cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6939375
title: BY-COVID Work Package 2 List of Resources

description: Work Package 2: <em>Accessing heterogeneous data across domains and jurisdictions for enabling the downstream processing of COVID-19 and future pandemic episodes data </em>has gathered a relevant list of resources for the following areas: Non-patient related, Human-patient biomolecular, Human-patient clinical and health and socio-economics.
DOI: 10.5281/zenodo.6939376
title: BY-COVID Work Package 2 List of Resources

description: Work Package 2: <em>Accessing heterogeneous data across domains and jurisdictions for enabling the downstream processing of COVID-19 and future pandemic episodes data </em>has gathered a relevant list of resources for the following areas: Non-patient related, Human-patient biomolecular, Human-patient clinical and health and socio-economics.
DOI: 10.25345/c5zw1930m
title: MassIVE MSV000091915 - Post-transcriptional characterization of the vertebrate aging brain sheds light on the origin of protein-transcript decoupling
title: PTM landscape characterization during aging in the short-lived killifish N. furzeri.
DOI: 10.5281/zenodo.7440956
title: Supplementary material - Non-invasive Multimodal Imaging Reveals Early Therapy-Induced Senescence in Human Cancer Cells

description: The repository features .xls and .txt worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: Figure 2. Raw pixel-wise signals detected in NLO images of TIS cells control cells that were used to perform the colocalization graphs and analyses reported. We describe the average colocalization of SRS and F-CARS signals, and TPEF and E-CARS signals, in both phenotypes. Figure 4. Raw data from image analyses of TPEF and SRS channels of multimodal NLO images, divided in 5 different time points over the therapy follow-up period. The data describe the early rearrangement of mitochondria (TPEF) and lipid vesicles (SRS) in TIS cells, with respect to control counterparts. Figure 6. Raw data from image analyses of QPI images, divided in 4 different time points over the therapy follow-up period. The data describe the early morphological modifications of TIS cells, with respect to control counterparts.
DOI: 10.5281/zenodo.7440957
title: Supplementary material - Non-invasive Multimodal Imaging Reveals Early Therapy-Induced Senescence in Human Cancer Cells

description: The repository features .xls and .txt worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: Figure 2. Raw pixel-wise signals detected in NLO images of TIS cells control cells that were used to perform the colocalization graphs and analyses reported. We describe the average colocalization of SRS and F-CARS signals, and TPEF and E-CARS signals, in both phenotypes. Figure 4. Raw data from image analyses of TPEF and SRS channels of multimodal NLO images, divided in 5 different time points over the therapy follow-up period. The data describe the early rearrangement of mitochondria (TPEF) and lipid vesicles (SRS) in TIS cells, with respect to control counterparts. Figure 6. Raw data from image analyses of QPI images, divided in 4 different time points over the therapy follow-up period. The data describe the early morphological modifications of TIS cells, with respect to control counterparts.
DOI: 10.5281/zenodo.7848221
title: Supplementary material - Non-invasive Multimodal Imaging Reveals Early Therapy-Induced Senescence in Human Cancer Cells

description: The repository features .xls and .txt worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: Figure 2. Raw pixel-wise signals detected in NLO images of TIS cells control cells that were used to perform the colocalization graphs and analyses reported. We describe the average colocalization of SRS and F-CARS signals, and TPEF and E-CARS signals, in both phenotypes. Figure 4. Raw data from image analyses of TPEF and SRS channels of multimodal NLO images, divided in 5 different time points over the therapy follow-up period. The data describe the early rearrangement of mitochondria (TPEF) and lipid vesicles (SRS) in TIS cells, with respect to control counterparts. Figure 6. Raw data from image analyses of QPI images, divided in 4 different time points over the therapy follow-up period. The data describe the early morphological modifications of TIS cells, with respect to control counterparts.
DOI: 10.5281/zenodo.7848251
title: Supplementary material - Non-invasive Multimodal Imaging Reveals Early Therapy-Induced Senescence in Human Cancer Cells

description: The repository features .xls and .txt worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: Figure 2. Raw pixel-wise signals detected in NLO images of TIS cells control cells that were used to perform the colocalization graphs and analyses reported. We describe the average colocalization of SRS and F-CARS signals, and TPEF and E-CARS signals, in both phenotypes. Figure 4. Raw data from image analyses of TPEF and SRS channels of multimodal NLO images, divided in 5 different time points over the therapy follow-up period. The data describe the early rearrangement of mitochondria (TPEF) and lipid vesicles (SRS) in TIS cells, with respect to control counterparts. Figure 6. Raw data from image analyses of QPI images, divided in 4 different time points over the therapy follow-up period. The data describe the early morphological modifications of TIS cells, with respect to control counterparts.
DOI: 10.5281/zenodo.7899237
title: Unlabeled Sentinel 2 time series dataset (training, T31TGJ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

description: This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles). In this repo,<strong> only data from the S2 tile T31TGJ</strong> are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924 <strong>Global unlabeled dataset description</strong> Dataset name S2 tiles ROI size Temporal extent Train T30TXT,T30TYQ,T30TYS,T30UVU, T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP 1024*1024 2018-2020 Val T30TYR,T30UWU,T31TEK,T31UER 256*256 2016-2019
DOI: 10.5281/zenodo.7899236
title: Unlabeled Sentinel 2 time series dataset (training, T31TGJ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

description: This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles). In this repo,<strong> only data from the S2 tile T31TGJ</strong> are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924 <strong>Global unlabeled dataset description</strong> Dataset name S2 tiles ROI size Temporal extent Train T30TXT,T30TYQ,T30TYS,T30UVU, T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP 1024*1024 2018-2020 Val T30TYR,T30UWU,T31TEK,T31UER 256*256 2016-2019
DOI: 10.5281/zenodo.7896621
title: Unlabeled Sentinel 2 time series dataset (training, T31TFN): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

description: This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles). In this repo,<strong> only data from the S2 tile T31TFN</strong> are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924 <strong>Global unlabeled dataset description</strong> Dataset name S2 tiles ROI size Temporal extent Train T30TXT,T30TYQ,T30TYS,T30UVU, T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP 1024*1024 2018-2020 Val T30TYR,T30UWU,T31TEK,T31UER 256*256 2016-2019
DOI: 10.5281/zenodo.7896620
title: Unlabeled Sentinel 2 time series dataset (training, T31TFN): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

description: This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles). In this repo,<strong> only data from the S2 tile T31TFN</strong> are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924 <strong>Global unlabeled dataset description</strong> Dataset name S2 tiles ROI size Temporal extent Train T30TXT,T30TYQ,T30TYS,T30UVU, T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP 1024*1024 2018-2020 Val T30TYR,T30UWU,T31TEK,T31UER 256*256 2016-2019
DOI: 10.5281/zenodo.7895498
title: Unlabeled Sentinel 2 time series dataset (training, T31TDJ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

description: This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles). In this repo,<strong> only data from the S2 tile T31TDJ</strong> are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924 <strong>Global unlabeled dataset description</strong> Dataset name S2 tiles ROI size Temporal extent Train T30TXT,T30TYQ,T30TYS,T30UVU, T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP 1024*1024 2018-2020 Val T30TYR,T30UWU,T31TEK,T31UER 256*256 2016-2019
DOI: 10.5281/zenodo.7895497
title: Unlabeled Sentinel 2 time series dataset (training, T31TDJ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

description: This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles). In this repo,<strong> only data from the S2 tile T31TDJ</strong> are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924 <strong>Global unlabeled dataset description</strong> Dataset name S2 tiles ROI size Temporal extent Train T30TXT,T30TYQ,T30TYS,T30UVU, T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP 1024*1024 2018-2020 Val T30TYR,T30UWU,T31TEK,T31UER 256*256 2016-2019
DOI: 10.5281/zenodo.7924181
title: Raw data obtained during study regarding road salt impact on zooplankton communities, Poland

description: Raw data obtained during the environmental study conducted in years 2019-2021 on four urban ponds located in Lodz, Poland.
DOI: 10.5281/zenodo.7924180
title: Raw data obtained during study regarding road salt impact on zooplankton communities, Poland

description: Raw data obtained during the environmental study conducted in years 2019-2021 on four urban ponds located in Lodz, Poland.
DOI: 10.5519/qd.aiwmy4h7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (686853 records)
DOI: 10.5281/zenodo.7900727
title: Dataset for "Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies"

description: This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 40B2-0_187223) in association with the recent publication entitled “Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies”. This work aims to study the humidity response of eco-friendly materials in the S-band (2-4GHz) using a microstrip line resonating at 3.3GHz. Several sensors coated with different biodegradable materials were tested and simulations have been performed. The S12 signal of the resonator was measured when varying the humidity. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.
DOI: 10.5281/zenodo.7907585
title: Dataset for "Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies"

description: This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 40B2-0_187223) in association with the recent publication entitled “Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies”. This work aims to study the humidity response of eco-friendly materials in the S-band (2-4GHz) using a microstrip line resonating at 3.3GHz. Several sensors coated with different biodegradable materials were tested and simulations have been performed. The S12 signal of the resonator was measured when varying the humidity. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.
DOI: 10.5281/zenodo.7900726
title: Dataset for "Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies"

description: This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 40B2-0_187223) in association with the recent publication entitled “Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies”. This work aims to study the humidity response of eco-friendly materials in the S-band (2-4GHz) using a microstrip line resonating at 3.3GHz. Several sensors coated with different biodegradable materials were tested and simulations have been performed. The S12 signal of the resonator was measured when varying the humidity. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.
DOI: 10.5517/cc7nmk1
title: CCDC 228363: Experimental Crystal Structure Determination

description: Related Article: V.P.Fedin, I.V.Kalinina, A.V.Virovets, D.Fenske|2003|Izv.Akad.Nauk SSSR,Ser.Khim.||119|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22800980
title: Repository_Darnajoux et al_2023_CSBJ

description: This document is a repository of all the code and dataset used for the article “A Diazotrophy-Ammoniotrophy Dual Growth Model for the sulfate-reducing bacteria Desulfovibrio vulgaris var. Hildenborough” by Darnajoux R., Inomura, K., & Zhang. X., accepted for publication in the journal Computational and Structural Biochemistry Journal in May 2023. This document regroups all the code for the function required to use the Diazotrophic-Ammoniotrophic Dual Cellular Model applied to the model Sulfate Reducing Bacteria Desulfovibrio vulgaris var. Hildenborough grown under Pyruvate and Lactate as a electron source and the validation data set from batch culture growth experiment. The final figures from this document were edited from these raw figures using Adobe Illustrator before inclusion in the article. Communication can be addressed to Romain Darnajoux (romaind(at)princeton(dot)edu or romain(dot)darnajoux(@)hotmail(dot)fr)
DOI: 10.6084/m9.figshare.22800980.v1
title: Repository_Darnajoux et al_2023_CSBJ

description: This document is a repository of all the code and dataset used for the article “A Diazotrophy-Ammoniotrophy Dual Growth Model for the sulfate-reducing bacteria Desulfovibrio vulgaris var. Hildenborough” by Darnajoux R., Inomura, K., & Zhang. X., accepted for publication in the journal Computational and Structural Biochemistry Journal in May 2023. This document regroups all the code for the function required to use the Diazotrophic-Ammoniotrophic Dual Cellular Model applied to the model Sulfate Reducing Bacteria Desulfovibrio vulgaris var. Hildenborough grown under Pyruvate and Lactate as a electron source and the validation data set from batch culture growth experiment. The final figures from this document were edited from these raw figures using Adobe Illustrator before inclusion in the article. Communication can be addressed to Romain Darnajoux (romaind(at)princeton(dot)edu or romain(dot)darnajoux(@)hotmail(dot)fr)
DOI: 10.5281/zenodo.7923977
title: One-particle and excitonic band structure in cubic Boron Arsenide

description: <strong>Data for "One-particle and excitonic band structure in cubic Boron Arsenide"</strong> The two directories are for calculations with (soc) and without (nosoc) SOC. Launching the supplied scripts in either will generate the BSE optics data (with opt.sh) and projected bands/DOS (with spd.sh) respectively. File description ([^1-2]): init.bas: initial structure definition ctrl.bas: main input file site.bas: structure input basp.bas: basis definition rst.bas: density/restart file sigm.bas: self-energy switches-for-lm: command line switches used across the scripts -- [^1]: https://www.questaal.org/docs/input/inputfile/ [^2]: https://www.questaal.org/docs/input/data_format/
DOI: 10.5281/zenodo.7923976
title: One-particle and excitonic band structure in cubic Boron Arsenide

description: <strong>Data for "One-particle and excitonic band structure in cubic Boron Arsenide"</strong> The two directories are for calculations with (soc) and without (nosoc) SOC. Launching the supplied scripts in either will generate the BSE optics data (with opt.sh) and projected bands/DOS (with spd.sh) respectively. File description ([^1-2]): init.bas: initial structure definition ctrl.bas: main input file site.bas: structure input basp.bas: basis definition rst.bas: density/restart file sigm.bas: self-energy switches-for-lm: command line switches used across the scripts -- [^1]: https://www.questaal.org/docs/input/inputfile/ [^2]: https://www.questaal.org/docs/input/data_format/
DOI: 10.6084/m9.figshare.21802374
title: Acceptability of Fully Guided Virtual Implant Planning Software Among Dental Undergraduate Students

description: Acceptability of Fully Guided Virtual Implant Planning Software Among Dental Undergraduate Students
DOI: 10.6084/m9.figshare.21802374.v1
title: Acceptability of Fully Guided Virtual Implant Planning Software Among Dental Undergraduate Students

description: Acceptability of Fully Guided Virtual Implant Planning Software Among Dental Undergraduate Students
DOI: 10.34740/kaggle/dsv/5662015
title: TMDB 10000 MOVIES DATASET
DOI: 10.6084/m9.figshare.21963280
title: Biomarkers of 4-hydroxy-<i>N,N</i>-methylpropyltryptamine (4-OH-MPT) intake identified from human hepatocyte incubations

description: 4-Hydroxy-<i>N,N</i>-methylpropyltryptamine (4-OH-MPT) is a psychedelic tryptamine whose use is regulated in several countries. Due to unspecific effects, consumption can be ascertained only through toxicological analyses. However, the trace amounts of tryptamines are usually challenging to detect in biological samples. 4-OH-MPT metabolism was characterized to identify optimal metabolite markers of intake in clinical/forensic toxicology. 4-OH-MPT was incubated with 10-donor-pooled human hepatocytes to simulate <i>in vivo</i> conditions; samples were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS), and data were processed with Compound Discoverer from Thermo Scientific. LC-HRMS/MS and data mining were supported by <i>in silico</i> metabolite predictions (GLORYx). Three phase I and four phase II metabolites were identified, including <i>N</i>-oxidation and <i>N</i>-demethylation at the alkylamine chain, and <i>O</i>-glucuronidation and sulfation at the hydroxylindole core. 4-OH-MPT metabolic fate was consistent with the human metabolism of tryptamine analogues: we suggest 4-OH-MPT-<i>N</i>-oxide and 4-hydroxy-<i>N,N</i>-propyltryptamine (4-OH-PT) as metabolite biomarkers of 4-OH-MPT consumption after glucuronide/sulfate hydrolysis in biological samples to improve detection of 4-OH-MPT and phase I metabolites; 4-OH-MPT-glucuronide is suggested as an additional biomarker when hydrolysis is not performed. Further research on the metabolism of structural analogues is necessary to evaluate the specificity of 4-OH-MPT metabolite biomarkers.
DOI: 10.5281/zenodo.7924187
title: Founding, Running, and Improving Scholarly Journals_Questionnaire Raw Data

description: This is the raw data set of a questionnaire on founding, running, and improving scholarly journals and the availability of educational material for these purposes. The questionnaire was prepared on the SoSci Survey platform and the raw data downloaded from it. The questionnaire had 93 respondents who were nearly all Editors or Scholarly Publishing Professionals working with academic journals. The questionnaire was prepared for the purposes of presenting a poster at the Society for Scholarly Publishing's (SSP) Annual Conference in Portland, Oregon, USA, in May 2023. The Findings of the project are available here: https://zenodo.org/record/7924220#.ZFy_SaXP3cs A Resource Compendium is available here: https://zenodo.org/record/7924268#.ZFy_DaXP3cs
DOI: 10.5281/zenodo.7924186
title: Founding, Running, and Improving Scholarly Journals_Questionnaire Raw Data

description: This is the raw data set of a questionnaire on founding, running, and improving scholarly journals and the availability of educational material for these purposes. The questionnaire was prepared on the SoSci Survey platform and the raw data downloaded from it. The questionnaire had 93 respondents who were nearly all Editors or Scholarly Publishing Professionals working with academic journals. The questionnaire was prepared for the purposes of presenting a poster at the Society for Scholarly Publishing's (SSP) Annual Conference in Portland, Oregon, USA, in May 2023. The Findings of the project are available here: https://zenodo.org/record/7924220#.ZFy_SaXP3cs A Resource Compendium is available here: https://zenodo.org/record/7924268#.ZFy_DaXP3cs