cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17035853.v1
title: October 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>October, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.17035853
title: October 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>October, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6073/pasta/53bba8f26d84d70325612b773c98c10d
title: Bonanza Creek LTER: Hourly Ultraviolet Radiation (UV) Measurements from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska

description: In 1993, Eppley total UV radiometers (TUVR) were installed at each climate station on 21 April at LTER1 and 5 May at LTER2. These radiometers consist of a Weston selenium barrier-layer photoelectric cell with a sealed-in quartz window, a bandpass filter to restrict the wavelength response of the photcell to the designed range (295-385 nm), and a diffusing disc of Virgin Teflon to reduce the light intensity at the filtered photocell and also to improve the adherence of the instrument to the Lambert cosine law. The disc is nearly uniform diffusing over the wavelength range of interest, as well as geometrically within the system. The terminals of the photocell are connected through a precision resistor and the signal measured as a voltage drop across the resistor. Winter measurements may be inaccurate due to snow cover.
DOI: 10.25919/yyex-t381
title: RV Investigator Voyage IN2017_V01 CTD Data

description: This record describes the Conductivity-Temperature-Depth (CTD) data collected from the Marine National Facility RV Investigator voyage IN2017_V01 titled: "Interactions of the Totten Glacier with the Southern Ocean through multiple glacial cycles." The voyage took place from Hobart (TAS) to Hobart between the 14th January and 5th March, 2017. Data for 33 deployments were acquired during this voyage using a Seabird SBE911 CTD, fitted with 36 twelve litre bottles on the rosette sampler. Casts 1-10, 13 – 33 were acquired using the Seabird SBE911 CTD unit 20, and casts 11 and 12 were acquired using the Seabird SBE911 CTD unit 22. CTD and sensors were changed in efforts to remediate sensor issues. CSIRO -supplied calibration factors were used to compute the pressures, and preliminary temperature and conductivity values. The data were subjected to automated QC to remove spikes and out-of-range values. Processing was completed using CapPro processing software, version 2.9. For the duration of the voyage significant spikes were observed across all recorded sensor channels. Investigation strongly suggests electrical interference was the cause of the data spikes, as mitigation measures have alleviated the issue. Care was taken in post processing to remove the spikes while maintaining true data features. The nature of the spiking was primarily extreme values persisting for a single scan, which were suitably detected and flagged by evaluating the second-difference of the data. An additional filter was also applied to the data which evaluated the median and standard deviation of the conductivity over a moving window, which was used to detect extreme changes in the sensor values characteristic of the noise induced spikes. The final conductivity calibration was based on two deployment groupings, due to sensor changes during the voyage. The final calibration for casts 1-13 from the secondary sensor had a standard deviation (S.D) of 0.0010393 PSU, within our target of ‘better than 0.002 PSU’. The final calibration for casts 14-33 from the secondary sensor had a standard deviation (S.D) of 0.0020084 PSU. The standard product of 1dbar binned averaged were produced using data from the secondary sensors. The final Oxygen calibration from the primary sensor had a S.D of 0.85991 uM. The agreement between the sensor and bottle data was good. Both Oxygen sensors calibrated closely. The Biospherical photosynthetically active radiation (PAR), Wetlabs Transmissometer, the Wetlabs ECO chlorophyll, and O&A IMU sensors were also installed on the auxiliary A/D channels of the CTD. The collected data were subsequently processed and archived within the CSIRO Oceans and Atmosphere (O&A) Information and Data Centre (IDC) in Hobart. Additional information regarding this dataset may be contained in the Voyage Summary and the CTD Data Processing Report.
DOI: 10.5281/zenodo.5708878
title: GRRIP WP5 - Researcher Survey

description: This dataset includes the full survey data of the GRRIP project's WP5 Researcher Survey, including documentation about using the dataset and the original survey design. Respondents of this survey work in a research performing or research funding organisation in the marine and maritime sector which is taking part in the GRRIP (Grounding RRI Practices in research performing organisations) project. The survey was conducted as part of auditing the policies and practices of the five marine & maritime organisations involved in GRRIP and to gather respondents' perspectives on these policies and practices. <strong>About GRRIP</strong><br> GRRIP is an EU-funded project (grrip.eu) that aims to embed sustainable RRI (responsible re-search and innovation) practices in the marine & maritime sector through institutional and cultural change. GRRIP will embed sustainable RRI practices in these five marine & maritime organisa-tions: The Centre for Marine and Renewable Energy (MaREI), University College Cork, Ireland Plataforma Oceánica de Canarias (Plocan), Gran Canaria WavEC Offshore Renewables, Lisbon, Portugal Institut Universitaire Mer Littoral U.Nantes (IUML), France Swansea University, Wales. -----------<br> <em>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No.</em> <em>820283</em><em>.</em>
DOI: 10.5281/zenodo.5708877
title: GRRIP WP5 - Researcher Survey

description: This dataset includes the full survey data of the GRRIP project's WP5 Researcher Survey, including documentation about using the dataset and the original survey design. Respondents of this survey work in a research performing or research funding organisation in the marine and maritime sector which is taking part in the GRRIP (Grounding RRI Practices in research performing organisations) project. The survey was conducted as part of auditing the policies and practices of the five marine & maritime organisations involved in GRRIP and to gather respondents' perspectives on these policies and practices. <strong>About GRRIP</strong><br> GRRIP is an EU-funded project (grrip.eu) that aims to embed sustainable RRI (responsible re-search and innovation) practices in the marine & maritime sector through institutional and cultural change. GRRIP will embed sustainable RRI practices in these five marine & maritime organisa-tions: The Centre for Marine and Renewable Energy (MaREI), University College Cork, Ireland Plataforma Oceánica de Canarias (Plocan), Gran Canaria WavEC Offshore Renewables, Lisbon, Portugal Institut Universitaire Mer Littoral U.Nantes (IUML), France Swansea University, Wales. -----------<br> <em>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No.</em> <em>820283</em><em>.</em>
DOI: 10.5281/zenodo.5649554
title: Data from the paper "A turquoise fluorescence lifetime-based biosensor for quantitative imaging of intracellular calcium"

description: Data that belongs to the paper "A turquoise fluorescence lifetime-based biosensor for quantitative imaging of intracellular calcium"
description: This work was supported by a NWO Chemical Sciences ECHO grant (711.017.003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.5649553
title: Data from the paper "A turquoise fluorescence lifetime-based biosensor for quantitative imaging of intracellular calcium"

description: Data that belongs to the paper "A turquoise fluorescence lifetime-based biosensor for quantitative imaging of intracellular calcium"
description: This work was supported by a NWO Chemical Sciences ECHO grant (711.017.003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.5708883
title: GRRIP WP5 - Stakeholder Survey

description: This dataset includes the full survey data of the GRRIP project's WP5 Stakeholder Survey, including documentation about using the dataset and the original survey design. Respondents of this survey are interested in a research organisation in the marine and maritime sector and stakeholder in the GRRIP (Grounding RRI Practices in research performing organisations) project. The survey was conducted as part of auditing the policies and practices of the five marine & maritime organisations involved in GRRIP and to gather respondents' perspectives on these policies and practices. <strong>About GRRIP</strong><br> GRRIP is an EU-funded project (grrip.eu) that aims to embed sustainable RRI (responsible re-search and innovation) practices in the marine & maritime sector through institutional and cultural change. GRRIP will embed sustainable RRI practices in these five marine & maritime organisa-tions: The Centre for Marine and Renewable Energy (MaREI), University College Cork, Ireland Plataforma Oceánica de Canarias (Plocan), Gran Canaria WavEC Offshore Renewables, Lisbon, Portugal Institut Universitaire Mer Littoral U.Nantes (IUML), France Swansea University, Wales. -----------<br> <em>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No.</em> <em>820283</em><em>.</em>
DOI: 10.5281/zenodo.5708882
title: GRRIP WP5 - Stakeholder Survey

description: This dataset includes the full survey data of the GRRIP project's WP5 Stakeholder Survey, including documentation about using the dataset and the original survey design. Respondents of this survey are interested in a research organisation in the marine and maritime sector and stakeholder in the GRRIP (Grounding RRI Practices in research performing organisations) project. The survey was conducted as part of auditing the policies and practices of the five marine & maritime organisations involved in GRRIP and to gather respondents' perspectives on these policies and practices. <strong>About GRRIP</strong><br> GRRIP is an EU-funded project (grrip.eu) that aims to embed sustainable RRI (responsible re-search and innovation) practices in the marine & maritime sector through institutional and cultural change. GRRIP will embed sustainable RRI practices in these five marine & maritime organisa-tions: The Centre for Marine and Renewable Energy (MaREI), University College Cork, Ireland Plataforma Oceánica de Canarias (Plocan), Gran Canaria WavEC Offshore Renewables, Lisbon, Portugal Institut Universitaire Mer Littoral U.Nantes (IUML), France Swansea University, Wales. -----------<br> <em>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No.</em> <em>820283</em><em>.</em>
DOI: 10.6073/pasta/fdbebf31933e31a7b55fa03e29f4ff2d
title: Bonanza Creek LTER: Hourly Net Radiation, Up and Down Shortwave/Longwave Radiation from 1992 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska

description: A collection of radiation data taken in Caribou-Poker Creeks Research Watershed. This set includes PAR, Net Radiation, and Shortwave/Longwave Radiation (both up and down). Winter measurements may be inaccurate due to snow cover.
DOI: 10.6073/pasta/3b4f9f63322fa09c645657f01db06abe
title: Bonanza Creek LTER: Hourly Dew Point Measurements from 2006 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska

description: Calculated dew point values using the air temperature and relative humidity data collected at the corresponding Caribou-Poker Creeks Research Watershed climate station.
DOI: 10.6073/pasta/672f4cac6963b66a34e909b009d7c866
title: Bonanza Creek LTER: Hourly Vapor Pressure (VP) Measurements from 2006 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska

description: Calculated vapor pressure values using the air temperature and relative humidity data collected at the corresponding Caribou-Poker Creeks Research Watershed climate station.
DOI: 10.25345/c5hg4h
title: MassIVE MSV000088404 - PPM1D mutations are oncogenic drivers of de novo Diffuse Midline Glioma formation
title: Khadka P, Reitman ZJ, Lu S, Buchan G, Gionet G, Dubois F, Carvalho DM, Shih J, Zhang S, Greenwald N, Zack T, Shapira O, Pelton K, Hartley R, Bear H, Georgis Y, Jarmale S, Melanson R, Bonano K, Schoolcraft K, Miller PG, Condurat AL, Gonzalez E, Qian K, Morin E, Langhnoja J, Lupien L, Rendo V, Digiacomo J, Wang D, Zhou K, Kumbhani R, Guerra-Garcia ME, Sinai CE, Becker S, Schneider R, Vogelzang J, Krug K, Babur O, Goodale A, Abid T, Kalani Z, Piccioni F, Beroukhim R, Perskey N, Root D, Carcaboso AM, Ebert BL, Fuller C, Kieran MW, Jones C, Keshishian H, Ligon KL, Carr SA, Phoenix TN, and Bandopadhayay P. 2021. The role of PPM1D mutations in de novo gliomagenesis has not been systematically explored. Here we analyze whole genomes sequences of 170 pediatric high-grade gliomas and find that truncating mutations in PPM1D that increase the stability of its phosphatase are clonal driver events in 11% of Diffuse Midline Gliomas (DMGs) and are enriched in primary pontine tumors. Through the development of novel DMG mouse models, we show that PPM1D mutations potentiate gliomagenesis and that PPM1D phosphatase activity is required for in vivo oncogenesis. Finally, we applied integrative phosphoproteomic and functional genomics assays and found that the oncogenic effects of PPM1D truncation converge on regulators of cell cycle, DNA damage response, and p53 pathways, revealing therapeutic vulnerabilities including MDM2 inhibition.
DOI: 10.6084/m9.figshare.15217512
title: UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra

description: Datasets for manuscript in Analytical Chemistry
DOI: 10.6084/m9.figshare.15217512.v2
title: UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra

description: Datasets for a manuscript
DOI: 10.6073/pasta/840db4f1116743d93ed7bd6798827ea1
title: Bonanza Creek LTER: Hourly Wind Speed and Direction at 3m and 10 m from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska

description: This dataset contains the hourly output from Wind sensors for the Bonanza Creek Experimental Forest (BCEF). This includes Level 3 weather stations as well as smaller scale and temporal studies. This data can be sorted and viewed by site, year, hour, height of measurement, mean, min, and max value. Updates of each site are different since some are still on going while other had only a 2-3 year life cycle. Winter measurements may be inaccurate due to snow cover.
DOI: 10.5517/ccdc.csd.cc287cxt
title: CCDC 2092466: Experimental Crystal Structure Determination

description: Related Article: Christian Schumacher, Claude Molitor, Sabrina Smid, Khai-Nghi Truong, Kari Rissanen, Carsten Bolm|2021|J.Org.Chem.|86|14213|doi:10.1021/acs.joc.1c01529
DOI: 10.5281/zenodo.5708912
title: Liver and Blood Expression Prediction Generated for Opportunities and challenges for transcriptome-wide association studies

description: db files are in sqlite format and contain weights to calculate genetic predictors of gene expression Blood, Liver indicate the tissue where the RNA was extracted from Crohns and LDL indicate that prediction models were generated using SNPs available in the GWAS summary results used in the paper (Wainberg et al https://doi.org/10.1038/s41588-019-0385-z) *txt.gz filex contain covariances between SNPs needed to run the summary version of PrediXcan or other TWAS methods Information about the RNAseq data can be found here https://pubmed.ncbi.nlm.nih.gov/27540175/ Sample size n=522
DOI: 10.5281/zenodo.5708911
title: Liver and Blood Expression Prediction Generated for Opportunities and challenges for transcriptome-wide association studies

description: db files are in sqlite format and contain weights to calculate genetic predictors of gene expression Blood, Liver indicate the tissue where the RNA was extracted from Crohns and LDL indicate that prediction models were generated using SNPs available in the GWAS summary results used in the paper (Wainberg et al https://doi.org/10.1038/s41588-019-0385-z) *txt.gz filex contain covariances between SNPs needed to run the summary version of PrediXcan or other TWAS methods Information about the RNAseq data can be found here https://pubmed.ncbi.nlm.nih.gov/27540175/ Sample size n=522
DOI: 10.15468/cs2nz5
title: Micoteca ARAN-fungi

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/92rq6w
title: Ornitho.eus

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/zvx5vd
title: Base de datos de plantas vasculares del País Vasco: ARAN-EH

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/yq9nnw
title: Fauna marina de la costa vasca

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/69qevx
title: Anillamiento SEO

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/nfhzgd
title: iNaturalist

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/5auqu5
title: Herbario ARAN

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/nkhp6s
title: Herbario VIT - Plantas Vasculares (Museo de Historia Natural de Álava)

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/dzhtgx
title: Herbario BIO

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.34943/cd40a136-f688-4266-836c-3d4a3373d5d0
title: ROPOS Echosounder, Hydrographic Multibeam Deployed 2012-05-28

description: The Imagenex 837A Delta-T multibeam sonar 4284 was deployed on 2012-05-28 on ROPOS. This Remotely Operated Vehicle is operated by Canadian Scientific Submersible Facility (CSSF). This device is a Echosounder, Hydrographic Multibeam. Multibeam bathymetric echosounders acoustically map the seafloor. Typically mounted below a ship or remote operated vehicle (ROV), they transmit a narrow fan of acoustic beams from a transducer. These beams are reflected by the seafloor and received by the instrument. The return signal angle and travel time are used to calculate the depth and position for points on the seafloor. Motion and heading sensors are used to compensate for the relative movement of the instrument. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.6073/pasta/ac61b734e8182dd55c2493c9805ba626
title: Bonanza Creek LTER: Hourly Photosynthetically Active Radiation (PAR) Measurements from 1988 to Present in the Bonanza Creek Experimental Forest and the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska

description: PAR sensors measure solar radiation in the 400 to 700 nm waveband in microEinsteins per sq. m. per second. The average hourly reading is converted to microEinsteins per sq. m. per hour and summed over 24 hours to obtain daily Einsteins per sq. m. Sensors are removed at the end of September for annual calibrations and replaced back at the sites by the first of May each year.
DOI: 10.34945/f5r59j
title: ASIA Impairment Scale, level of injury, intraoperative time series mean arterial pressure and heart rate after spinal cord injury in patients in a multi-site retrospective TRACK-SCI cohort: site 1 of 2

description: STUDY PURPOSE: The main goal for the collection of this data was to assess whether intraoperative hemodynamics (arterial pressure and heart rate) is associated to patient neurological improvement at hospital discharge in spinal cord injury (SCI). The data were collected in two level I trauma centers in Northern California (Santa Clara Valley Medical Center and Zuckerberg San Francisco General Hospital). This dataset represents the data from one of the centers, while the data collected in the other center is available elsewhere (see provenance). Both datasets have been harmonized, meaning variables and data dictionary align. DATA COLLECTED: The dataset includes mean arterial pressure (MAP) and heart rate (HR) at 5 minute intervals during SCI associated surgery for n = 87 patients, as well as summaries across surgery time (average, standard deviation, kurtosis and skewness). The data also include demographics (age and gender), length metrics (surgery duration and days from injury to discharge), and injury characteristics and outcome metrics (AIS grade at admission, AIS grade at hospital discharge, dichotomized neurological level of injury: cervical vs. non-cervical). DATA USAGE NOTES:
DOI: 10.6073/pasta/f03d7d682eae2f467642e4260686ea15
title: Global data set of long-term summertime vertical temperature profiles in 153 lakes

description: Climate change and other anthropogenic stressors have led to long-term changes in lake thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients. Though many studies highlight generally consistent warming of surface water temperatures in lakes around the world, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with some time series beginning as early as 1912. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data for understanding changes and drivers of lake thermal structure in a time of rapid global and ecological change.