cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8299429
title: X-band 3D Radar Cross Section Values for Desert Locusts

description: Radar Cross Section values simulated using WiPL-D Pro Electromagnetic Solver at X-band (9.4GHz) frequency. Naming convention: [species_name]_[internal_material]_[geometry]_[body_length]_[body_width]_[along_body_RCS_x1000]_[across_body_RCS_x1000]_[model_mass_x1000]_[resolution]. Internal material: water or BBG (AKA NelsonLGB). Geometry refers to model dimension. Body length, and width are in millimetres. RCS values are in cm<sup>2</sup>. Model mass is grams. Resolution refers to the number of plates used in WiPL-d Pro to create the model.
DOI: 10.34740/kaggle/dsv/6385644
title: Country Data
DOI: 10.18739/a2dn3zx27
title: Supraglacial stream discharge measurements from six catchments within the Pâqitsoq region of Sermeq Avannarleq, western Greenland Ice Sheet 2017 -2018

description: Meltwater produced on the Greenland Ice Sheet (GrIS) surface is routed through complex networks of supraglacial streams across the ice surface. Most of the supraglacial streams on the GrIS terminate into moulins. Moulins are vertical conduits that penetrate through the entire ice thickness and deliver meltwater to the ice sheet's bed where water pressure fluctuations can modulate sliding speed. A poorly constrained parameter in glacial hydrological models is the timing and the volume of meltwater entering moulins within the GrIS ablation area. Here, we present direct measurements of supraglacial stream discharge within six catchments within the GrIS ablation area during the 2017 and 2018 melt seasons. In 2017 we measured stream discharge using a fluorescent dye dilution method. We continuously injected a Rhodamine WT solution (known concentration and pumping rate) into supraglacial streams and measured the dye concentration with a Cyclops fluorometer installed 50-100 meters downstream of the dye injection site. In 2018 using a continuous wave Doppler flowmeter (Teledyne ISCO 2150) that was installed on the stream's floor. We then use these measurements to model catchment discharge from runoff data (Mejia et al., 2021) using the synthetic unit hydrograph, following the methodology described by King 2018.
DOI: 10.5281/zenodo.8298944
title: C. intestinalis larval behavior dataset with perturbations of neurotransmitter and neuromodulator pathways

description: C. intestinalis larval behavior dataset with perturbations of neurotransmitter and neuromodulator pathways
DOI: 10.5281/zenodo.8298943
title: C. intestinalis larval behavior dataset with perturbations of neurotransmitter and neuromodulator pathways

description: C. intestinalis larval behavior dataset with perturbations of neurotransmitter and neuromodulator pathways
DOI: 10.5281/zenodo.8299432
title: Amphioxus larval behavior (DLC dataset)

description: Amphioxus larval behavior (DLC dataset)
DOI: 10.5281/zenodo.8299431
title: Amphioxus larval behavior (DLC dataset)

description: Amphioxus larval behavior (DLC dataset)
DOI: 10.18739/a23r0pv3m
title: South Alaskan ice-marginal lakes from Landsat 8 (2013-2019) via Google Earth Engine

description: This dataset accompanies a manuscript submitted for review to the Journal of Remote Sensing. Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. As dynamic features that experience short-term (i.e., day to year) variations in area and volume, they form an important yet understudied element of the complete hydrologic system of glaciers with which they are in contact. To accelerate the study of ice-marginal lakes over large temporal and spatial extents, we automate the mapping of ice-marginal lakes by implementing a trained minimum-distance classifier of monthly Landsat 8 data products in Google Earth Engine. We produce maps of ice-marginal lakes in south Alaska for the summer months March through August for each year from 2013 through 2019. These maps are manually reviewed for accuracy. By spatially joining all maps, we can identify lakes throughout time, even if they are changing rapidly or dramatically. This dataset includes the spatial join of all lakes and shapefiles of each individual lake identified, grouped by lake. Within these lake shapefiles is illustrated an individual history of lake change; each feature is a delineation of the lake at a specific point in time.
DOI: 10.5517/ccdc.csd.cc2dysz4
title: CCDC 2233177: Experimental Crystal Structure Determination

description: Related Article: E.D. Maggott, D.A. Haynes, M. Hantusch, M. Seifert, J. Weigand, S.F. Mapolie|2023|React.Funct.Polym.|191|105675|doi:10.1016/j.reactfunctpolym.2023.105675
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.cc2fgm8t
title: CCDC 2248376: Experimental Crystal Structure Determination

description: Related Article: Indrani Roy, Mrinmoy Ghosh, Mamata Barua, Pameli Ghosh, Subratanath Koner, Nasim Sepay, Mohd Afzal, Abdullah Alarifi, Sandip Saha, Nayim Sepay|2023|J.Mol.Liq.|388|122717|doi:10.1016/j.molliq.2023.122717
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.cc2fjh4m
title: CCDC 2250170: Experimental Crystal Structure Determination

description: Related Article: Amirbek D. Radzhabov, Natalia S. Soldatova, Daniil M. Ivanov, Mekhman S. Yusubov, Vadim Yu. Kukushkin, Pavel S. Postnikov|2023|Org.Biomol.Chem.|21|6743|doi:10.1039/D3OB00833A
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.cc2fl7pz
title: CCDC 2251861: Experimental Crystal Structure Determination

description: Related Article: Irene Carrasco, Philipp Ehni, Max Ebert, Noée Dumait, Gregory Taupier, Maria Amela-Cortes, Claire Roiland, Stéphane Cordier, Julius A. Knöller, Emmanuel Jacques, Sabine Laschat, Yann Molard|2023|ACS Applied Materials and Interfaces|15|39752|doi:10.1021/acsami.3c08441
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.cc2fl7q0
title: CCDC 2251862: Experimental Crystal Structure Determination

description: Related Article: Irene Carrasco, Philipp Ehni, Max Ebert, Noée Dumait, Gregory Taupier, Maria Amela-Cortes, Claire Roiland, Stéphane Cordier, Julius A. Knöller, Emmanuel Jacques, Sabine Laschat, Yann Molard|2023|ACS Applied Materials and Interfaces|15|39752|doi:10.1021/acsami.3c08441
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.cc2fn62d
title: CCDC 2253733: Experimental Crystal Structure Determination

description: Related Article: Ganesh Chandra Upreti, Tavinder Singh, Divakar Chaudhary, Anand Singh|2023|J.Org.Chem.|88|11801|doi:10.1021/acs.joc.3c01090
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: 10.18739/a2x63b64h
title: In-situ snow accumulation, melt, and firn density records using ground penetrating radar and firn cores, western Greenland, 1955-2017

description: The stability of the Greenland Ice Sheet (GIS) is of critical interest to scientists and society at large in the context of future sea-level rise. The magnitude of GIS volumetric shrinkage in the coming decades depends on its glacial discharge and surface mass balance (SMB), with the later representing the balance between accumulation and surface melt. Estimates of Greenland SMB increasingly utilize climate reanalyses and high-resolution regional climate models (RCMs) to determine snow accumulation, surface melt and runoff/refreeze. These models show significant and model-dependent biases along the high-gradient edges of the GIS where the highest and most variable (in space and time) rates of accumulation and surface melt are observed. Thus, the edges of the GIS are in critical need of updated validation with in-situ accumulation and melt data, as model biases lead to significant deviations in mass balance estimates. Here we propose a traverse in the Western Greenland percolation zone over two field seasons to develop continuous in-situ snow accumulation and firn density records using ground-based radar and shallow firn cores. Our research objectives include: (1) determining the spatiotemporal patterns of snow accumulation in Western Greenland over the past 20-40 years; (2) evaluating surface melt refreeze and englacial meltwater storage in the Western Greenland percolation zone over the past 20-40 years; and (3) quantifying the accumulation and surface melt biases of the most recent climate reanalysis models and their RCM counterparts.
DOI: 10.6084/m9.figshare.3761562.v2692
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: 10.25345/c58s4k05w
title: MassIVE MSV000092783 - Root associated bacterial communities and root metabolite composition are linked to nitrogen use efficiency in sorghum
title: Development of cereal crops with high nitrogen-use efficiency (NUE) is a priority for worldwide agriculture. In addition to conventional plant breeding and genetic engineering, the use of the plant microbiome offers another approach to improve crop NUE. To gain insight into the bacterial communities associated with sorghum lines that differ in NUE, a field experiment was designed comparing 24 diverse sorghum lines under sufficient and deficient nitrogen (N). Amplicon sequencing and untargeted gas chromatography-mass spectrometry (GC-MS) were used to characterize the bacterial communities and the root metabolome associated with sorghum genotypes varying in sensitivity to low N. We demonstrated that N stress and sorghum type (energy, sweet, and grain sorghum) significantly impacted the root-associated bacterial communities and root metabolite composition of sorghum. We found a positive correlation between sorghum NUE and bacterial richness and diversity in the rhizosphere. The greater alpha diversity in high NUE lines was associated with the decreased abundance of a dominant bacterial taxa, Pseudomonas. Multiple strong correlations were detected between root metabolites and rhizosphere bacterial communities in response to low-N stress. This indicates that the shift in the sorghum microbiome due to low-N is associated with the root metabolites of the host plant. Taken together, our findings suggest that host genetic regulation of root metabolites plays a role in defining the root-associated microbiome of sorghum genotypes differing in NUE and tolerance to low-N stress.
DOI: 10.15468/s8ewse
title: University of Pretoria: South African Ticks(Acari: Ixodidae)

description: ca. 35000 individual collection records for African ticks (Acari: Ixodidae)
DOI: 10.5517/ccdc.csd.cc2fs55l
title: CCDC 2257549: Experimental Crystal Structure Determination

description: Related Article: Ryoga Hojo, Katrina Bergmann, Seja A. Elgadi, Don M. Mayder, Megan A. Emmanuel, Martins S. Oderinde, Zachary M. Hudson|2023|J.Am.Chem.Soc.|145|18366|doi:10.1021/jacs.3c04132
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.23674200
title: Zippi_Shvartsman_et_al_2023_bmi_manual_files

description: <b>Included files:</b>Each file includes LFP (local field potential) data for both animals (‘h’, ‘y’) during a particular type of task control (‘bmi’ or ‘manual’) and time-locked to 500ms before or after a particular event in the task (‘go_cue’ or ‘target’) for each rewarded trial in each day of the task (‘h’: [1-13], ‘y’: [1-22]).<b>File description:</b>Each file includes a Pandas DataFrame, saved as a .feather file. Data can be accessed using Python by calling:<i>import pandas as pd</i><i>pd.read_feather([file name])</i>Each DataFrame has the following columns:<b>control_type:</b> ‘bmi’, ‘manual’, or ‘baseline’<b>event:</b> go cue (‘go_cue’) or target acquisition (‘target’)<b>subj:</b> which animal, ‘h’ or ‘y’<b>day:</b> which day of the session, ‘h’: [1-13], ‘y’: [1-22]<b>roi: </b>region of interest; ‘direct’, ‘dlpfc’, or ‘cd’ where ‘direct’ includes most channels from m1 each day but is specific to channels which had sufficient spiking to be used as input to the BMI decoder<b>ch:</b> electrode channel number, only low-noise channels were included (see Methods for details)<b>n_rewarded_trial:</b> which trial number data segment is from, only successfully completed (rewarded) trials are included<b>time_from_window_ms</b>: go_cue: 0-500ms from go cue, for target: -500-0ms from target acquisition<b>lfp: </b>local field potential value (see Methods for details)
DOI: 10.6084/m9.figshare.24052824
title: Zippi_Shvartsman_et_al_2023_baseline_files

description: <b>Included files:</b>Each file includes LFP (local field potential) data for both animals (‘h’, ‘y’) during rest periods for each day (‘baseline’) without any time-locking (500ms segments were randomly selected from baseline in our analyses). Separate baseline files are included for each animal.<b>File description:</b>Each file includes a Pandas DataFrame, saved as a .feather file. Data can be accessed using Python by calling:<i>import pandas as pd</i><i>pd.read_feather([file name])</i>Each DataFrame has the following columns:<b>control_type:</b> ‘bmi’, ‘manual’, or ‘baseline’<b>subj:</b> which animal, ‘h’ or ‘y’<b>day:</b> which day of the session, ‘h’: [1-13], ‘y’: [1-22]<b>roi: </b>region of interest; ‘direct’, ‘dlpfc’, or ‘cd’ where ‘direct’ includes most channels from m1 each day but is specific to channels which had sufficient spiking to be used as input to the BMI decoder<b>ch:</b> electrode channel number, only low-noise channels were included (see Methods for details)<b>time_from_window_ms</b>: represents every ms from start to end of the recorded rest period<b>lfp: </b>local field potential value (see Methods for details)
DOI: 10.6084/m9.figshare.24052824.v1
title: Zippi_Shvartsman_et_al_2023_baseline_files

description: <b>Included files:</b>Each file includes LFP (local field potential) data for both animals (‘h’, ‘y’) during rest periods for each day (‘baseline’) without any time-locking (500ms segments were randomly selected from baseline in our analyses). Separate baseline files are included for each animal.<b>File description:</b>Each file includes a Pandas DataFrame, saved as a .feather file. Data can be accessed using Python by calling:<i>import pandas as pd</i><i>pd.read_feather([file name])</i>Each DataFrame has the following columns:<b>control_type:</b> ‘bmi’, ‘manual’, or ‘baseline’<b>subj:</b> which animal, ‘h’ or ‘y’<b>day:</b> which day of the session, ‘h’: [1-13], ‘y’: [1-22]<b>roi: </b>region of interest; ‘direct’, ‘dlpfc’, or ‘cd’ where ‘direct’ includes most channels from m1 each day but is specific to channels which had sufficient spiking to be used as input to the BMI decoder<b>ch:</b> electrode channel number, only low-noise channels were included (see Methods for details)<b>time_from_window_ms</b>: represents every ms from start to end of the recorded rest period<b>lfp: </b>local field potential value (see Methods for details)
DOI: 10.25919/mfwm-wp25
title: Discovery and quantitative proteomics data collected from low-gluten beer

description: These datasets were colelcted to measure the gluten and immunogenic epitope containing proteins in the low-gluten containing beer samples.
DOI: 10.5517/ccdc.csd.cc26pf1d
title: CCDC 2046373: Experimental Crystal Structure Determination

description: Related Article: Chang Li, Di Wu, Zhenguo Gao, Wei Chen|2023|CrystEngComm|25|4889|doi:10.1039/D3CE00392B
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.cc26pf2f
title: CCDC 2046374: Experimental Crystal Structure Determination

description: Related Article: Chang Li, Di Wu, Zhenguo Gao, Wei Chen|2023|CrystEngComm|25|4889|doi:10.1039/D3CE00392B
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.cc26pf4h
title: CCDC 2046376: Experimental Crystal Structure Determination

description: Related Article: Chang Li, Di Wu, Zhenguo Gao, Wei Chen|2023|CrystEngComm|25|4889|doi:10.1039/D3CE00392B
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.cc2ch568
title: CCDC 2189319: Experimental Crystal Structure Determination

description: Related Article: Dongshuai Su, Jinxiong Cai, Ping Yin, Siping Pang|2023|CrystEngComm|25|4902|doi:10.1039/D3CE00647F
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.cc2d1dtp
title: CCDC 2205892: Experimental Crystal Structure Determination

description: Related Article: Dongshuai Su, Jinxiong Cai, Ping Yin, Siping Pang|2023|CrystEngComm|25|4902|doi:10.1039/D3CE00647F
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.cc2dln7x
title: CCDC 2222459: Experimental Crystal Structure Determination

description: Related Article: Vishal A. Lohar, Anila M. Menon, Ajay Suresh, Deepak Chopra|2023|CrystEngComm|25|4779|doi:10.1039/D3CE00373F
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.cc2dln8y
title: CCDC 2222460: Experimental Crystal Structure Determination

description: Related Article: Vishal A. Lohar, Anila M. Menon, Ajay Suresh, Deepak Chopra|2023|CrystEngComm|25|4779|doi:10.1039/D3CE00373F
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.cc2dzxcp
title: CCDC 2234243: Experimental Crystal Structure Determination

description: Related Article: Hiroshi Nishimoto, Takeshi Kawase, Jun-ichi Nishida|2023|CrystEngComm|25|4856|doi:10.1039/D3CE00416C
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.cc2dzxdq
title: CCDC 2234244: Experimental Crystal Structure Determination

description: Related Article: Hiroshi Nishimoto, Takeshi Kawase, Jun-ichi Nishida|2023|CrystEngComm|25|4856|doi:10.1039/D3CE00416C
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.cc2f4bpm
title: CCDC 2238500: Experimental Crystal Structure Determination

description: Related Article: Akiyuki Nakano, Vivek S. Raut, Naoki Asao, Akane Ando, Kiyoshi Fujisawa, Hideyuki Higashimura|2023|Cat.Sci.Tech.|13|4968|doi:10.1039/D3CY00170A
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.cc2f4bqn
title: CCDC 2238501: Experimental Crystal Structure Determination

description: Related Article: Akiyuki Nakano, Vivek S. Raut, Naoki Asao, Akane Ando, Kiyoshi Fujisawa, Hideyuki Higashimura|2023|Cat.Sci.Tech.|13|4968|doi:10.1039/D3CY00170A
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.