Novosjolova, Māris Turks, Gediminas Jonusauskas, Kaspars Traskovskis|2023|J.Mater.Chem.C|11|14608|doi:10.1039/D3TC02681G
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.21947653
title: Confidently extracting hierarchical taxonomy information from unstructured maintenance records of industrial equipment

description: Maintenance records of complex industrial equipment contain a large amount of unstructured data (e.g. technician notes) pertaining to repair actions and associated equipment sub-components, degradation conditions, failure mechanisms, etc. These unstructured data can yield valuable insights to improve the equipment design and maintenance plans, resulting in higher productivity and lower operating costs. Since manual review of information is time-consuming, companies make limited use of the maintenance records. To address this opportunity, we propose a taxonomy-guided method for automatically analysing the unstructured data and inferring critical information, specifically the hierarchy of the equipment's sub-assemblies and constituent parts that malfunctioned or failed during a breakdown event. Our method leverages syntactic (related to word frequency) as well as semantic (related to word co-occurrence and their meaning) knowledge. A novel contribution of our work is that we provide a confidence score for the information inferred by our method. Only the maintenance records which receive a low confidence score will require manual review to confirm the automated method's results, thus ensuring minimal use of human resources. We demonstrate the performance of our method using a real-world data set from equipment used in oil rigs.
DOI: 10.6084/m9.figshare.21947653.v1
title: Confidently extracting hierarchical taxonomy information from unstructured maintenance records of industrial equipment

description: Maintenance records of complex industrial equipment contain a large amount of unstructured data (e.g. technician notes) pertaining to repair actions and associated equipment sub-components, degradation conditions, failure mechanisms, etc. These unstructured data can yield valuable insights to improve the equipment design and maintenance plans, resulting in higher productivity and lower operating costs. Since manual review of information is time-consuming, companies make limited use of the maintenance records. To address this opportunity, we propose a taxonomy-guided method for automatically analysing the unstructured data and inferring critical information, specifically the hierarchy of the equipment's sub-assemblies and constituent parts that malfunctioned or failed during a breakdown event. Our method leverages syntactic (related to word frequency) as well as semantic (related to word co-occurrence and their meaning) knowledge. A novel contribution of our work is that we provide a confidence score for the information inferred by our method. Only the maintenance records which receive a low confidence score will require manual review to confirm the automated method's results, thus ensuring minimal use of human resources. We demonstrate the performance of our method using a real-world data set from equipment used in oil rigs.
DOI: 10.25345/c5862bn9t
title: MassIVE MSV000093289 - Lambert_SAINT3709_IPMS_Fusion_P134_VS12
title: This set of submissions contains the mass spectrometry files for the manuscript by Kougnassoukou Tchara et al. that describes the functional characterization of the human acetyl lysine machinery. AP-MS, BioID and endogenous experiments were performed from Flp-In T-REx HEK293, A375 and 501mel cells and MS files were acquired on 5600 TripleTOF, Orbitrap Elite, Orbitrap Velos and LTQ mass spectrometers. Different MassIVE submissions corresponding to SAINT Task Results (see list below) have been made; this README file pertains to all submissions. Please refer to the individual Tables 1 within each submission for details of submitted files. For questions, please contact Jean-Philippe Lambert (lambert@lunenfeld.ca).
DOI: 10.6084/m9.figshare.24496615
title: DIPY_EVAC+_weights (new version)

description: The data is for the EVAC+ model in dipy. Old one will be deleted as soon as this is officially used.
DOI: 10.5281/zenodo.10034655
title: Dataset for the paper "Gamma-ray burst detection with Poisson-FOCuS and other trigger algorithms"

description: Datasets for the paper "Detecting GRBs with Poisson-FOCuS and other trigger algorithms". The repository contains four different datasets:
simulated_dataset_grb120707800.fits: This dataset comprises a FITS table containing data for 30,000 simulated Gamma-Ray Burst (GRB) lightcurves. These lightcurves simulate different intensities and are modeled after the short burst GRB120707800. The data are binned at a 16 ms resolution and stored as images. This file includes both control and testing datasets.
simulated_dataset_grb180703949.fits: Similar to the previous dataset, this one contains data for 30,000 simulated GRB lightcurves. However, these lightcurves are modeled after long burst GRB180703949. The data is also binned at a 16 ms resolution and stored as images. Like the previous dataset, it includes both control and testing datasets.
gbm_dataset_20171002_20171009.zip: This dataset consists of a zipped CSV file containing count data observed by the 12 NaI detectors of Fermi-GBM during the period from October 2, 2017, to October 9, 2017. The data are binned at a 16 ms resolution.
gbm_dataset_20140101_20140108.zip: As above but for the period from January 1, 2014, to January 8, 2014. 
gbm_dataset_20190601_20190608.zip: As above but for the period from June 1, 2019, to June 8, 2017. 
simulated_dataset_compeff: This dataset includes a collection of 1000 randomly generated, Poisson-distributed count time series with varying lengths and means, stored as text files. Rather dull but provided for completeness.
 
Code used in this research is available here.
DOI: 10.5281/zenodo.8334676
title: Gamma-ray burst detection with Poisson-FOCuS and other trigger algorithms

description: Datasets for the paper "Detecting GRBs with Poisson-FOCuS and other trigger algorithms". The repository contains the following datasets:
simulated_dataset_grb120707800.fits: This dataset comprises a FITS table containing data for 30,000 simulated Gamma-Ray Burst (GRB) lightcurves. These lightcurves simulate different intensities and are modeled after the short burst GRB120707800. The data are binned at a 16 ms resolution and stored as images. This file includes both control and testing datasets.
simulated_dataset_grb180703949.fits: Similar to the previous dataset, this one contains data for 30,000 simulated GRB lightcurves. However, these lightcurves are modeled after long burst GRB180703949. The data is also binned at a 16 ms resolution and stored as images. Like the previous dataset, it includes both control and testing datasets.
gbm_dataset_50to300keV_binned016ms_20171002_201710.zip: This dataset consists of a zipped CSV file containing count data observed by the 12 NaI detectors of Fermi-GBM during the period from October 2, 2017, to October 10, 2017. The data are binned at a 16 ms resolution and covers the energy band 50-300 keV.
simulated_dataset_compeff: This dataset includes a collection of 1000 randomly generated, Poisson-distributed count time series with varying lengths and means, stored as text files. Rather dull but provided for completeness.
DOI: 10.5281/zenodo.8334675
title: Dataset for the paper "Gamma-ray burst detection with Poisson-FOCuS and other trigger algorithms"

description: Datasets for the paper "Detecting GRBs with Poisson-FOCuS and other trigger algorithms". The repository contains four different datasets:
simulated_dataset_grb120707800.fits: This dataset comprises a FITS table containing data for 30,000 simulated Gamma-Ray Burst (GRB) lightcurves. These lightcurves simulate different intensities and are modeled after the short burst GRB120707800. The data are binned at a 16 ms resolution and stored as images. This file includes both control and testing datasets.
simulated_dataset_grb180703949.fits: Similar to the previous dataset, this one contains data for 30,000 simulated GRB lightcurves. However, these lightcurves are modeled after long burst GRB180703949. The data is also binned at a 16 ms resolution and stored as images. Like the previous dataset, it includes both control and testing datasets.
gbm_dataset_20171002_20171009.zip: This dataset consists of a zipped CSV file containing count data observed by the 12 NaI detectors of Fermi-GBM during the period from October 2, 2017, to October 9, 2017. The data are binned at a 16 ms resolution.
gbm_dataset_20140101_20140108.zip: As above but for the period from January 1, 2014, to January 8, 2014. 
gbm_dataset_20190601_20190608.zip: As above but for the period from June 1, 2019, to June 8, 2017. 
simulated_dataset_compeff: This dataset includes a collection of 1000 randomly generated, Poisson-distributed count time series with varying lengths and means, stored as text files. Rather dull but provided for completeness.
 
Code used in this research is available here.
DOI: 10.5066/p933z1tk
title: LCMAP CONUS Reference Data Product 1984-2021 land cover, land use and change process attributes

description: This product contains LCMAP CONUS Reference plot location data in a .shp format as well as annual land cover, land use, and change process variables for each reference data plot in a separate .csv table. The same information available in the.csv file is also provided in a .xlsx format. The LCMAP Reference Data Product was utilized for evaluation and validation of the Land Change Monitoring, Assessment, and Projection (LCMAP) land cover and land cover change products.� This LCMAP Reference Data Product includes the collection of an independent dataset of 25,000 randomly-distributed and 2,000 stratified random 30-meter by 30-meter samples across the conterminous United States (CONUS). The 2,000 samples were selected using a stratified random sampling process with 4 strata based off the LCMAP Collection 1.0 Science Products.The LCMAP Reference Data Products collected variables related to primary and secondary land use, primary and secondary land cover(s), change processes, and other ancillary variables annually across CONUS from 1984?2021.�
DOI: 10.5281/zenodo.10069922
title: Swift/BAT-GUANO upper limits fits map for S231102w
DOI: 10.5281/zenodo.10069923
title: Swift/BAT-GUANO upper limits fits map for S231102w
DOI: 10.25318/9810038501-fra
title: Obtention d’un diplôme d’études secondaires, selon l’année de recensement : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement

description: Données historiques du recensement (2006, 2011, 2016 et 2021) sur la répartition en pourcentage de la population selon le diplôme d'études secondaires ou l'attestation d'équivalence, y compris des combinaisons de titres scolaires d'études secondaires et postsecondaires.
DOI: 10.5519/qd.psgfhkzq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (842 records)
DOI: 10.5519/qd.87y80dgv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3208 records)
DOI: 10.1184/r1/24481162
title: RECOMB2024_invtrans_merged_sort.vcf.gz

description: .vcf file containing structural variations of K-562 cancer cell line, which is used in the work "Improving Hi-C contact matrices using genome graphs".
DOI: 10.1184/r1/24481162.v1
title: RECOMB2024_invtrans_merged_sort.vcf.gz

description: .vcf file containing structural variations of K-562 cancer cell line, which is used in the work "Improving Hi-C contact matrices using genome graphs".
DOI: 10.5281/zenodo.10069971
title: Constraining present-day anthropogenic total iron emissions using model and observations