cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fmvmk
title: CCDC 2253409: Experimental Crystal Structure Determination

description: Related Article: Meenu Murali, Christian Bijani, Jean-Claude Daran, Eric Manoury, Rinaldo Poli|2023|Chemical Science|14|8152|doi:10.1039/D3SC02204H
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: 10.13012/b2idb-6770800_v1
title: Simulation Trajectories for "DNA double helix, a tiny electromotor"

description: Trajectory data for Nature Nanotechnology manuscript "DNA double helix, a tiny electromotor" that demonstrates how an electric field applied along the helical axis of a DNA or RNA molecule will generate an electroosmotic flow that causes the duplex to spin about that axis, much like a turbine.
DOI: 10.5517/ccdc.csd.cc27yvgj
title: CCDC 2084268: Experimental Crystal Structure Determination

description: Related Article: Shashwati Paul, Daniel Adelfinsky, Christophe Salome, Thomas Fessard, M. Kevin Brown|2023|Chemical Science|14|8070|doi:10.1039/D3SC02695G
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: 10.13012/b2idb-6626437_v1
title: Data for the paper "Assessment of spatiotemporal flood risk due to compound precipitation extremes across the contiguous United States"

description: This is the data used in the paper "Assessment of spatiotemporal flood risk due to compound precipitation extremes across the contiguous United States". Code from the Github repository https://github.com/adtonks/precip_extremes can be used with the data here to reproduce the paper's results. v1.0.0 of the code is also archived at https://doi.org/10.5281/zenodo.8104252 This dataset is derived from NOAA-CIRES-DOE 20th Century Reanalysis V3. The NOAA-CIRES-DOE Twentieth Century Reanalysis Project version 3 used resources of the National Energy Research Scientific Computing Center managed by Lawrence Berkeley National Laboratory which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and used resources of NOAA's Remotely Deployed High Performance Computing Systems.
DOI: 10.25345/c5dj58s84
title: MassIVE MSV000092573 - Toll-like receptor activation remodels the cell-surface proteome of human dendritic cells
title: Dendritic cells (DCs) are specialized sentinel and antigen presenting cells coordinating innate and adaptive immunity. Through proteins on their cell surface, DCs sense changes in the environment, internalize pathogens, present processed antigens, and communicate with other immune cells. By combining chemical labeling and quantitative mass spectrometry, we systematically profiled and compared the cell-surface proteomes of human primary conventional DCs (cDCs) in their resting and activated states. Toll-like receptor activation by a lipopeptide globally reshaped the cell-surface proteome of cDCs, with more than one hundred proteins up or down regulated. By simultaneously elevating positive regulators and reducing inhibitory signals across multiple protein families, the remodeling creates a cell-surface milieu promoting immune responses. Still, cDCs maintain the stimulatory-to-inhibitory balance by leveraging a distinct set of inhibitory molecules. This analysis thus uncovers the molecular complexity and plasticity of the cDC cell surface and provides a roadmap for understanding cDC activation and signaling.
DOI: 10.18434/mds2-3023
title: Cutting Force Monitoring Experiment 01
DOI: 10.5281/zenodo.8208811
title: spinus_spinescens_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8208810
title: spinus_spinescens_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23816817
title: Bønnelycke, E-M. S., Giacon, T. A., Bosco, G., Kainerstorfer, J. M., Paganini, M., Ruesch, A., Wu, J., McKnight, J. C. "Cerebral haemodynamic and systemic physiological changes in trained freedivers completing sled-assisted dives to two different depths"

description: Raw NIRS and arterial blood gas data included in the analysis for the following paper: Bønnelycke, E-M. S., Giacon, T. A., Bosco, G., Kainerstorfer, J. M., Paganini, M., Ruesch, A., Wu, J., McKnight, J. C. "Cerebral haemodynamic and systemic physiological changes in trained freedivers completing sled-assisted dives to two different depths".
DOI: 10.48420/23800317
title: International city-level decarbonisation projects November 2022 snapshot

description: This dataset contains a list of international city-level sustainability intervention case studies as part of a PhD research project. This PhD project examines accelerating city-level climate action, and is co-funded by the Greater Manchester Combined Authority (GMCA), with the aim of informing action within Greater Manchester and producing insights for cities worldwide. The list gathers publicly available data on 187 case studies (from sources such as C40 cities and Carbon Disclosure Project). Key details are collated, including the location of the case study and the population of the city it was conducted in. It then maps the case studies on to the six categories taken from the Greater Manchester 5-year Environment Plan 2019-2024. The categories include: Transport, Buildings, Energy, Resilience, Nature, Resources, and Other – the Other category was added to encapsulate those case studies that did not fall into the first six categories. Many of these sustainability projects occupy multiple categories and therefore each has been assigned a primary and secondary category to demonstrate the main area that the case study falls into and the next most relevant area so that they can be easily filtered to find what the user is looking for. There is then a column for GMCA relevant keywords, then a brief description of the project followed by the date it started. If you hover over the description of the intervention, you can see more detail in the comment. The next set of columns describe the carbon saving and recorded carbon saving of each intervention. Then the cost, time to set up, and time to see results columns have data missing as there was insufficient time to find this out. Finally, there is a column that lists all of the co-benefits of implementing each case study and the link to where the information was gathered. Website databases such as C40 cities and Carbon Disclosure Project were used to collate this list.This list outlines examples of the multitude of ways different cities are attempting to decarbonisation across the world. If decision-makers need inspiration in a certain sustainability category this list can be filtered to the specifications needed and then a case study can be studied for information on how cities can decarbonise. It is not an exhaustive list of case studies from around the world, but provides a snapshot of those who have contributed to international databases that monitor the sustainability initiatives of cities.<b>Feedback</b><br>Please could you give feedback on the following:How have you used this case study list?Who sent you this list and have you sent it on to anyone else?Do you have any other comments?Would you like to be notified if this dataset is updated?Contact: harry.barton@manchester.ac.uk
DOI: 10.48420/23800317.v1
title: International city-level decarbonisation projects November 2022 snapshot

description: This dataset contains a list of international city-level sustainability intervention case studies as part of a PhD research project. This PhD project examines accelerating city-level climate action, and is co-funded by the Greater Manchester Combined Authority (GMCA), with the aim of informing action within Greater Manchester and producing insights for cities worldwide. The list gathers publicly available data on 187 case studies (from sources such as C40 cities and Carbon Disclosure Project). Key details are collated, including the location of the case study and the population of the city it was conducted in. It then maps the case studies on to the six categories taken from the Greater Manchester 5-year Environment Plan 2019-2024. The categories include: Transport, Buildings, Energy, Resilience, Nature, Resources, and Other – the Other category was added to encapsulate those case studies that did not fall into the first six categories. Many of these sustainability projects occupy multiple categories and therefore each has been assigned a primary and secondary category to demonstrate the main area that the case study falls into and the next most relevant area so that they can be easily filtered to find what the user is looking for. There is then a column for GMCA relevant keywords, then a brief description of the project followed by the date it started. If you hover over the description of the intervention, you can see more detail in the comment. The next set of columns describe the carbon saving and recorded carbon saving of each intervention. Then the cost, time to set up, and time to see results columns have data missing as there was insufficient time to find this out. Finally, there is a column that lists all of the co-benefits of implementing each case study and the link to where the information was gathered. Website databases such as C40 cities and Carbon Disclosure Project were used to collate this list.This list outlines examples of the multitude of ways different cities are attempting to decarbonisation across the world. If decision-makers need inspiration in a certain sustainability category this list can be filtered to the specifications needed and then a case study can be studied for information on how cities can decarbonise. It is not an exhaustive list of case studies from around the world, but provides a snapshot of those who have contributed to international databases that monitor the sustainability initiatives of cities.<b>Feedback</b><br>Please could you give feedback on the following:How have you used this case study list?Who sent you this list and have you sent it on to anyone else?Do you have any other comments?Would you like to be notified if this dataset is updated?Contact: harry.barton@manchester.ac.uk
DOI: 10.6084/m9.figshare.21400155
title: FIGSHARE DATA AUGUST.xlsx

description: Psychosis Prevalence (2019/20) and Risk Factors, 983 London MSOAs
DOI: 10.6084/m9.figshare.21400155.v2
title: FIGSHARE DATA AUGUST.xlsx

description: Psychosis Prevalence (2019/20) and Risk Factors, 983 London MSOAs
DOI: 10.6084/m9.figshare.21400155.v1
title: DATA.xlsx

description: Prevalence and Risk Factors, 983 London MSOAs
DOI: 10.34740/kaggle/dsv/6237850
title: Cristiano Ronaldo | All Club Goals
DOI: 10.18112/openneuro.ds004653.v1.0.0
title: test
DOI: 10.18112/openneuro.ds004653.v1.0.1
title: test
DOI: 10.18112/openneuro.ds004653.v1.0.2
title: test
DOI: 10.18112/openneuro.ds004653.v1.0.3
title: test
DOI: 10.18112/openneuro.ds004653.v1.0.4
title: test
DOI: 10.21227/qz96-yh44
title: e-FLASH

description: The increasing availability of multimodal data holds many promises for developments in millimeter-wave (mmWave) multiple-antenna systems by harnessing the potential for enhanced situational awareness. Specifically, inclusion of non-RF modalities to complement RF-only data in communications-related decisions like beam selection may speed up decision making in situations where an exhaustive search, spanning all candidate options, is required by the standard. However, to accelerate research in this topic, there is a need to collect real-world datasets in a principled manner. This article presents an experimentally obtained dataset, composed of 23 GB of data, which aids in beam selection in vehicle-to-everything mmWave bands, with the goal of facilitating machine learning (ML) in the wireless communication required for autonomous driving. Beyond this specific example, the article describes methodologies of creating such datasets that use time synchronized and heterogeneous types of LiDAR, GPS, and camera images, paired with the RF ground truth data of selected beams in the mmWave band. While we use beam selection as the primary demonstrator, we also discuss how multimodal datasets may be used in other ML-based PHY-layer optimization areas, such as beamforming and localization.
DOI: 10.6084/m9.figshare.23823678.v1
title: Supplementary Table 1

description: Supplementary Table 1
DOI: 10.6084/m9.figshare.23823678
title: Supplementary Table 1

description: Supplementary Table 1
DOI: 10.34683/sr2634
title: StoneRounds Case: SR2634
title: AD Retinitis Pigmentosa

description: StoneRounds Case SR2634 was diagnosed with AD Retinitis Pigmentosa likely caused by mutations in the gene: RHO.
DOI: 10.34683/sr1119
title: StoneRounds Case: SR1119
title: XL Retinitis Pigmentosa

description: StoneRounds Case SR1119 was diagnosed with XL Retinitis Pigmentosa likely caused by mutations in the gene: RPGR.
DOI: 10.6073/pasta/03029db50ae4a977c20267ebb13224ea
title: Community-level flowering & fitness data across an elevational gradient, Rocky Mountain Biological Lab, 2021-2022

description: We collected data at three sites in Washington Gulch near the Rocky Mountain Biological Laboratory (RMBL, Gothic, Colorado, USA) from June to August 2021 and 2022. RMBL is located in the East River valley of the West Elk mountains, approximately 10 kilometers from Crested Butte, Colorado. Study sites were located at 2815 m (38°53'50"N, 106°58'43"W), 3165 m (38°57'38"N, 107°01'53"W) and 3380 m (38°58'10"N, 107°01'53"W) in elevation. All sites are approximately 50 m2 and have similar slope and aspect (Sloat et al. 2015). We randomly defined five 1.2 m x 1.2 m plots within each site. Within the plots, we marked all newly open flowering units once per week from early June to mid-August. We counted flowering units as either a single flower (most species), a flowering head (Asteraceae), or an umbel (Apiaceae). We only counted flowering units once as they opened, so counts were not cumulative. We quantified fitness as successful fruit and seed development at the end of the season. When flowers began to produce fruits, we counted the number of units with fruit, the number of developed fruits produced by each flowering unit, and the number of seeds per fruit. We also recorded the week of flowering for each fruit. We could not count fruits and seeds for all species because they disappeared, were consumed too quickly, or did not produce seeds during our field season.
DOI: 10.5281/zenodo.8116913
title: Molecular profiling of sponge deflation reveals an ancient relaxant-inflammatory response

description: A hallmark of animals is the coordination of whole-body movement. Neurons and muscles are central to this, yet coordinated movements also exist in sponges that lack these cell types. Sponges are sessile animals with a complex canal system for filter-feeding. They undergo whole-body movements resembling “contractions'' that lead to canal closure and water expulsion. Here, we combine 3D optical coherence microscopy, pharmacology, and functional proteomics to elucidate anatomy, molecular physiology, and control of these movements. We show that they are driven by the relaxation of actomyosin stress fibers in epithelial canal cells, which leads to whole-body deflation via collapse of the incurrent and expansion of the excurrent system, controlled by an Akt/NO/PKG/A pathway. A concomitant increase in reactive oxygen species and secretion of proteinases and cytokines indicate an inflammation-like state reminiscent of vascular endothelial cells experiencing oscillatory shear stress. This suggests an ancient relaxant-inflammatory response of perturbed fluid-carrying systems in animals. File descriptions: pharma_*.avi: Exemplary videos for pharmacological treatments of Spongilla <em>lacustris</em>, related to Fig. 2 OCM_tomographic_scan: 2D scan through an unsegmented sponge body. First a x-z scan, afterwards a x-y scan. ink_treatment_suppl_figS3.mov: Video of ink treated <em>Spongilla lacustris</em> specimen, related to Fig. S3 S_lacustris_annotations_emapper: EggNOG mapper (v2.1.9) results used for GO term enrichment analysis S_lacustris_proteome: Proteome file used as database for proteomic searches S_lacustris_scRNAseq.h5ad: file containing scRNAseq and cell type information of <em>Spongilla lacustris.</em> Suppl_file_*.xlsx: Tables of proteomics (TPP, phophoproteomics, secretomics) results
DOI: 10.5281/zenodo.8116912
title: Molecular profiling of sponge deflation reveals an ancient relaxant-inflammatory response

description: A hallmark of animals is the coordination of whole-body movement. Neurons and muscles are central to this, yet coordinated movements also exist in sponges that lack these cell types. Sponges are sessile animals with a complex canal system for filter-feeding. They undergo whole-body movements resembling “contractions'' that lead to canal closure and water expulsion. Here, we combine 3D optical coherence microscopy, pharmacology, and functional proteomics to elucidate anatomy, molecular physiology, and control of these movements. We show that they are driven by the relaxation of actomyosin stress fibers in epithelial canal cells, which leads to whole-body deflation via collapse of the incurrent and expansion of the excurrent system, controlled by an Akt/NO/PKG/A pathway. A concomitant increase in reactive oxygen species and secretion of proteinases and cytokines indicate an inflammation-like state reminiscent of vascular endothelial cells experiencing oscillatory shear stress. This suggests an ancient relaxant-inflammatory response of perturbed fluid-carrying systems in animals. File descriptions: pharma_*.avi: Exemplary videos for pharmacological treatments of Spongilla <em>lacustris</em>, related to Fig. 2 OCM_tomographic_scan: 2D scan through an unsegmented sponge body. First a x-z scan, afterwards a x-y scan. ink_treatment_suppl_figS3.mov: Video of ink treated <em>Spongilla lacustris</em> specimen, related to Fig. S3 S_lacustris_annotations_emapper: EggNOG mapper (v2.1.9) results used for GO term enrichment analysis S_lacustris_proteome: Proteome file used as database for proteomic searches S_lacustris_scRNAseq.h5ad: file containing scRNAseq and cell type information of <em>Spongilla lacustris.</em> Suppl_file_*.xlsx: Tables of proteomics (TPP, phophoproteomics, secretomics) results
DOI: 10.18112/openneuro.ds004653.v1.0.5
title: test
DOI: 10.6084/m9.figshare.23823693
title: <b>Supplementary Material – </b><b>Funga Capixaba: Ascomycota and lichen-forming fungi</b>

description: <b>Table S1 –</b> <b>List of macroscopic Ascomycota and lichen-forming fungi from Espírito Santo state.</b><br><b>This Supplementary Material is part of the Original Paper published at Rodriguesia - DOI:</b> <b>http://dx.doi.org/10.1590/2175-7860202374044</b>
DOI: 10.14272/mvvvovplzkmnho-uhfffaoysa-n.1
title: C20H15NO2S
title: InChI=1S/C20H15NO2S/c1-2-23-19-18(17(22)14-13-15-9-5-3-6-10-15)24-20(21-19)16-11-7-4-8-12-16/h3-12H,2H2,1H3

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/C20H15NO2S/c1-2-23-19-18(17(22)14-13-15-9-5-3-6-10-15)24-20(21-19)16-11-7-4-8-12-16/h3-12H,2H2,1H3, and canonical SMILES descriptor[cheminf_000007]: CCOc1nc(sc1C(=O)C#Cc1ccccc1)c1ccccc1, 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-34387 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]: Melting point descriptor[CHEMINF_000256]: 123.5 (°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:0000287 | fluorescence spectroscopy (fluorimetry) CHMO:0000292 | ultraviolet-visible spectrophotometry (UV-VIS) CHMO:0000796 | high-performance liquid chromatography-mass spectrometry (HPLC-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/mvvvovplzkmnho-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C20H15NO2S/c1-2-23-19-18(17(22)14-13-15-9-5-3-6-10-15)24-20(21-19)16-11-7-4-8-12-16/h3-12H,2H2,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (300 MHz, CDCl3, ppm) δ = 8.08–7.95 (m, 2H), 7.75–7.63 (m, 2H), 7.57–7.39 (m, 6H), 4.75 (q, J = 7.1 Hz, 2H), 1.55 (t, J = 7.1 Hz, 3H).
DOI: 10.14272/mvvvovplzkmnho-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C20H15NO2S/c1-2-23-19-18(17(22)14-13-15-9-5-3-6-10-15)24-20(21-19)16-11-7-4-8-12-16/h3-12H,2H2,1H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2925 ν(-CH3)(w), 2199 ν(-CΞC)(s), 1566 ν(-C=O)(vs), 1523 ν(-C=C)(vs), 1489 (m), 1473 (w), 1442 (w), 1391 (vs), 1379 (vs), 1350 (vs), 1302 (s), 1245 (w), 1215 (m), 1183 (w), 1173 (w), 1161 (w), 1111 (vs), 1075 (m), 1028 (s), 1014 (m), 969 (s), 926 (w), 918 (w), 862 (w), 855 (w), 815 (w), 795 (m), 789 (m), 773 (s), 754 (vs), 736 (vs), 714 (w), 682 (vs), 666 (s), 656 (m) cm–1.
DOI: 10.14272/mvvvovplzkmnho-uhfffaoysa-n/chmo0000292
title: ultraviolet-visible spectrophotometry (UV-VIS) ()
title: InChI=1S/C20H15NO2S/c1-2-23-19-18(17(22)14-13-15-9-5-3-6-10-15)24-20(21-19)16-11-7-4-8-12-16/h3-12H,2H2,1H3

description: dataset for ultraviolet-visible spectrophotometry (UV-VIS) UV-VIS (CHCl3), λmax = 380 nm
DOI: 10.14272/mvvvovplzkmnho-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C20H15NO2S/c1-2-23-19-18(17(22)14-13-15-9-5-3-6-10-15)24-20(21-19)16-11-7-4-8-12-16/h3-12H,2H2,1H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (76 MHz, CDCl3, ppm) δ = 14.9, 67.1, 87.5, 91.7, 114.6, 120.6, 126.3, 128.3, 128.8, 130.1 (2C), 131.5 (2C), 132.5 (2C), 132.6 (2C), 165.1, 167.1, 170.4.
DOI: 10.14272/mvvvovplzkmnho-uhfffaoysa-n/chmo0000287
title: fluorescence spectroscopy (fluorimetry) ()
title: InChI=1S/C20H15NO2S/c1-2-23-19-18(17(22)14-13-15-9-5-3-6-10-15)24-20(21-19)16-11-7-4-8-12-16/h3-12H,2H2,1H3

description: dataset for fluorescence spectroscopy (fluorimetry) UV-VIS (CHCl3), λFL = 447 nm
DOI: 10.14272/mvvvovplzkmnho-uhfffaoysa-n/chmo0000796
title: high-performance liquid chromatography-mass spectrometry (HPLC-MS) ()
title: InChI=1S/C20H15NO2S/c1-2-23-19-18(17(22)14-13-15-9-5-3-6-10-15)24-20(21-19)16-11-7-4-8-12-16/h3-12H,2H2,1H3

description: dataset for high-performance liquid chromatography-mass spectrometry (HPLC-MS)