cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14456312.v7
title: ibutton-left-nondominant-upper-arm-cotton-sports-wristband-ventral-exterior-5min

description: Skin temperature using a Maxim Thermocron iButton DS1925L set at a resolution of one sample every 5 min (max resolution) and temperature resolution of 0.0625°C (max resolution), placed on the ventral side of the forearm, by being attached with a velcro to the exterior of a cotton sports wrist band.<br>This dataset is meant to capture ambient temperature. It is placed just beside the main skin sensor but on the exterior of the cotton wristband instead of the interior, so that this sensor reflects the ambient temperature at the exact site where the main skin sensor is. This should be used in combination with the ibutton-left-nondominant-upper-arm-cotton-sports-wristband-5min dataset (main skin sensor, placed on the radial artery).<br>Start using the files from the datetime (in filename): 071621 and onward. Indeed, earlier logs are redundant. Concatenate every files beyond this date to get the full log, each file containing one distinct period of time.<br>Earlier logs contain the same data as 071621, as initially it was thought that the sensor had enough memory to collect 1 year of data, but this was in fact incorrect (it's about 6-7 months of data). So it's necessary to clear the sensor's data before collecting a new period.<br>Note that the datetime in the filename is in US format: monthdayyear.
DOI: 10.6084/m9.figshare.14456314
title: ibutton-left-nondominant-upper-arm-cotton-sports-wristband-dorsal-interior-5min

description: Skin temperature using a Maxim Thermocron iButton DS1925L set at a resolution of one sample every 5 min (max resolution) and temperature resolution of 0.0625°C (max resolution), placed on the dorsal side of the forearm, at the opposite of the radial artery, by being attached with a velcro to the interior of a cotton sports wrist band. <br> This dataset is meant to capture skin temperature at another site than the radial artery, so that the dataset may be useful for spurious temperature changes regression. This should be used in combination with the ibutton-left-nondominant-upper-arm-cotton-sports-wristband-radial-artery-ventral-interior-5min dataset. <br> Start using the files from the datetime (in filename): 071621 and onward. Indeed, earlier logs are redundant. Concatenate every files beyond this date to get the full log, each file containing one distinct period of time.<br> Earlier logs contain the same data as 071621, as initially it was thought that the sensor had enough memory to collect 1 year of data, but this was in fact incorrect (it's about 6-7 months of data). So it's necessary to clear the sensor's data before collecting a new period. <br> Note that the datetime in the filename is in US format: monthdayyear.<br>
DOI: 10.6084/m9.figshare.14456314.v8
title: ibutton-left-nondominant-upper-arm-cotton-sports-wristband-dorsal-interior-5min

description: Skin temperature using a Maxim Thermocron iButton DS1925L set at a resolution of one sample every 5 min (max resolution) and temperature resolution of 0.0625°C (max resolution), placed on the dorsal side of the forearm, at the opposite of the radial artery, by being attached with a velcro to the interior of a cotton sports wrist band. <br> This dataset is meant to capture skin temperature at another site than the radial artery, so that the dataset may be useful for spurious temperature changes regression. This should be used in combination with the ibutton-left-nondominant-upper-arm-cotton-sports-wristband-radial-artery-ventral-interior-5min dataset. <br> Start using the files from the datetime (in filename): 071621 and onward. Indeed, earlier logs are redundant. Concatenate every files beyond this date to get the full log, each file containing one distinct period of time.<br> Earlier logs contain the same data as 071621, as initially it was thought that the sensor had enough memory to collect 1 year of data, but this was in fact incorrect (it's about 6-7 months of data). So it's necessary to clear the sensor's data before collecting a new period. <br> Note that the datetime in the filename is in US format: monthdayyear.<br>
DOI: 10.6084/m9.figshare.14456314.v7
title: ibutton-left-nondominant-upper-arm-cotton-sports-wristband-dorsal-interior-5min

description: Skin temperature using a Maxim Thermocron iButton DS1925L set at a resolution of one sample every 5 min (max resolution) and temperature resolution of 0.0625°C (max resolution), placed on the dorsal side of the forearm, at the opposite of the radial artery, by being attached with a velcro to the interior of a cotton sports wrist band.<br>This dataset is meant to capture skin temperature at another site than the radial artery, so that the dataset may be useful for spurious temperature changes regression. This should be used in combination with the ibutton-left-nondominant-upper-arm-cotton-sports-wristband-5min dataset.<br>Start using the files from the datetime (in filename): 071621 and onward. Indeed, earlier logs are redundant. Concatenate every files beyond this date to get the full log, each file containing one distinct period of time.<br>Earlier logs contain the same data as 071621, as initially it was thought that the sensor had enough memory to collect 1 year of data, but this was in fact incorrect (it's about 6-7 months of data). So it's necessary to clear the sensor's data before collecting a new period.<br>Note that the datetime in the filename is in US format: monthdayyear.<br>
DOI: 10.6084/m9.figshare.15001182
title: greenteg-core-axillary-5min

description: Readings from the GreenTEG CORE sensor, which measures the core body temperature non-invasively continuously 24/7 from an axially/apically placed sensor maintained with a Polar Pro chest strap. See the dataset greenteg-core-axillary for more infos. <br> <br> The difference with the greenteg-core-axillary dataset is that this one is generated by the cloud (cloud.corebodytemp.com), which is accessible to all users even without a Research license. It is however limited to a sampling resolution of one sample every 5 min (0.0033... Hz), whereas with the Research license as in the greenteg-core-axillary dataset, the sampling rate is 1 Hz (one sample per second). However, most if not all past studies on core body temperature used sampling rates of 5min or more (usually 10min), so this sampling rate should be sufficient for most experimental purposes. <br> This dataset is also likely more complete than the greenteg-core-axillary dataset, as the memory on the CORE device is of 3.5 days for a 1 Hz resolution, and 6 days for a one sample per 5 min resolution (by connecting to the cloud at least once every 48h), but is more coarsely grained as a trade-off. Both datasets are acquired simultaneously, the CORE can do both with a Research license using the same sensor. <br> This is the only sensor that was brought for travel during the period of 2021-08-21 to 2021-09-13.
DOI: 10.6084/m9.figshare.15001182.v4
title: greenteg-core-axillary-5min

description: <br>Readings from the GreenTEG CORE sensor, which measures the core body temperature non-invasively continuously 24/7. See the dataset greenteg-core-axillary for more infos.<br>The difference with the greenteg-core-axillary dataset is that this one is generated by the cloud (cloud.corebodytemp.com), which is accessible to all users even without a Research license. It is however limited to a sampling resolution of one sample every 5 min (0.0033... Hz), whereas with the Research license as in the greenteg-core-axillary dataset, the sampling rate is 1 Hz (one sample per second). However, most if not all past studies on core body temperature used sampling rates of 5min or more (usually 10min), so this sampling rate should be sufficient for most experimental purposes.<br>This dataset is also likely more complete than the greenteg-core-axillary dataset, as the memory on the CORE device is of 3.5 days for a 1 Hz resolution, and 6 days for a one sample per 5 min resolution (by connecting to the cloud at least once every 48h), but is more coarsely grained as a trade-off. Both datasets are acquired simultaneously, the CORE can do both with a Research license using the same sensor.<br>This is the only sensor that was brought for travel during the period of 2021-08-21 to 2021-09-13.
DOI: 10.17909/pvn9-ya02
title: Data from Harris 2022 paper

description: HST imaging from programs 10710, 10429, 12238 of globular clusters around giant early-type galaxies
DOI: 10.7284/123597
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise TN343 using a Hawaii UHDAS instrument system onboard the platform RV Thomas G. Thompson

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/123600
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise TN343 using a SeaBird SBE-911+ instrument system onboard the platform RV Thomas G. Thompson

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/127458
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise TN347 using a SeaBird SBE-911+ instrument system onboard the platform RV Thomas G. Thompson

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.20425467
title: BoolODE Simulated Time Series with single gene KO

description: BoolODE was designed to simulate single-cell expression data sets on the basis of a network of gene-gene interactions (Pratapa et al., 2020). To simulate data sets with gene KOs, the simulations were allowed to run for 101 time points, at which point a gene’s expression value was set to zero and was again set to zero on each future iteration, mimicking a KO gene.
DOI: 10.6084/m9.figshare.20425467.v1
title: BoolODE Simulated Time Series with single gene KO

description: BoolODE was designed to simulate single-cell expression data sets on the basis of a network of gene-gene interactions (Pratapa et al., 2020). To simulate data sets with gene KOs, the simulations were allowed to run for 101 time points, at which point a gene’s expression value was set to zero and was again set to zero on each future iteration, mimicking a KO gene.
DOI: 10.18112/openneuro.ds003029.v1.0.4
title: Epilepsy-iEEG-Multicenter-Dataset
DOI: 10.7284/127468
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise TN348 using a SeaBird SBE-911+ instrument system onboard the platform RV Thomas G. Thompson

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5517/ccdc.csd.cc28svtr
title: CCDC 2109265: Experimental Crystal Structure Determination

description: Related Article: Pei-Pei Zhang, Huajun Andrew Zhou, Ai-Yun Ni, He Zhao, Jian-Jun Zhang, Hong-Hong Wei, Shuqin Liu, Jun Ni, Chunying Duan|2022|Cryst.Growth Des.|22|4845|doi:10.1021/acs.cgd.2c00338
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.cc28svvs
title: CCDC 2109266: Experimental Crystal Structure Determination

description: Related Article: Pei-Pei Zhang, Huajun Andrew Zhou, Ai-Yun Ni, He Zhao, Jian-Jun Zhang, Hong-Hong Wei, Shuqin Liu, Jun Ni, Chunying Duan|2022|Cryst.Growth Des.|22|4845|doi:10.1021/acs.cgd.2c00338
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.cc28svwt
title: CCDC 2109267: Experimental Crystal Structure Determination

description: Related Article: Pei-Pei Zhang, Huajun Andrew Zhou, Ai-Yun Ni, He Zhao, Jian-Jun Zhang, Hong-Hong Wei, Shuqin Liu, Jun Ni, Chunying Duan|2022|Cryst.Growth Des.|22|4845|doi:10.1021/acs.cgd.2c00338
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.cc28svxv
title: CCDC 2109268: Experimental Crystal Structure Determination

description: Related Article: Pei-Pei Zhang, Huajun Andrew Zhou, Ai-Yun Ni, He Zhao, Jian-Jun Zhang, Hong-Hong Wei, Shuqin Liu, Jun Ni, Chunying Duan|2022|Cryst.Growth Des.|22|4845|doi:10.1021/acs.cgd.2c00338
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: 10.34740/kaggle/dsv/4029749
title: Elon Musk Tweets
DOI: 10.17909/64m6-1c38
title: Data from Harris 2022 paper

description: HST imaging from program 11711 for globular clusters around NGC 4889
DOI: 10.6084/m9.figshare.20419179
title: R-Script_GESE29044_STAGE 3

description: R-script
DOI: 10.6084/m9.figshare.20419206
title: Raw data derived from differential expression analysis performed in GEO2R for each IDC stage in the datasets.

description: Raw Data
DOI: 10.6084/m9.figshare.20419176
title: R-Script_GESE29044_STAGE 2

description: R-script
DOI: 10.6084/m9.figshare.20419119
title: R-Script_GESE32291_STAGE 1

description: R-Script
DOI: 10.6084/m9.figshare.20419164
title: R-Script_GESE32291_STAGE 2

description: R-script
DOI: 10.6084/m9.figshare.20419167
title: R-Script_GESE32291_STAGE 3

description: R-script
DOI: 10.6084/m9.figshare.20419170
title: R-Script_GESE29044_STAGE 1

description: R-script
DOI: 10.5281/zenodo.6113017
title: Rapid increase in transferrin receptor recycling promotes adhesion during T cell activation

description: This Dataset contains primary data used for the publication "Rapid increase in transferrin receptor recycling promotes adhesion during T cell activation" accepted for publication in BMC Biology on 15. July 2022 <strong>Abstract:</strong> Background<br> T cell activation leads to increased expression of the receptor for the iron transporter transferrin (TfR) to provide iron required for the cell differentiation and clonal expansion that takes place during the days after encounter with a cognate antigen. However, T cells mobilise TfR to their surface within minutes after activation, although the reason and mechanism driving this process remain unclear. Results<br> Here we show that T cells transiently increase endocytic uptake and recycling of TfR upon activation, thereby boosting their capacity to import iron. We demonstrate that increased TfR recycling is powered by a fast endocytic sorting pathway relying on the membrane proteins flotillins, Rab5 and Rab11a-positive endosomes. Our data further reveal that iron import is required for a non-canonical signalling pathway involving the kinases Zap70 and PAK, which controls adhesion of the integrin LFA-1 and eventually leads to conjugation with antigen-presenting cells. Conclusions<br> Altogether, our data suggest that T cells boost their iron importing capacity immediately upon activation to promote adhesion to antigen-presenting cells. Data are organised in compressed (.zip) folders entitled as the corresponding Figures in the publication. Programs we recommend to view the files are:<br> .fcs files: FlowJo software v10 (Tree Star, Ashland, OR, USA)<br> .lsm and .czi files: ZEN 2012 SP1 and higher (Carl Zeiss, Jena, Germany)<br> .lif files: LAS X v3 (Leica Microsystems, Wetzlar, Germany)<br> .pzfx files: Prism v7 software (GraphPad, San Diego, CA, USA)
DOI: 10.5281/zenodo.6113016
title: Rapid increase in transferrin receptor recycling promotes adhesion during T cell activation

description: This Dataset contains primary data used for the publication "Rapid increase in transferrin receptor recycling promotes adhesion during T cell activation" accepted for publication in BMC Biology on 15. July 2022 <strong>Abstract:</strong> Background<br> T cell activation leads to increased expression of the receptor for the iron transporter transferrin (TfR) to provide iron required for the cell differentiation and clonal expansion that takes place during the days after encounter with a cognate antigen. However, T cells mobilise TfR to their surface within minutes after activation, although the reason and mechanism driving this process remain unclear. Results<br> Here we show that T cells transiently increase endocytic uptake and recycling of TfR upon activation, thereby boosting their capacity to import iron. We demonstrate that increased TfR recycling is powered by a fast endocytic sorting pathway relying on the membrane proteins flotillins, Rab5 and Rab11a-positive endosomes. Our data further reveal that iron import is required for a non-canonical signalling pathway involving the kinases Zap70 and PAK, which controls adhesion of the integrin LFA-1 and eventually leads to conjugation with antigen-presenting cells. Conclusions<br> Altogether, our data suggest that T cells boost their iron importing capacity immediately upon activation to promote adhesion to antigen-presenting cells. Data are organised in compressed (.zip) folders entitled as the corresponding Figures in the publication. Programs we recommend to view the files are:<br> .fcs files: FlowJo software v10 (Tree Star, Ashland, OR, USA)<br> .lsm and .czi files: ZEN 2012 SP1 and higher (Carl Zeiss, Jena, Germany)<br> .lif files: LAS X v3 (Leica Microsystems, Wetzlar, Germany)<br> .pzfx files: Prism v7 software (GraphPad, San Diego, CA, USA)
DOI: 10.17909/bk3t-td27
title: Data from Harris 2022 paper

description: HST imaging from programs 10861, 11711 of NGC 4874, for globular cluster photometry
DOI: 10.17909/3x7e-fj90
title: Data from Harris 2022 paper

description: HST imaging from program 10558 for NGC 1132 and ESO306-G017, for globular cluster photometry
DOI: 10.17909/qssg-zb94
title: Data from Harris 2022 paper

description: HST imaging of IC 4051, from program 12918 for globular cluster photometry
DOI: 10.17909/qafy-dv12
title: Data from Harris 2022 paper

description: HST imaging of NGC 1275 and 1278 from program 15235, for globular cluster photometry
DOI: 10.5281/zenodo.6027311
title: Townsend_et_al_2022

description: Mathematica files for Townsend et al. 2022 "Durability of vaccine-induced immunity against breakthrough infection by SARS-CoV-2"
DOI: 10.5281/zenodo.5534924
title: Townsend_et_al_2022

description: Mathematica files for Townsend et al. 2022 "Durability of vaccine-induced immunity against breakthrough infection by SARS-CoV-2"
DOI: 10.5281/zenodo.6964733
title: Dataset Graven in De Gids - Literatuur en Wetenschap

description: Dataset behorende bij 'Van Wissen, L. (2019). 'Men wilde modern zijn, geen atheïst': Een topic-modellinganalyse van natuurwetenschappen en theologie in honderd jaar <em>De Gids</em>. In A-F. van der Meer, W. Schrover, N. Moser, & M. Onrust (Eds.), <em>Naar het onbekende: Perspectieven op literatuur, cultuur en kennis</em> (pp. 237-249). Uitgeverij Verloren.' <strong>Instructies</strong><br> Zie: <em>degids_literatuur_en_wetenschap.ipynb</em> <strong>Corpus</strong><br> Het corpus kan door zijn licentie niet meegeleverd worden in deze dataset. Wel is het model van 300 topics beschikbaar, samen met de index van woorden. Dit is een (voor het programma voldoende) benadering/abstractie van de representatie van de inhoud van de echte artikelen. Het gehele corpus van deze periode is overigens vrij in te zien in de DBNL.
description: {"references": ["Van Wissen, L. (2019). 'Men wilde modern zijn, geen athe\u00efst': Een topic-modellinganalyse van natuurwetenschappen en theologie in honderd jaar De Gids. In A-F. van der Meer, W. Schrover, N. Moser, & M. Onrust (Eds.), Naar het onbekende: Perspectieven op literatuur, cultuur en kennis (pp. 237-249). Uitgeverij Verloren."]}
DOI: 10.5281/zenodo.6964732
title: Dataset Graven in De Gids - Literatuur en Wetenschap

description: Dataset behorende bij 'Van Wissen, L. (2019). 'Men wilde modern zijn, geen atheïst': Een topic-modellinganalyse van natuurwetenschappen en theologie in honderd jaar <em>De Gids</em>. In A-F. van der Meer, W. Schrover, N. Moser, & M. Onrust (Eds.), <em>Naar het onbekende: Perspectieven op literatuur, cultuur en kennis</em> (pp. 237-249). Uitgeverij Verloren.' <strong>Instructies</strong><br> Zie: <em>degids_literatuur_en_wetenschap.ipynb</em> <strong>Corpus</strong><br> Het corpus kan door zijn licentie niet meegeleverd worden in deze dataset. Wel is het model van 300 topics beschikbaar, samen met de index van woorden. Dit is een (voor het programma voldoende) benadering/abstractie van de representatie van de inhoud van de echte artikelen. Het gehele corpus van deze periode is overigens vrij in te zien in de DBNL.
description: {"references": ["Van Wissen, L. (2019). 'Men wilde modern zijn, geen athe\u00efst': Een topic-modellinganalyse van natuurwetenschappen en theologie in honderd jaar De Gids. In A-F. van der Meer, W. Schrover, N. Moser, & M. Onrust (Eds.), Naar het onbekende: Perspectieven op literatuur, cultuur en kennis (pp. 237-249). Uitgeverij Verloren."]}
DOI: 10.17909/fk8b-jv82
title: Data from Harris 2022 paper

description: HST imaging of NGC 1129 from program 13698, for globular cluster photometry
DOI: 10.5281/zenodo.6964776
title: Edited DHS data for R training

description: Edited DHS data for R training
DOI: 10.5281/zenodo.5053059
title: Edited DHS data for R training

description: Edited DHS data for R training
DOI: 10.5281/zenodo.6893129
title: Initial Data set

description: Initial Data set
DOI: 10.5281/zenodo.6964831
title: GPS fixes for foraging Southern Giant Petrels (Macronectes giganteus) breeding in Nelson Island, Maritime Antarctic Peninsula (December 2019-January 2020)

description: In the ZIP file there are two files. One is a .csv with GPS fixes processed using the adehabitatLT to identify events of low speed and high turn movements categorized as foraging. Points within a 250m radius from the breeding colony were excluded, and foraging trips were estimated based on the moment the animals crossed such a radius. Each foraging point was classified as being on land, within 5kms of coastline or on pelagic waters, and on different levels of Sea Ice Cover (from Bremen Sea Ice Remote Sensing data browser https://seaice.uni-bremen.de/databrowser/, Spreen, G., L. Kaleschke, and G.Heygster (2008), Sea ice remote sensing using AMSR-E 89 GHz channels J. Geophys. Res.,vol. 113, C02S03, doi:10.1029/2005JC003384). Second file is a R Script with details of the methods for processing the data. Two types of devices were used: Kite-M (Ecotone, NAV individuals) and Axy Trek Marine (Techno SmArt, HP individuals) configurated to collect points each 5 and 10 minutes, respectively. Funded by Fondecyt Iniciación (111 801 75) and Programa Áreas Marinas Protegidas del Instituto Antártico Chileno (24 03 052).