cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2gs1gs
title: CCDC 2287225: Experimental Crystal Structure Determination

description: Related Article: Tomislav Stolar, Jasna Alić, Gregor Talajić, Nikola Cindro, Mirta Rubčić, Krešimir Molčanov, Krunoslav Užarević, José G. Hernández|2023|Chem.Commun.|59|13490|doi:10.1039/D3CC04448C
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.cc2gs1ht
title: CCDC 2287226: Experimental Crystal Structure Determination

description: Related Article: Tomislav Stolar, Jasna Alić, Gregor Talajić, Nikola Cindro, Mirta Rubčić, Krešimir Molčanov, Krunoslav Užarević, José G. Hernández|2023|Chem.Commun.|59|13490|doi:10.1039/D3CC04448C
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: 10.25911/zkq5-7187
title: Reflected flux in reactions of 40Ca+208Pb at below-barrier energies

description: The measurements of the reflected flux were performed at Legnaro National Laboratory XTU Tandem-ALPI accelerator complex, using the PRISMA magnetic spectrometer. PRISMA features a large solid angle (80 msr, ∆θlab = ±6◦, ∆φ = ±11◦), momentum acceptance ∆p = ±10%, mass-resolution ∆A/A ∼ 1/200, and energy resolution up to 1/1000 (via time-of-flight measurement). In this experiment, PRISMA was located at θlab = 115◦. The magnetic fields were set for each energy to maximise the transmission for the dominant charge state of the elastically scattered beam. Thus, the measurements focus on the evolution of quasi-elastic scattering to multinucleon transfer (or deep-inelastic scattering). Beams of 40Ca were produced in 12 energy steps between Ecm=189.0 and 230.5 MeV. For the energies above 213 MeV, where the ALPI booster accelerator was used, carbon degrader foils of 135 μg/cm^2 or 205 μg/cm^2 were employed to provide three beam energies for each accelerator tune. The 40Ca beams were delivered onto ~150 μg/cm^2 208PbS targets oriented with their normals at 60 degrees to the beam axis. The targets had 20 μg/cm^2 carbon backings which were placed upstream of the target such that the particles accepted into PRISMA did not pass through the carbon backing. The atomic (proton) number Z, mass number A and energies of the scattered beam-like particles passing through PRISMA were determined using the ToF − Bρ − ∆E technique. Ions pass through a position-sensitive microchannel plate timing detector (MCP) before entering the quadrupole and dipole magnets. At the focal plane, ions first pass through a multi-wire parallel plate avalanche counter (MWPPAC) then into a segmented ionisation chamber. The measured positions of the ions in the MCP and MWPPAC define the trajectory of the ions through the magnetic elements, determining the magnetic rigidity Bρ. The energy loss of ions in the ionisation chamber enables the determination of Z, and with Bρ, the charge-state q. Together with time-of-flight (TOF), this allows determination of A and the kinetic energy of the projectile-like nuclei. Following Z,N determination, the ground-state to ground-state energy difference (Q-value, Qgg) could be obtained for each event. With the kinetic energy information, the total excitation energy Ex could be derived (Ex=Qgg-Q), making use of two-body kinematics. In the data files, the center of mass energy is indicated, along with the measurement angle of 115 degrees.
DOI: 10.57760/sciencedb.13221
title: More Impact, Less Effort: Laying the Foundation for research data management through PIDs like ORCiD
title: More Impact, Less Effort: Laying the Foundation for research data management through PIDs like ORCiD

description: More Impact, Less Effort: Laying the Foundation for research data management through PIDs like ORCiD
description: More Impact, Less Effort: Laying the Foundation for research data management through PIDs like ORCiD
DOI: 10.6084/m9.figshare.24579388
title: <b>M</b><b>antle metasomatism due to a paleo-subduction event: insights from Cu-Zn isotopic geochemistry of </b><b>South</b><b> </b><b>China</b><b> Sea Cenozoic volcanic rocks</b>

description: Supplementary Material, SupplementaryCalculated Data, and Supplementary Figures generated in this study are included in the Supplementary I, Table S1, and Figures S1-S8.
DOI: 10.6084/m9.figshare.24579388.v1
title: <b>M</b><b>antle metasomatism due to a paleo-subduction event: insights from Cu-Zn isotopic geochemistry of </b><b>South</b><b> </b><b>China</b><b> Sea Cenozoic volcanic rocks</b>

description: Supplementary Material, SupplementaryCalculated Data, and Supplementary Figures generated in this study are included in the Supplementary I, Table S1, and Figures S1-S8.
DOI: 10.34740/kaggle/dsv/6985196
title: WallStreetBets 2022
DOI: 10.6084/m9.figshare.24579529.v1
title: Scientific Workflow Management Systems List

description: List of SWfMSs
DOI: 10.6084/m9.figshare.24579529
title: Scientific Workflow Management Systems List and Necessary Scripts

description: List of SWfMSsScript Necessary for data download and preprocessing and post processing
DOI: 10.6084/m9.figshare.24579529.v3
title: Scientific Workflow Management Systems List and Necessary Scripts

description: List of SWfMSsScript Necessary for data download and preprocessing and post processing
DOI: 10.6084/m9.figshare.24579529.v2
title: Scientific Workflow Management Systems List

description: List of SWfMSsScript Necessary for data download and preprocessing and post processing
DOI: 10.6084/m9.figshare.24579541.v1
title: Stack Overflow Dataset related to SWfMSs

description: It contains the questions related to SWfMS used in our study and jupyter notebook scripts.
DOI: 10.6084/m9.figshare.24579541
title: Stack Overflow Dataset related to SWfMSs

description: It contains the questions related to SWfMS used in our study and jupyter notebook scripts.
DOI: 10.6084/m9.figshare.24579553
title: GitHub Dataset and necessary scripts

description: This contains the dataset we obtain for our study with scripts to process them
DOI: 10.5281/zenodo.10143375
title: Characterizing the complexity of subduction zone flow with an ensemble of multiscale global convection models

description: Parameter files and model input .txt files for ASPECT mantle convection simulations.
DOI: 10.48577/jpl.qj9pya
title: ML-ready Dataset for Identification of Frost in Martian HiRISE Images

description: This dataset was created to study Mars' seasonal frost cycle and its role in the planet's climate and surface evolution over the past ~2 billion years. Scientists are interested in identifying low-latitude frosted microclimates and their impact on climate, and future exploration of Mars will partially depend on finding potential habitable environments and resources for human explorers. Previous studies of Mars' seasonal frost cycle were limited by a trade-off between coverage and spatial resolution, as scientists were forced to take either a global perspective using coarse observations or a local perspective focused on higher-resolution observations. To overcome this limitation, data science techniques could be useful to identify frost at a global scale with both high-resolution visible and coarse resolution thermal datasets. Toward that goal, models are needed to help identify frost in large-scale, high-resolution imagery. The dataset provided here is meant to address that need. It contains high resolution visible data (from HiRISE on MRO) with frost labels, which can be used to train machine learning models for frost identification in large datasets.
DOI: 10.6084/m9.figshare.24570937.v1
title: 2008 Korea GNSS

description: 2008 Korea GNSS ​data
DOI: 10.26179/veb0-5z29
title: Quality controlled radiometer data from Macquarie Island, 2016-2018
DOI: 10.6084/m9.figshare.24573580
title: 7SK RNA SL3 DMS-MaPseq DREEM processed data

description: Structural plasticity is integral to RNA function; however, there are currently few methods to quantitatively resolve RNAs that have multiple structural states. NMR spectroscopy is a powerful approach for resolving conformational ensembles but is size-limited. Chemical probing is well-suited for large RNAs but provides limited structural and kinetics information. Here, we integrate the two approaches to visualize a two-state conformational ensemble for the central stem-loop 3 (SL3) of 7SK RNA, a critical element for 7SK RNA function in transcription regulation. We find that the SL3 distal end exchanges between two equally populated yet structurally distinct states in both isolated SL3 constructs and full-length 7SK RNA. We rationally designed constructs that lock SL3 into a single state and demonstrate that both chemical probing and NMR data fit to a linear combination of the two states. Comparison of vertebrate 7SK RNA sequences shows either or both states are highly conserved. These results provide new insights into 7SK RNA structural dynamics and demonstrate the utility of integrating chemical probing with NMR spectroscopy to gain quantitative insights into RNA conformational ensembles.
DOI: 10.6084/m9.figshare.24573580.v4
title: 7SK RNA SL3 DMS-MaPseq DREEM processed data

description: Structural plasticity is integral to RNA function; however, there are currently few methods to quantitatively resolve RNAs that have multiple structural states. NMR spectroscopy is a powerful approach for resolving conformational ensembles but is size-limited. Chemical probing is well-suited for large RNAs but provides limited structural and kinetics information. Here, we integrate the two approaches to visualize a two-state conformational ensemble for the central stem-loop 3 (SL3) of 7SK RNA, a critical element for 7SK RNA function in transcription regulation. We find that the SL3 distal end exchanges between two equally populated yet structurally distinct states in both isolated SL3 constructs and full-length 7SK RNA. We rationally designed constructs that lock SL3 into a single state and demonstrate that both chemical probing and NMR data fit to a linear combination of the two states. Comparison of vertebrate 7SK RNA sequences shows either or both states are highly conserved. These results provide new insights into 7SK RNA structural dynamics and demonstrate the utility of integrating chemical probing with NMR spectroscopy to gain quantitative insights into RNA conformational ensembles.
DOI: 10.6084/m9.figshare.24573580.v3
title: 7SK RNA SL3 DMS-MaPseq DREEM processed data

description: Structural plasticity is integral to RNA function; however, there are currently few methods to quantitatively resolve RNAs that have multiple structural states. NMR spectroscopy is a powerful approach for resolving conformational ensembles but is size-limited. Chemical probing is well-suited for large RNAs but provides limited structural and kinetics information. Here, we integrate the two approaches to visualize a two-state conformational ensemble for the central stem-loop 3 (SL3) of 7SK RNA, a critical element for 7SK RNA function in transcription regulation. We find that the SL3 distal end exchanges between two equally populated yet structurally distinct states in both isolated SL3 constructs and full-length 7SK RNA. We rationally designed constructs that lock SL3 into a single state and demonstrate that both chemical probing and NMR data fit to a linear combination of the two states. Comparison of vertebrate 7SK RNA sequences shows either or both states are highly conserved. These results provide new insights into 7SK RNA structural dynamics and demonstrate the utility of integrating chemical probing with NMR spectroscopy to gain quantitative insights into RNA conformational ensembles.
DOI: 10.34740/kaggle/dsv/6985442
title: VTUStudyMarkAnalysis
DOI: 10.5281/zenodo.10146579
title: Data provided in manuscript Mid-Holocene rainfall seasonality and ENSO dynamics over the southwestern Pacific

description: Here we provide datasets of trace elements (LA-ICP-MS), carbon and oxygen stable isotopes, and greyscale values extracted from stalagmite C132 from Niue Island, covering the mid-Holocene (6.4 to 5.4 ka BP). The dataset includes the speleothem 230Th dates, and layer counting.
DOI: 10.5281/zenodo.8346849
title: Data provided in manuscript Mid-Holocene rainfall seasonality and ENSO dynamics over the southwestern Pacific

description: Here we provide datasets of trace elements (LA-ICP-MS), carbon and oxygen stable isotopes, and greyscale values extracted from stalagmite C132 from Niue Island, covering the mid-Holocene (6.4 to 5.4 ka BP). The dataset includes the speleothem 230Th dates, and layer counting.
DOI: 10.5281/zenodo.8346850
title: Data provided in manuscript Mid-Holocene rainfall seasonality and ENSO dynamics over the southwestern Pacific

description: Here we provide datasets of trace elements (LA-ICP-MS), carbon and oxygen stable isotopes, and greyscale values extracted from stalagmite C132 from Niue Island, covering the mid-Holocene (6.4 to 5.4 ka BP). The dataset includes the speleothem 230Th dates, and layer counting.
DOI: 10.18141/1962402
title: National Critical Minerals Data Dashboard

description: The Critical Minerals (CM) Data Dashboard provides data-driven insights to help unlock the potential of conventional and unconventional CM resources in the United States. The dashboard focuses on 12 CM essential for producing zero-emission transportation and clean power technology. More information about these CM and potential resources is available in our CM Story Map. Dashboard data sets can be filtered geographically using the options provided along the top of the dashboard. Data sets can also be filtered by various attributes using the options in the expandable side panel on the left side of the screen. The data sets in this dashboard were compiled from multiple sources including the USGS, USEPA, OSMRE, DHS, and EarthJustice. Users are encouraged to provide feedback/suggestions on the function and content of the dashboard, as well as any issues that may arise.
DOI: 10.57931/2006303
title: HarDWR - Cumulative Water Rights Curves

description: For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. This product is the dataset used as input to the WBM model (Grogan et al., in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (submission pending). This database contains 1,744 individual .csv files, two for each Water Management Area (WMA; see here) in the 11-state region. File naming convention: WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either S for surface water rights, or G for groundwater rights. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the ### unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al., in review)
DOI: 10.5281/zenodo.10146611
title: Libros de la Biblioteca de Juan Íñiguez de Arnedo (1641-1710) , Obispo de Pamplona (1700-1710)

description: Libros que en algún tiempo fueron propiedad de Juan Íñiguez de Arnedo
DOI: 10.5281/zenodo.10146610
title: Libros de la Biblioteca de Juan Íñiguez de Arnedo (1641-1710) , Obispo de Pamplona (1700-1710)

description: Libros que en algún tiempo fueron propiedad de Juan Íñiguez de Arnedo
DOI: 10.25582/data.2021-09.2327485/2208235
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-12.2390335/2208236
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-09.2327484/2208237
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2326940/2208238
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327388/2208239
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-07.2258016/2208240
title: Data for EMSL Project 60000 from July 2021
DOI: 10.25582/data.2021-09.2326971/2208241
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327343/2208242
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-12.2390248/2208243
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-09.2327334/2208244
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-11.2382366/2208245
title: Data for EMSL Project 60000 from November 2021
DOI: 10.25582/data.2021-09.2327524/2208246
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-08.2297742/2208247
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-09.2327353/2208248
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2328816/2208249
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327400/2208250
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-11.2382228/2208251
title: Data for EMSL Project 60000 from November 2021
DOI: 10.25582/data.2021-09.2327502/2208252
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-08.2295661/2208253
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-09.2326881/2208254
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-08.2296922/2208255
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-08.2296893/2208256
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-09.2327322/2208257
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327442/2208258
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-07.2257235/2208259
title: Data for EMSL Project 60000 from July 2021
DOI: 10.25582/data.2021-09.2327310/2208260
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327480/2208261
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-12.2390234/2208262
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-08.2296258/2208263
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-08.2296445/2208264
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-09.2326956/2208265
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-11.2383444/2208266
title: Data for EMSL Project 60000 from November 2021
DOI: 10.25582/data.2021-09.2326877/2208267
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327332/2208268
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2326885/2208269
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2326937/2208270
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2326945/2208271
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-08.2297015/2208272
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-08.2296119/2208273
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-09.2327324/2208274
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327465/2208275
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2326894/2208276
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327397/2208277
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-11.2383330/2208278
title: Data for EMSL Project 60000 from November 2021
DOI: 10.25582/data.2021-07.2290677/2208279
title: Data for EMSL Project 60000 from July 2021
DOI: 10.25582/data.2021-12.2390331/2208280
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-09.2327547/2208281
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327434/2208282
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-12.2390220/2208283
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-09.2326893/2208284
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-07.2290687/2208285
title: Data for EMSL Project 60000 from July 2021
DOI: 10.25582/data.2021-09.2327518/2208286
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327355/2208287
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-12.2390229/2208288
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-09.2326907/2208289
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-12.2390290/2208290
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-12.2390196/2208291
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-11.2382117/2208292
title: Data for EMSL Project 60000 from November 2021
DOI: 10.25582/data.2021-07.2290660/2208293
title: Data for EMSL Project 60000 from July 2021
DOI: 10.25582/data.2021-11.2382609/2208294
title: Data for EMSL Project 60000 from November 2021
DOI: 10.25582/data.2021-07.2257310/2208295
title: Data for EMSL Project 60000 from July 2021
DOI: 10.25582/data.2021-11.2382214/2208296
title: Data for EMSL Project 60000 from November 2021
DOI: 10.25582/data.2021-09.2327307/2208297
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2327336/2208298
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-09.2326975/2208299
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-12.2390267/2208300
title: Data for EMSL Project 60000 from December 2021
DOI: 10.25582/data.2021-08.2296470/2208301
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-09.2326948/2208302
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-08.2296276/2208303
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-09.2327438/2208304
title: Data for EMSL Project 60000 from September 2021
DOI: 10.25582/data.2021-08.2296300/2208305
title: Data for EMSL Project 60000 from August 2021
DOI: 10.25582/data.2021-08.2297003/2208306
title: Data for EMSL Project 60000 from August 2021