cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2989xt
title: CCDC 2123156: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc2989yv
title: CCDC 2123157: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc2989zw
title: CCDC 2123158: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b0y
title: CCDC 2123159: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b1z
title: CCDC 2123160: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b20
title: CCDC 2123161: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b31
title: CCDC 2123162: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b42
title: CCDC 2123163: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b53
title: CCDC 2123164: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b64
title: CCDC 2123165: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b75
title: CCDC 2123166: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b86
title: CCDC 2123167: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298b97
title: CCDC 2123168: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298bb8
title: CCDC 2123169: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc298bc9
title: CCDC 2123170: Experimental Crystal Structure Determination

description: Related Article: Hyeonglim Seo, Moritz K. Jackl, Mark Kalaj, Seth M. Cohen|2022|Inorg.Chem.|61|7631|doi:10.1021/acs.inorgchem.2c00891
DOI: 10.5517/ccdc.csd.cc29xxy3
title: CCDC 2142966: Experimental Crystal Structure Determination

description: Related Article: Anitha S. Gowda, Tia S. Lee, Michael C. Rosko, Jeffrey L. Petersen, Felix N. Castellano, Carsten Milsmann|2022|Inorg.Chem.|61|7338|doi:10.1021/acs.inorgchem.2c00182
DOI: 10.5517/ccdc.csd.cc29xxz4
title: CCDC 2142967: Experimental Crystal Structure Determination

description: Related Article: Anitha S. Gowda, Tia S. Lee, Michael C. Rosko, Jeffrey L. Petersen, Felix N. Castellano, Carsten Milsmann|2022|Inorg.Chem.|61|7338|doi:10.1021/acs.inorgchem.2c00182
DOI: 10.5517/ccdc.csd.cc29xy06
title: CCDC 2142968: Experimental Crystal Structure Determination

description: Related Article: Anitha S. Gowda, Tia S. Lee, Michael C. Rosko, Jeffrey L. Petersen, Felix N. Castellano, Carsten Milsmann|2022|Inorg.Chem.|61|7338|doi:10.1021/acs.inorgchem.2c00182
DOI: 10.5517/ccdc.csd.cc2b5g77
title: CCDC 2150198: Experimental Crystal Structure Determination

description: Related Article: Guilherme M. D. M. Rúbio, Tristan T. Y. Tan, Alexander Prado-Roller, Jia Min Chin, Michael R. Reithofer|2022|Inorg.Chem.|61|7448|doi:10.1021/acs.inorgchem.2c00509
DOI: 10.5517/ccdc.csd.cc2b5g88
title: CCDC 2150199: Experimental Crystal Structure Determination

description: Related Article: Guilherme M. D. M. Rúbio, Tristan T. Y. Tan, Alexander Prado-Roller, Jia Min Chin, Michael R. Reithofer|2022|Inorg.Chem.|61|7448|doi:10.1021/acs.inorgchem.2c00509
DOI: 10.5517/ccdc.csd.cc2b5g99
title: CCDC 2150200: Experimental Crystal Structure Determination

description: Related Article: Guilherme M. D. M. Rúbio, Tristan T. Y. Tan, Alexander Prado-Roller, Jia Min Chin, Michael R. Reithofer|2022|Inorg.Chem.|61|7448|doi:10.1021/acs.inorgchem.2c00509
DOI: 10.5517/ccdc.csd.cc2b5gbb
title: CCDC 2150201: Experimental Crystal Structure Determination

description: Related Article: Guilherme M. D. M. Rúbio, Tristan T. Y. Tan, Alexander Prado-Roller, Jia Min Chin, Michael R. Reithofer|2022|Inorg.Chem.|61|7448|doi:10.1021/acs.inorgchem.2c00509
DOI: 10.5517/ccdc.csd.cc2b5gcc
title: CCDC 2150202: Experimental Crystal Structure Determination

description: Related Article: Guilherme M. D. M. Rúbio, Tristan T. Y. Tan, Alexander Prado-Roller, Jia Min Chin, Michael R. Reithofer|2022|Inorg.Chem.|61|7448|doi:10.1021/acs.inorgchem.2c00509
DOI: 10.5517/ccdc.csd.cc29bsp4
title: CCDC 2125536: Experimental Crystal Structure Determination

description: Related Article: Liang Zhao, Zenggang Du, Guanfeng Ji, Yefei Wang, Wei Cai, Cheng He, Chunying Duan|2022|Inorg.Chem.|61|7256|doi:10.1021/acs.inorgchem.1c03813
DOI: 10.18739/a2zk55n7k
title: Dissolved O2 (Oxygen) concentration and O2/N2 (Oxygen/Nitrogen) saturation ratio in surface seawater measured in the Bering, Chukchi, and Beaufort Seas October-November, 2020

description: These data files contain surface dissolved O2 (Oxygen) concentration and dissolved O2/N2 (Oxygen/Nitrogen) saturation ratio for the Bering, Chukchi, and Beaufort Seas with relevant accompanying ancillary information. The data are useful for understanding the balance of physical and biological controls driving surface ocean dissolved O2 concentrations and for calculating net biological community production rates. The data were collected with funding from NSF (National Science Foundation) Award 1928684 “High Resolution Observing of Arctic Net Community Productivity with Ships of Opportunity” Data were collected between October 17th, 2020 and November 16th, 2020 from the surface underway seawater supply of R/V (Research Vessel) Sikuliaq on Cruise ID (Identifier) SKQ202014S. Data were collected near-continuously using Pro-Oceanus miniTDGP (total dissolved gas pressure instrument), a SBE45 thermosalinograph, and a SBE43 dissolved O2 sensor, with occasional outages for instrument maintenance or flow disruptions. The cruise departed and returned from Seward, AK (Alaska) and sampled waters of the Bering, Chukchi and southern Beaufort Seas.
DOI: 10.5281/zenodo.6565140
title: Copper River, Alaska, Chinook Salmon Inriver Abundance Estimate 2018-2021 DATA ARCHIVE

description: Long-term monitoring of returning adult Chinook salmon (<em>Oncorhynchus tshawytscha</em>) abundance on the Copper River, AK, has been conducted using fishwheels and two-sample mark-recapture methods since 2003. This data archive is from from the 2018-2021 field seasons. The annual objective was to estimate the inriver abundance of Copper River Chinook salmon such that the estimate was within 25% of the true abundance 95% of the time. This data represents annual catch, bycatch, tagging site data, recapture site data, session data, QC check tables, CPUE, mark-recapture matrix, mark-recapture stratification tables, effort, and daily catch matrix. See annual report for methodology, analyses and results @ http://akssf.org/default.aspx?id=3477 or contact the Alaska Sustainable Salmon Fund or U.S. Fish and Wildlife Service Office of Subsistence Management Fisheries Resource Monitoring Program or Native Village of Eyak DENR.
description: {"references": ["Piche. M. J., J. B. Paley, J. C. Whissel, and J. J. Smith. 2022. Estimating the inriver abundance of Copper River Chinook salmon, 2021 Final Report. U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program (Study No. 18-504), Anchorage, Alaska.", "Piche. M. J., C.R. Weaver, J. C. Whissel, and J. J. Smith. 2021. Estimating the inriver abundance of Copper River Chinook salmon, 2020 annual report. U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program (Study No. 18-504), Anchorage, Alaska.", "Piche. M. J., J. C. Whissel, and J. J. Smith. 2021. Estimating the inriver abundance of Copper River Chinook salmon, 2019 annual report. U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program (Study No. 18-504), Anchorage, Alaska.", "Piche. M. J., J. C. Whissel, and J. J. Smith. 2019. Estimating the inriver abundance of Copper River Chinook salmon, 2018 annual report. U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program (Study No. 18-504), Anchorage, Alaska."]}
DOI: 10.5281/zenodo.6565139
title: Copper River, Alaska, Chinook Salmon Inriver Abundance Estimate 2018-2021 DATA ARCHIVE

description: Long-term monitoring of returning adult Chinook salmon (<em>Oncorhynchus tshawytscha</em>) abundance on the Copper River, AK, has been conducted using fishwheels and two-sample mark-recapture methods since 2003. This data archive is from from the 2018-2021 field seasons. The annual objective was to estimate the inriver abundance of Copper River Chinook salmon such that the estimate was within 25% of the true abundance 95% of the time. This data represents annual catch, bycatch, tagging site data, recapture site data, session data, QC check tables, CPUE, mark-recapture matrix, mark-recapture stratification tables, effort, and daily catch matrix. See annual report for methodology, analyses and results @ http://akssf.org/default.aspx?id=3477 or contact the Alaska Sustainable Salmon Fund or U.S. Fish and Wildlife Service Office of Subsistence Management Fisheries Resource Monitoring Program or Native Village of Eyak DENR.
description: {"references": ["Piche. M. J., J. B. Paley, J. C. Whissel, and J. J. Smith. 2022. Estimating the inriver abundance of Copper River Chinook salmon, 2021 Final Report. U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program (Study No. 18-504), Anchorage, Alaska.", "Piche. M. J., C.R. Weaver, J. C. Whissel, and J. J. Smith. 2021. Estimating the inriver abundance of Copper River Chinook salmon, 2020 annual report. U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program (Study No. 18-504), Anchorage, Alaska.", "Piche. M. J., J. C. Whissel, and J. J. Smith. 2021. Estimating the inriver abundance of Copper River Chinook salmon, 2019 annual report. U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program (Study No. 18-504), Anchorage, Alaska.", "Piche. M. J., J. C. Whissel, and J. J. Smith. 2019. Estimating the inriver abundance of Copper River Chinook salmon, 2018 annual report. U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program (Study No. 18-504), Anchorage, Alaska."]}
DOI: 10.17632/tptf5hyvdn.1
title:  Isotopic signature from species envolved in trophic relationships with the crab Libinia ferreirae and its symbionts, in Cananéia, São Paulo - Brazil.

description: Isotopic values of δ13C and δ15N for all organisms separated in guilds groups collected through trawl fishing at sea of Cananéia in the São Paulo State, Brazil (25º80′43′′S, 47º85′34′′W), during the winter/2018 and summer/2019, that was envolved in trophic relationships with the crab Libinia ferreirae as a jellyfish simbiont and as a epibiont host.
This data was used to test the type of relationship the carb L. ferreirae have with the host jellyfish and with the differentes epibionts that this crab have on the carapace during the adult phase.
DOI: 10.17632/tptf5hyvdn.2
title: Isotopic signature from species involved in trophic relationships with the crab Libinia ferreirae and its symbionts, in Cananéia, São Paulo - Brazil.

description: Isotopic values of δ13C and δ15N for all organisms separated in guilds groups collected through trawl fishing at sea of Cananéia in the São Paulo State, Brazil (25º80′43′′S, 47º85′34′′W), during the winter/2018 and summer/2019, that was envolved in trophic relationships with the crab Libinia ferreirae as a jellyfish simbiont and as a epibiont host.
This data was used to test the type of relationship the carb L. ferreirae have with the host jellyfish and with the differentes epibionts that this crab have on the carapace during the adult phase.
DOI: 10.17632/t6ryv6crjd.1
title: Macrolitter along a highly frequented roadway

description: Road runoff constitutes a potential non-point source of pollution as roadside ditches may connect land-based sources to waterway. Here is a dataset of macrolitter collected in a retention pond from a portion of 800 m of a highly frequented highway in Nantes, France (Cheviré Bridge: Lat. 47.1849; Long. -1.6144). Roadtraffic is exclusively dedicated to motorists with a constant traffic of around 90,000 vehicles per day (10% heavy vehicles) and no pedestrians are allowed. Macrolitter > 1 cm were collected (n = 36,439 items) over one year (August 2020/July 2021) during 10 field campaigns, covering the second lockdown related to COVID-19 pandemic. Macrolitter were classified according to TSG-ML/OSPAR (238 categories). 84% items were plastics by count. Dry mass of each category was also reported for a total mass of 88.5 kg (60% of plastics by mass). Categories are gathered relative to their main material type including artificial polymes (i.e. plastics), rubber, textile, paper/cardboard, manufactured wood, metal, glass/ceramic and other.

Top 10 items represent 92% by count of the total items and Top 10 items by mass represent 72% of the total mass. The most specific items featuring in both Top 10 are plastic fragments (31% by count and 24% by mass), EPS fragments (17% by count and 4% by count), foam packaging (11% by count and 6% by mass), Cardboards (6% by count and 13% by mass), wood fragments (4% by count and 6% by mass), industrial plastic sheets (1% by count and 5% by mass) and rubber fragments(1% by count and 4% by mass). Cigarette butts, paper fragments and sweet wrappers are only featuring in the Top 10 by count and represent respectively 18%, 3% and 2%. Other textiles, plastic bags and metal fragments are only featuring in the Top 10 by mass and represent respectively 4%, 4% and 3%.

Those data are relevant for studies aiming to estimate macrolitter fluxes related to roadsides together with their sources. Although individual protections like single use masks were found, no specific trends were highlighted during the lockdown.
DOI: 10.25345/c5k06x48w
title: MassIVE MSV000089498 - Cyclophilin B function elucidated by electrochemical sensors
title: We have investigated the function of CypB through negative ionization high resolution LCMS and examined the purity of a CypB protein with intact mass analyzes on a Q Exactive mass spectrometer.
DOI: 10.18739/a2q52ff1v
title: Dissolved O2 concentration and O2/N2 saturation ratio in surface seawater measured in the Bering and Chukchi Seas May-June 2021

description: These data files contain surface dissolved oxygen (O2) concentration and dissolved oxygen/nitrogen (O2/N2) saturation ratio for the Bering and Chukchi Seas with relevant accompanying ancillary information. The data are useful for understanding the balance of physical and biological controls driving surface ocean dissolved O2 concentrations and for calculating net biological community production rates. The data were collected with funding from National Science Foundation (NSF) Award 1928684 “High Resolution Observing of Arctic Net Community Productivity with Ships of Opportunity.” Data were collected between May 21st, 2021 and June 13th, 2021 from the surface uncontaminated seawater supply of R/V Sikuliaq on Cruise ID SKQ202108S. Data were collected near-continuously using Pro-Oceanus miniTDGP (total dissolved gas pressure instrument), a SBE45 thermosalinograph, and an Aanderaa optode (4330) dissolved O2 sensor, with occasional outages for instrument maintenance or flow disruptions. The cruise departed from Dutch Harbor, Alaska (AK) and returned to Seward, AK and sampled waters of the Bering and Chukchi Seas.
DOI: 10.6084/m9.figshare.14214494.v1
title: Bacterial data for NZ urbanization microbial metacommunity

description: Data for analyses performed in the Urbanization increases diversity in a nectar microbial metacommunity.<br>bacterial_OTU_df.csv - OTU tablebacterial_sample_metadata.csv - sample metadatabacterial_tax_table.csv - taxonomic table
DOI: 10.15131/shef.data.19299539
title: Human-Robot Video Data from a Manufacturing Factory

description: This repository provides videos collected when an operator or several operators are interacting with an UR5 robot in a manufacturing factory. This data repository could be used for object detection, classification and segmentation tasks in videos.
DOI: 10.5281/zenodo.6565175
title: supplementary table 1

description: 792 miRNAs targeted by METTL1 a
DOI: 10.5281/zenodo.6565174
title: supplementary table 1

description: 792 miRNAs targeted by METTL1 a
DOI: 10.6084/m9.figshare.19289594.v1
title: Tracing of fly larval segments

description: This is a tracing data of segment boundaries in freely crawling third instar Drosophila larvae, which was used on Sun et al.
DOI: 10.18112/openneuro.ds004069.v1.0.2
title: Queensland Twin Adolescent Brain (QTAB)
DOI: 10.6084/m9.figshare.19561216.v10
title: Supplementary data

description: <em><strong>Supplementary datas of elemene liposome injection for the prevention and treatment of colorectal adenoma carcinogenesis based on network pharmacological analysis and experimental verification. Supplementary information: S1-S9 and Supplementary information legends (including supplementary Figures).</strong></em>
DOI: 10.5519/qd.z5kj6nma
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (18002 records)
DOI: 10.18112/openneuro.ds004069.v1.0.3
title: Queensland Twin Adolescent Brain (QTAB)
DOI: 10.3886/e125341v1-118680
title: Data and Code for: Political Fragmentation and Government Stability. Evidence from Local Governments in Spain

description: <br> <pre>This paper studies how political fragmentation affects government stability. Using a regression-discontinuity design, we show that each additional party with representation in the local parliament increases the probability that the incumbent government is unseated by 5 percentage points. The entry of an additional party affects stability by increasing both the probability of a single-party majority and the instability of governments when such a majority is not feasible. We interpret our results in light of a bargaining model of coalition formation featuring government instability. <br><br><br></pre>
description: Spanish Municipalities <br>.
DOI: 10.6084/m9.figshare.19799734
title: Data_survival_Narisara

description: Data_survival_cannabis
DOI: 10.6084/m9.figshare.19799734.v1
title: Data_survival_Narisara

description: Data_survival_cannabis
DOI: 10.6084/m9.figshare.17203961
title: Kokopu habitat data

description: Relevant spotlighting and habitat data used in "<em>Predicting biomass of resident kōkopu (Galaxias) populations using local habitat characteristics</em>"
DOI: 10.6084/m9.figshare.17203961.v1
title: Kokopu habitat data

description: Relevant spotlighting and habitat data used in "<em>Predicting biomass of resident kōkopu (Galaxias) populations using local habitat characteristics</em>"
DOI: 10.6084/m9.figshare.19090553
title: Example dataset for the CommPath R package tutorial

description: This is the example dataset used in the CommPath R package tutorial. We downloaded the processed scRNA-seq data on hepatocellular carcinoma (HCC) samples from the paper of Sharma, et al., 2020, and then randomly selected the expression data of 3000 cells across the top 5000 highly variable genes from the tumor and normal tissues, respectively.
DOI: 10.6084/m9.figshare.19090553.v4
title: Example dataset for the CommPath R package tutorial

description: This is the example dataset used in the CommPath R package tutorial. We downloaded the processed scRNA-seq data on hepatocellular carcinoma (HCC) samples from the paper of Sharma, et al., 2020, and then randomly selected the expression data of 3000 cells across the top 5000 highly variable genes from the tumor and normal tissues, respectively.
DOI: 10.6084/m9.figshare.19799791
title: PKD2 A803* Array

description: 96 individuals of <em>PKD2</em> p.Arg803* array data
DOI: 10.6084/m9.figshare.19799791.v2
title: PKD2 A803* Array

description: 96 individuals of <em>PKD2</em> p.Arg803* array data
DOI: 10.6084/m9.figshare.19799791.v1
title: PKD2 founder mutation is the most common mutation of polycystic kidney disease in Taiwan

description: 96 individuals of <em>PKD2</em> p.Arg803* array data
DOI: 10.5281/zenodo.6565237
title: Data for "Functional Dynamics of Substrate Recognition in TEM Beta-Lactamase"

description: Molecular dynamics data associated with the publication: "Functional Dynamics of Substrate Recognition in TEM Beta-Lactamase" Trajectores were generated in GROMACS, and the carbon alpha coordinates were extracted and aligned with the JEDi analysis software. Details of the simulations and analysis are given in the publication. Data in apo.zip contains trajectories for 32 total trajectories of TEM-1, TEM-2, TEM-10, and TEM-52 beta-lactamase, each starting form different 8 crystal structures Data in holo.zip contains 16 trajectories of TEM-1, TEM-2, TEM-10, and TEM-52 beta-lactamase in complex with ampicillin, amoxicillin, cefotaxime, and ceftazidime each. Trajectories files are in comma delimited format, with rows representing degrees for freedom (789 total), and columns representing samples (10000 per trajectory file). Supervised Projective Learning for Orthogonal Completeness (SPLOC) software for analysis as performed in the publication can be found at: https://github.com/BioMolecularPhysicsGroup-UNCC/MachineLearning/tree/master/SPLOC