cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7083149
title: IDN-Sum

description: Summarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text summarization algorithms. Our dataset is generated using random playthroughs of 8 IDN episodes, taken from 2 different IDN games, and consists of 10,000 documents. Playthrough documents are annotated through automatic alignment with fan-sourced summaries using a commonly used alignment algorithm. The dataset is released as open source for future researchers to train and test their own approaches for IDN text. Annotated Data folder contains the IDN-Sum data that was automatically annotated using the alignment algorithm. Subfolders hold different versions of data in the format suitable for input to BertSum (bs) from TransformerSum library (https://github.com/HHousen/TransformerSum) and SummaRuNNer(sr) (for implementation at https://github.com/hpzhao/SummaRuNNer). They are are named using convention [model_name]_[summary_length]. Unannotated playthroughs can be found in Cleaned Data folder.
DOI: 10.5281/zenodo.7083148
title: IDN-Sum

description: Summarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text summarization algorithms. Our dataset is generated using random playthroughs of 8 IDN episodes, taken from 2 different IDN games, and consists of 10,000 documents. Playthrough documents are annotated through automatic alignment with fan-sourced summaries using a commonly used alignment algorithm. The dataset is released as open source for future researchers to train and test their own approaches for IDN text. Annotated Data folder contains the IDN-Sum data that was automatically annotated using the alignment algorithm. Subfolders hold different versions of data in the format suitable for input to BertSum (bs) from TransformerSum library (https://github.com/HHousen/TransformerSum) and SummaRuNNer(sr) (for implementation at https://github.com/hpzhao/SummaRuNNer). They are are named using convention [model_name]_[summary_length]. Unannotated playthroughs can be found in Cleaned Data folder.
DOI: 10.6084/m9.figshare.21668156
title: Abell and Winckler Supplementary Dataset S1

description: Abell and Winckler Supplementary Dataset S1 used in:<br>"Abell and Winckler. (2023). <b>Long-term variability in Pliocene North Pacific Ocean export production and its implications for ocean circulation in a warmer world</b>"<br>submitted to <i>AGU Advances.</i>
DOI: 10.6084/m9.figshare.21668156.v3
title: Abell and Winckler Supplementary Dataset S1

description: Abell and Winckler Supplementary Dataset S1 used in:<br>"Abell and Winckler. (2023). <b>Long-term variability in Pliocene North Pacific Ocean export production and its implications for ocean circulation in a warmer world</b>"<br>submitted to <i>AGU Advances.</i>
DOI: 10.14272/haukgwoqlxzejp-uhfffaoysa-m/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C22H21BrN.ClH/c1-24-12-10-19(11-13-24)21-14-16-2-6-18(21)7-3-17-5-9-20(8-4-16)22(23)15-17;/h2,5-6,9-15H,3-4,7-8H2,1H3;1H/q+1;/p-1

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (126 MHz, D2O, ppm) δ = 156.3 (1C, CAr), 144.6 (2C, CAr), 142.1 (1C, CAr), 141.0 (1C, CAr), 139.6 (1C, CAr), 138.5 (1C, CAr), 137.5 (1C, CAr), 136.3 (1C, CAr), 136.0 (1C, CAr), 133.8 (1C, CAr), 132.6 (1C, CAr), 131.7 (1C, CAr), 128.8 (1C, CAr), 127.8 (2C, CAr), 126.5 (1C, CAr), 47.2 (1C, CH3), 34.8 (1C, CH2), 33.6 (1C, CH2), 32.9 (1C, CH2), 32.3 (1C, CH2).
DOI: 10.14272/haukgwoqlxzejp-uhfffaoysa-m.1
title: C22H21BrClN
title: InChI=1S/C22H21BrN.ClH/c1-24-12-10-19(11-13-24)21-14-16-2-6-18(21)7-3-17-5-9-20(8-4-16)22(23)15-17;/h2,5-6,9-15H,3-4,7-8H2,1H3;1H/q+1;/p-1

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367]. The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]: InChI descriptor[cheminf_000113]: InChI=1S/C22H21BrN.ClH/c1-24-12-10-19(11-13-24)21-14-16-2-6-18(21)7-3-17-5-9-20(8-4-16)22(23)15-17;/h2,5-6,9-15H,3-4,7-8H2,1H3;1H/q+1;/p-1, and canonical SMILES descriptor[cheminf_000007]: C[n+]1ccc(cc1)c1cc2ccc1CCc1ccc(CC2)c(c1)Br.[Cl-], and by the IUPAC name[cheminf_000107]: . The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]: The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-33144 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]: Melting point descriptor[CHEMINF_000256]: 221.6 (°C) Boiling point descriptor[CHEMINF_000257]: Refractive index descriptor[CHEMINF_000253]: The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]: CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR) CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR) CHMO:0000630 | infrared absorption spectroscopy (IR) CHMO:0000470 | mass spectrometry (MS) The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: Used ontologies: CHEBI - Chemical Entities of Biological Interest CHEMINF - chemical information ontology (information entities about chemical entities) CHMO - Chemical Methods Ontology OBI - Ontology for Biomedical Investigations
DOI: 10.14272/haukgwoqlxzejp-uhfffaoysa-m/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C22H21BrN.ClH/c1-24-12-10-19(11-13-24)21-14-16-2-6-18(21)7-3-17-5-9-20(8-4-16)22(23)15-17;/h2,5-6,9-15H,3-4,7-8H2,1H3;1H/q+1;/p-1

description: dataset for mass spectrometry (MS) MS (ESI), m/z (%): 380/378 (96/100). HRMS-ESI (m/z): [M]+ calcd for [C22H21Br79N]+ 378.0852; found 378.0850. HRMS-ESI (m/z): [M]+ calcd for [C22H21Br81N]+ 380.0852; 380.0830.
DOI: 10.14272/haukgwoqlxzejp-uhfffaoysa-m/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C22H21BrN.ClH/c1-24-12-10-19(11-13-24)21-14-16-2-6-18(21)7-3-17-5-9-20(8-4-16)22(23)15-17;/h2,5-6,9-15H,3-4,7-8H2,1H3;1H/q+1;/p-1

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (500 MHz, D2O) δ = 8.76 (d, J = 6.3 Hz, 2H), 8.12 (d, J = 6.3 Hz, 2H), 7.43 (dd, J = 7.8, 1.9 Hz, 1H), 6.90 (d, J = 1.9 Hz, 1H), 6.83 (d, J = 7.8 Hz, 1H), 6.79–6.72 (m, 2H), 6.52 (dd, J = 7.9, 1.8 Hz, 1H), 4.39 (s, 3H), 3.53 (ddd, J = 13.0, 10.2, 2.3 Hz, 1H), 3.40–3.24 (m, 2H), 3.15 (ddt, J = 14.2, 10.9, 5.1 Hz, 2H), 3.01 (ddd, J = 13.2, 10.7, 5.6 Hz, 1H), 2.88 (ddd, J = 14.1, 10.3, 4.0 Hz, 1H), 2.65 (ddd, J = 14.0, 10.1, 4.6 Hz, 1H).
DOI: 10.14272/haukgwoqlxzejp-uhfffaoysa-m/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C22H21BrN.ClH/c1-24-12-10-19(11-13-24)21-14-16-2-6-18(21)7-3-17-5-9-20(8-4-16)22(23)15-17;/h2,5-6,9-15H,3-4,7-8H2,1H3;1H/q+1;/p-1

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3996 (m), 3987 (m), 3980 (m), 3976 (m), 3970 (m), 3965 (m), 3957 (m), 3534 (m), 3366 (vs), 3360 (vs), 3334 (vs), 3329 (vs), 3260 (vs), 3130 (vs), 3036 (vs), 2964 (vs), 2928 (vs), 2893 (s), 2852 (s), 1637 (vs), 1518 (m), 1433 (m), 1403 (s), 1194 (m) cm–1.
DOI: 10.6084/m9.figshare.23605752
title: Supplementary material to support the manuscript 'Productivity, water use, and carbon capture rate of an outdoor open raceway pond of Sodalinema alkaliphilum at pH 10+.'

description: In 2020 and 2021, 3,000 L of biomass dominated by <i>Candidatus </i><i>Sodalinema alkaliphilum</i> was grown in an outdoor raceway pond in Calgary, Canada.The biomass accounting file contains details pertaining to biomass density, growth, and yield as well as the implementation of a biomass growth model.The water use file contains details regarding rainfall, water use (evaporation and overflow), and make-up water requirements. The carbon capture file contains calculations for the abiotic carbon capture rate of the pond and the pH throughout each season.The nutrients file contains nutrient concentrations of the pond and make-up tap water (Nitrate, Mg, K, P, S, Ca, Fe).The weather file contains temperature and irradiance data.
DOI: 10.48366/r603749
title: Review of application of machine learning models for real time and short term prediction of water levels and discharge

description: Review and proposal of thorough review of application of machine learning models for real-time and short term prediction (forecasting) of water levels (stage) and discharge (streamflow, runoff, inflow). The emphasis is on methodology related to real time to short term prediction of high water events, but includes any machine learning (particulary supervised learning) modeling of levels and discharge. Some properties placed in the table may be estimated by reader, which is described in property "notes". For example, in some cases number of instances, historical dataset length, especially input shape and output shape should be estimated by reader. - prone to errors, they are estimated. Input shape also could be estimated in some contributions, Ratio of parts in dataset split is always in the form of percentage to sum up to 100 %, which often should be estimated by reader. Terms used in variety of papers for dataset split often differs (training, building, calibration, validation, verification, testing, etc.), but in the most of the cases it has two parts (which are here described as training and testing) or three parts (which are here described as called training, calibration and verification).
DOI: 10.5281/zenodo.8195949
title: A Multimodal Sensing Ring for Quantification of Scratch Intensity

description: Dataset and code accompanying the paper, A Multimodal Sensing Ring for Quantification of Scratch Intensity. More information located on the paper's github page. For the most updated version of code and data and for a README, please visit the github page.
DOI: 10.5281/zenodo.8195948
title: A Multimodal Sensing Ring for Quantification of Scratch Intensity

description: Dataset and code accompanying the paper, A Multimodal Sensing Ring for Quantification of Scratch Intensity. More information located on the paper's github page. For the most updated version of code and data and for a README, please visit the github page.
DOI: 10.6084/m9.figshare.3761562.v2663
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5066/p93qzrrl
title: Downscaled western bumble bee predicted occupancy for 2020, western conterminous United States.

description: This data represents occupancy predictions for western bumble bee in 2020 across the western conterminous United States. This product is a 30-meter resolution downscaled version of previously published occupancy layers for the western bumble bee. For more information on original publications, see the publication listed in the Larger Works Cited Section.
DOI: 10.6084/m9.figshare.23671941
title: <b>Putative neural mechanisms underlying release-mode-specific abnormalities in dopamine neural activity in a schizophrenia-like model: the distinct roles of glutamate and serotonin in the impaired regulations of dopamine neurons</b>

description: The author confirms that the data supporting the findings of this study .<br>
DOI: 10.6084/m9.figshare.23671941.v2
title: <b>Putative neural mechanisms underlying release-mode-specific abnormalities in dopamine neural activity in a schizophrenia-like model: the distinct roles of glutamate and serotonin in the impaired regulations of dopamine neurons</b>

description: The author confirms that the data supporting the findings of this study .<br>
DOI: 10.6084/m9.figshare.23671941.v3
title: <b>Putative neural mechanisms underlying release-mode-specific abnormalities in dopamine neural activity in a schizophrenia-like model: the distinct roles of glutamate and serotonin in the impaired regulations of dopamine neurons</b>

description: The author confirms that the data supporting the findings of this study .<br>
DOI: 10.15784/601711
title: Hercules Dome Ice-Penetrating Radar Swath Topographies

description: This dataset consists of gridded basal topographies in the vicinity of Hercules Dome, East Antarctica generated from multi-element ice-penetrating radar data.
DOI: 10.15784/601713
title: Beardmore Glacier High-Frequency Impulse Radar Data

description: This dataset contains high-frequency impulse radar data collected in the vicinity of the Beardmore Glacier grounding zone.
DOI: 10.21227/p4dh-5w64
title: 33 and 136-bus test system data for explainable reinforcement learning

description: This comprehensive dataset comprises multiple files, encompassing essential information on various aspects of power systems. It includes the active and reactive power consumption data for both the 33- and 136-bus test systems, along with the resistance and reactance values of the distribution lines, and the network structure.In addition to this valuable data, the dataset also features a second type containing feasible reconfiguration actions for the 33- and 136-bus systems. These actions provide insights into optimizing and adjusting the configuration of the power distribution network.Lastly, the third type of data is dedicated to capturing the actions of a reinforcement learning agent operating within the 33- and 136-bus test systems under diverse load conditions. This particular aspect showcases the agent's adaptability and decision-making abilities in response to changing power demands.Together, this dataset offers a comprehensive and rich resource for researchers, engineers, and practitioners working in the field of power systems and reinforcement learning, facilitating advanced studies and the development of innovative solutions for efficient and reliable power grid management.
DOI: 10.6084/m9.figshare.23811546
title: Untitled Item

description: Peanut transcriptome
DOI: 10.6084/m9.figshare.23811546.v1
title: Untitled Item

description: Peanut transcriptome
DOI: 10.6073/pasta/f8d4596d8a73d32046bcd87e35678550
title: Soil moisture determinations by Electrical Resistivity (ERT) Experiment at the Kellogg Biological Station, Hickory Corners, MI (2009)

description: Dataset AbstractLarge-scale conversion of croplands to perennial biofuel crops could substantially impact regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, these differences in crop water use are not well known due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. Data used in: https://doi.org/10.1002/vzj2.20124original data source http://lter.kbs.msu.edu/datasets/222
DOI: 10.18739/a2xw47x8w
title: Arctic Water Isotope Network (AWIN) long-term water vapor observations, 2017-2022

description: The dataset presented here contains coordinated long-term (multi-year) continuous water vapor isotopic (δ18O, δD, deuterium excess) measurements from four land-based sites across the Arctic. These stations include Nord and Thule in Greenland and the National Ecological Observation Network (NEON) sites of Toolik and Utqiagvik in Alaska, USA. Data begins for some sites in August 2017, with all sites operational by September 2019 to coincide with the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition. Data continues at available sites through August 2022. In a region with relatively few long-term water vapor isotopic measurements, this coordinated dataset covering multiple years provides an important observations to investigate the rapidly changing Arctic system, including to examine the response to sea ice loss, moisture transport variations due to shifts in atmospheric circulation, and extreme events such as cyclones and atmospheric rivers.
DOI: 10.17909/cq37-w558
title: HST/STIS and FUSE Observations of HD 75309

description: This dataset includes the HST/STIS spectrum of HD 75309, obtained with the E140H grating on 2000 March 28, and the FUSE observations of HD 75309, obtained on 2000 January 26. These data were used to study high-velocity shocked gas in the Vela supernova remnant.
DOI: 10.6084/m9.figshare.23811504
title: Treatment with Gut-Specific Nonsteroidal Anti-Inflammatory Drug Attenuates Metabolic Inflammation but not Body Mass in Fattening Ground Squirrels

description: 16S rRNA V4 region sequences from the 13-lined ground squirrel gut microbiome, that received mesalazine or control treatment during the active season.
DOI: 10.6084/m9.figshare.23811504.v1
title: Treatment with Gut-Specific Nonsteroidal Anti-Inflammatory Drug Attenuates Metabolic Inflammation but not Body Mass in Fattening Ground Squirrels

description: 16S rRNA V4 region sequences from the 13-lined ground squirrel gut microbiome, that received mesalazine or control treatment during the active season.
DOI: 10.5066/p9bpbr8s
title: High-Resolution Mass Spectrometry Data for Wastewater Samples Collected at an on-Site Separator and Storage Tank at the Marcellus Shale Energy and Environment Laboratory (MSEEL) 2015-2019, Morgantown Industrial Park (MIP), West Virginia

description: The Marcellus Shale Energy and Environment Laboratory (MSEEL) is part of the Northeast Natural Energy LLC (NNE) production facility on the Monongahela River in Monongalia County, West Virginia, USA. Natural gas extraction in the area creates large volumes of wastewater that may contain chemical compounds that pose risks to humans, animals, and the environment. The U. S. Geological Survey (USGS) has been studying the organic compounds in water produced from shale gas wells and in 2014, NNE started drilling two production wells in the Marcellus Shale. The well, MIP (Morgantown Industrial Park) -5H, was completed the following year. Samples were collected from this well. Large volumes of wastewater fluids were produced from this well and contain a mixture of chemicals added during hydraulic fracturing of the formation and chemical constituents from the host rocks. The original hydraulic fracturing fluids and the wastewater contain organic materials that could be toxic or pose risks to the environment if leaked, spilled, or improperly disposed of. This project was designed to collect comprehensive data from multiple sample types, including Monongahela River water, wastewater collected at the separator (during the flowback and produced-water phase), storage tank wastewater, and field blanks. Water samples for the analyses contained in this release were collected from July 2015 through December 2018. Samples of Monongahela River water were collected from a lined holding pond adjacent to the MIP well pad on November 5, 2015. Wastewater was intermittently sampled from the MIP-5H separator on the well pad from December 10, 2015, to December 6, 2018. The wastewater produced during the first two weeks of production was labeled as flowback water until December 23, 2015, then the wastewater produced after this date was defined as produced water. A storage tank at the well pad site was sampled on December 6, 2018. This data release contains non-target liquid chromatography/high-resolution mass spectrometry data.
DOI: 10.5517/ccdc.csd.cc2dp0bg
title: CCDC 2224725: Experimental Crystal Structure Determination

description: Related Article: Jean-Louis Do, Thomas Auvray, Cameron B. Lennox, Hatem M. Titi, Louis A. Cuccia, Tomislav Friščić|2023|Green Chemistry|25|5899|doi:10.1039/D3GC01150J
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.cc2dp0dj
title: CCDC 2224727: Experimental Crystal Structure Determination

description: Related Article: Jean-Louis Do, Thomas Auvray, Cameron B. Lennox, Hatem M. Titi, Louis A. Cuccia, Tomislav Friščić|2023|Green Chemistry|25|5899|doi:10.1039/D3GC01150J
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.cc2dp0fk
title: CCDC 2224728: Experimental Crystal Structure Determination

description: Related Article: Jean-Louis Do, Thomas Auvray, Cameron B. Lennox, Hatem M. Titi, Louis A. Cuccia, Tomislav Friščić|2023|Green Chemistry|25|5899|doi:10.1039/D3GC01150J
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.cc2dp0gl
title: CCDC 2224729: Experimental Crystal Structure Determination

description: Related Article: Jean-Louis Do, Thomas Auvray, Cameron B Lennox, Hatem M. Titi, Louis A Cuccia, Tomislav Friščić|2023|Green Chemistry|25|5899|doi:10.1039/D3GC01150J
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.cc2dp0hm
title: CCDC 2224730: Experimental Crystal Structure Determination

description: Related Article: Jean-Louis Do, Thomas Auvray, Cameron B. Lennox, Hatem M. Titi, Louis A. Cuccia, Tomislav Friščić|2023|Green Chemistry|25|5899|doi:10.1039/D3GC01150J
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.8200934
title: panulirus_gracilis_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8200933
title: panulirus_gracilis_zanfona_1.fasta.gz

description: .
DOI: 10.3886/e193067v1
title: US Major-League Sports Stadium and Arena Construction Costs (1909-2027)

description: This dataset covers the modern era of concrete and steel sports venues. It includes all venues opened in US and Canada from 1909 to 2026 that served as the main host for teams in the four major US-based professional sports leagues: Major League Baseball (MLB), National Basketball Association (NBA), National Football League (NFL), and National Hockey League (NHL).