cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2f63yq
title: CCDC 2240213: Experimental Crystal Structure Determination

description: Related Article: Suman Sar, Prasanta Ghorai|2023|Org.Lett.|25|1946|doi:10.1021/acs.orglett.3c00494
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.cc2df275
title: CCDC 2217096: Experimental Crystal Structure Determination

description: Related Article: Cole Carter, Yosi Kratish, Tobin J. Marks|2023|Inorg.Chem.|62|4799|doi:10.1021/acs.inorgchem.2c03973
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.cc2c3tkw
title: CCDC 2178418: Experimental Crystal Structure Determination

description: Related Article: Ana Arteaga, Aaron D. Nicholas, Lucas C. Ducati, Jochen Autschbach, Robert G. Surbella III|2023|Inorg.Chem.|62|4814|doi:10.1021/acs.inorgchem.2c03976
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.cc2fb778
title: CCDC 2244159: Experimental Crystal Structure Determination

description: Related Article: Ying Wang, Yuxiang Yang, Yijie Wu, Jin Li, Bowen Hu, Yimin Cai, Lihua Yuan, Wen Feng|2023|Inorg.Chem.|62|4922|doi:10.1021/acs.inorgchem.2c04384
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: 10.34740/kaggle/dsv/5273735
title: World's most Air polluted cities(7k+ data 2017-22)
DOI: 10.34740/kaggle/dsv/5274170
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6084/m9.figshare.19483346.v1
title: Stoma Study: Plasticity of the adult human small intestinal microbiota

description: The data here are the 16S amplicon sequencing data from the stoma study containing mapping files and example scripts.
DOI: 10.6084/m9.figshare.22360807
title: Fig Yield and Climate-Pest Interaction Dataset in a Mediterranean region : Bejaïa province, Algeria.

description: This dataset contains information on fig cultivation, pests, and climate in the Bejaïa province of Algeria, collected through surveys and investigations conducted between 2010 and 2022. The dataset includes information on the number of fig trees harvested (FTN), the surface area cultivated (C.AREA), and yield per tree for each of the 52 communes in the province. Additionally, data on the annual occurrence of pests in the region from 2011 to 2022 and monthly temperature and precipitation (min, max, and mean) from 2010 to 2018 were obtained from WorldClim2 database
DOI: 10.6084/m9.figshare.22360807.v3
title: Fig Yield and Climate-Pest Interaction Dataset in a Mediterranean region : Bejaïa province, Algeria.

description: This dataset contains information on fig cultivation, pests, and climate in the Bejaïa province of Algeria, collected through surveys and investigations conducted between 2010 and 2022. The dataset includes information on the number of fig trees harvested (FTN), the surface area cultivated (C.AREA), and yield per tree for each of the 52 communes in the province. Additionally, data on the annual occurrence of pests in the region from 2011 to 2022 and monthly temperature and precipitation (min, max, and mean) from 2010 to 2018 were obtained from WorldClim2 database
DOI: 10.6084/m9.figshare.22360807.v2
title: Fig Yield and Climate-Pest Interaction Dataset in a Mediterranean region : Bejaïa province, Algeria.

description: This dataset contains information on fig cultivation, pests, and climate in the Bejaïa province of Algeria, collected through surveys and investigations conducted between 2010 and 2022. The dataset includes information on the number of fig trees harvested (FTN), the surface area cultivated (C.AREA), and yield per tree for each of the 52 communes in the province. Additionally, data on the annual occurrence of pests in the region from 2011 to 2022 and monthly temperature and precipitation (min, max, and mean) from 2010 to 2018 were obtained from WorldClim2 database
DOI: 10.5281/zenodo.7787503
title: Supporting data for ''Sea surface warming patterns drive hydrological sensitivity uncertainties"

description: Supporting data for our work on Nature Climate Change. You will find: - Integrated desired fireds from the CAM5 patch experiments, - Post-processed data from CMIP5 and CMIP6 models, - python codes for generating the plots in the manuscript.
DOI: 10.5281/zenodo.7787501
title: Supplementary data for Saintilan et al. 2023

description: Supplementary Data 1: Surface Elevation Table data for Tidal Marshes, including elevation rate, elevation deficit, elevation capital, losses and gains of water area within 5 kilometres, losses and gains of tidal marsh within 5 kilometres. Supplementary Data 2: Area change in coral islands.
DOI: 10.5281/zenodo.7784157
title: ParticleNeRF Dataset

description: Dataset used in the paper ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields
DOI: 10.5281/zenodo.7784156
title: ParticleNeRF Dataset

description: Dataset used in the paper ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields
DOI: 10.26187/deakin.22359847
title: SAW-IT-Plus Video Dataset

description: The SAW-IT-Plus dataset contains 11,458 videos collected in the wild, and 22 homemade videos (snake category). Videos are arranged in 8 main categories of animals (frogs - 0, snakes - 1, lizards - 2, birds - 3, small mammals &lt; 2kg - 4, medium or large mammals &gt; 2kg - 5, spiders - 7 and scorpions - 8). Echidnas - originally category 6 – were merged with big mammals. Some videos of crustacea and other reptiles are available but not classified. Empty videos (7,896) were added to allow for further testing of the algorithm. They are separated in 3 categories (details in Table 1). CSV files are available to detail the species for frogs, lizards, birds and small mammals for each video. Because the videos were mainly collected from real-world data; the number of videos for each animal category are unbalanced (Table 1). This folder also contains training images used to automatically detect videos containing animals in our overall dataset. More information available in the ReadMe files. <br> The dataset was collected in Victoria, Australia, from February to October 2021 as part of the ERP22 (formerly ARI-PPD 05) grant.
DOI: 10.26187/deakin.22359847.v1
title: SAW-IT-Plus Video Dataset

description: The SAW-IT-Plus dataset contains 11,458 videos collected in the wild, and 22 homemade videos (snake category). Videos are arranged in 8 main categories of animals (frogs - 0, snakes - 1, lizards - 2, birds - 3, small mammals &lt; 2kg - 4, medium or large mammals &gt; 2kg - 5, spiders - 7 and scorpions - 8). Echidnas - originally category 6 – were merged with big mammals. Some videos of crustacea and other reptiles are available but not classified. Empty videos (7,896) were added to allow for further testing of the algorithm. They are separated in 3 categories (details in Table 1). CSV files are available to detail the species for frogs, lizards, birds and small mammals for each video. Because the videos were mainly collected from real-world data; the number of videos for each animal category are unbalanced (Table 1). This folder also contains training images used to automatically detect videos containing animals in our overall dataset. More information available in the ReadMe files. <br> The dataset was collected in Victoria, Australia, from February to October 2021 as part of the ERP22 (formerly ARI-PPD 05) grant.
DOI: 10.6084/m9.figshare.22109861.v1
title: Supplementary Material for: Phenotypic Variation in 46,XX Disorders of Sex Development due to the 4th Zinc Finger Domain Variant of WT1 : A Familial Case Report

description: Introduction: The variants in the zinc finger (ZF) domains 1–3 in WT1 are one of the major causes of 46,XY disorders of sex development. Recently, the variants in fourth ZF (ZF4 variants) were reported to cause 46,XX DSD. However, all the nine patients reported were de novo, and no familial cases were identified. Case presentation and Results: The proband (16yrs social female), had 46,XX karyotype with dysplastic testes and moderate virilization in genitalia. A ZF4 variant, p.Arg495Gln in WT1 was identified in the proband, her brother and mother. The mother did not show any virilization with normal fertility, and the 46,XY brother developed normal puberty. Discussion/Conclusion: The phenotypic variations due to ZF4 variant are extremely broad in 46,XX cases.
DOI: 10.21227/0ghx-rw44
title: DGA results

description: Power transformers are critical components in a power system network and they are subjected to multifactorial stresses. The dissolved gas composition of an oil-filled transformer provides good indication of the transformer health. Numerous methods have been developed to interpret the gas data, both conventional and artificial intelligent based. There are issues with inaccurate diagnosis, high dependency on expert judgment and rigid diagnosis standards as transformer designs, materials and issues continue to evolve. This paper attempts to predict transformer fault using Multiclass Classification Models by Microsoft Azure ML Studio, based on Duval Triangle 1. Trained with 628 samples and validated with 269 samples, the One-vs-All Multiclass model achieved an overall accuracy of 90.3%. The model is then compared with 4 published works of similar nature. The output fault classes of this work are clustered accordingly to facilitate good comparison. Its performance in terms of prediction accuracy is superior when compared with SOM (73.6%) and SVM (78.5%) models, similar as Auto-WEKA Random Forest model (90.3%) and inferior when compared with ANFIS (97.7%). When validated with 122 datasets of inspected and diagnosed faulty transformers from IEC TC 10 database and published works, the overall accuracy is 76.2% for 7 output fault classes. The prediction accuracy advances to 91.8% when the fault classes are clustered into 4 fault types. Less output classes result in higher accuracy as there is lower probability for the diagnosis to fall into adjacent or neighboring classes. More training data is expected to improve the overall performance of the model.
DOI: 10.34740/kaggle/dsv/5274625
title: Spotify Daily Top 50 Of 63 countries + Global
DOI: 10.5519/qd.x7ry7j7n
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.6482274
title: CIRO experimental results

description: This repository includes the experiment results, source code, and test data for Three Cs risk inference, using the CIRO (COVID-19 Infection Risk Ontology) and HermiT. results.csv: The results of the experiment. test_cases.csv: The test cases of the experiment. CIRO.owl: The COVID-19 Infection Risk Ontology. test_main.py: The source code of the experiment. Requirements: Python 3 Owlready2 rdflib
DOI: 10.5281/zenodo.6482275
title: CIRO experimental results

description: This repository includes the experiment results, source code, and test data for Three Cs risk inference, using the CIRO (COVID-19 Infection Risk Ontology) and HermiT. results.csv: The results of the experiment. test_cases.csv: The test cases of the experiment. CIRO.owl: The COVID-19 Infection Risk Ontology. test_main.py: The source code of the experiment. Requirements: Python 3 Owlready2 rdflib
DOI: 10.15156/bio/sh0878781.09fu
title: SH0878781.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0878782.09fu
title: SH0878782.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0878783.09fu
title: SH0878783.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0878784.09fu
title: SH0878784.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0878785.09fu
title: SH0878785.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.19841758
title: Effect of habitual reading direction on saccadic eye movements: A pilot study

description: The raw data and experimental programs investigated the effect of habitual reading direction on saccadic eye movements (i.e. prosaccade, antisaccade and self-paced saccade).
DOI: 10.6084/m9.figshare.19841758.v2
title: Effect of habitual reading direction on saccadic eye movements: A pilot study

description: The raw data investigated the effect of habitual reading direction on saccadic eye movements (i.e. prosaccade, antisaccade and self-paced saccade).
DOI: 10.6084/m9.figshare.22249924
title: mmsd mouse brain neg msi.zip

description: MMSD-MSI raw data of a mouse brain tissue section in the negative ion mode. The raw data are subsection compressed.
DOI: 10.25625/p1lzai/br48po
title: Using LiveDocs for scientific dissemination, and research data reproducibility and re-usability.pdf

description: :unav
DOI: 10.25625/p1lzai
title: Slides for the INF mini-workshop session "Using LiveDocs for scientific dissemination, and research data reproducibility and re-usability"

description: Slides for the presentation and hands-on sessions.
DOI: 10.25625/0iceba/42k483
title: UHS_Benchmark_PROP_EOSNUM.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/lkw3um
title: UHS_Benchmark_LowH2.DATA

description: UHS Benchmark - Input File - Eclipse - Case I
DOI: 10.25625/0iceba/1uiw0a
title: UHS_Benchmark_PROPS.INC

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/56bt8j
title: eclipse_caseI.tab

description: UHS Benchmark Results Eclipse Case I
DOI: 10.25625/0iceba/ketexb
title: dumux_caseI.tab

description: UHS Benchmark Results Dumux Case I
DOI: 10.25625/0iceba/qswcrv
title: UHS_Benchmark_PROP_PORO.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/vx8hzc
title: ReadMe.txt

description: Benchmark Description
DOI: 10.25625/0iceba/ygcmmf
title: UHS_Benchmark_PROP_PERMY.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/8dts0h
title: UHS_Benchmark_PROP_EQLNUM.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/olz5rf
title: UHS_Benchmark_SUM.INC

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/zcnk2s
title: UHS_Benchmark_GRID.INC

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/6nsvvv
title: UHS_Benchmark_PROP_PERMX.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/kmcz1k
title: UHS_Benchmark_SOL.INC

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/n1gcj3
title: UHS_Benchmark_PROP_ROCKNUM.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/niup7n
title: UHS_Benchmark_PROP_PERMZ.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/oucywp
title: UHS_Benchmark_SCH.INC

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/vwsfd9
title: UHS_Benchmark_PROP_SATNUM.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/yuvhbg
title: UHS_Benchmark_PROP_PVTNUM.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/15yfzz
title: UHS_Benchmark_HighH2.DATA

description: UHS Benchmark - Input File - Eclipse - Case II
DOI: 10.25625/0iceba/80uptk
title: eclipse_caseII.tab

description: UHS Benchmark Results Eclipse Case II
DOI: 10.25625/0iceba/gtk3lg
title: UHS_Benchmark_GRID.GRDECL

description: UHS Benchmark - Input File - Eclipse
DOI: 10.25625/0iceba/levocq
title: dumux_caseII.tab

description: UHS Benchmark Results Dumux Case II
DOI: 10.25625/0iceba/rbncq7
title: dumux_caseIV.tab

description: UHS Benchmark Results Dumux Case IV
DOI: 10.25625/0iceba/s1gucd
title: dumux_caseIII.tab

description: UHS Benchmark Results Dumux Case III
DOI: 10.6084/m9.figshare.21687677
title: extended data Implants

description: extended data containing case performa, and informed questionnaire form.
DOI: 10.6084/m9.figshare.21687677.v2
title: extended data Implants

description: extended data containing case performa, and informed questionnaire form.
DOI: 10.6084/m9.figshare.21687677.v1
title: extended data Implants

description: extended data containing case performa, and informed questionnaire form.
DOI: 10.6084/m9.figshare.21986900
title: Identifying the Deep-inflow Mixing Features in Orographically Locked Diurnal Convection (Model Simulation)

description: Model output used in Figures 2 and 3.
DOI: 10.6084/m9.figshare.21986900.v4
title: Identifying the Deep-inflow Mixing Features in Orographically Locked Diurnal Convection (Model Simulation)

description: Model output used in Figures 2 and 3.
DOI: 10.6084/m9.figshare.21986900.v3
title: Identifying the Deep-inflow Mixing Features in Orographically Locked Diurnal Convection (Model Simulation)

description: Model output used in Figures 2 and 3.
DOI: 10.6084/m9.figshare.21986564
title: Identifying the Deep-inflow Mixing Features in Orographically Locked Diurnal Convection (Observation)

description: Observaltional data used in Figures 1 and 4.
DOI: 10.6084/m9.figshare.21986564.v3
title: Identifying the Deep-inflow Mixing Features in Orographically Locked Diurnal Convection (Observation)

description: Observaltional data used in Figures 1 and 4.
DOI: 10.6084/m9.figshare.21986564.v4
title: Identifying the Deep-inflow Mixing Features in Orographically Locked Diurnal Convection (Observation)

description: Observaltional data used in Figures 1 and 4.
DOI: 10.6084/m9.figshare.19483346
title: Stoma Study: Plasticity of the adult human small intestinal microbiota

description: The data here are the 16S amplicon sequencing data from the stoma study containing mapping files and example scripts.
DOI: 10.25914/3p9e-dq16
title: Fire danger indices historical data from the Copernicus Emergency Management Service (CEMS) v4.0