cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9992204.v1
title: sequencing data.xls

description: sequence data
DOI: 10.6084/m9.figshare.9992216.v1
title: Table1_SM.xlsx

description: Table 1 of Supplementary Material
DOI: 10.6084/m9.figshare.9992177
title: OTU 2019-04-30 phylum.xls

description: sequence data
DOI: 10.6084/m9.figshare.9992213.v1
title: Table2_SM.xlsx

description: Table 2 of Supplementary Material
DOI: 10.6084/m9.figshare.9992201.v1
title: data_Burraco_et_al.xlsx

description: Dataset of Burraco, Valdes & Orizaola in Journal of Animal Ecology.
DOI: 10.6084/m9.figshare.9992216.v2
title: Table1_SM.xlsx

description: Table 1 of Supplementary Material
DOI: 10.6084/m9.figshare.9992177.v1
title: OTU 2019-04-30 phylum.xls

description: sequence data
DOI: 10.5517/ccdc.csd.cc23r5ln
title: CCDC 1958691: Experimental Crystal Structure Determination

description: Related Article: Khetpakorn Chakarawet, T. David Harris, Jeffrey R. Long|2020|Chemical Science|11|8196|doi:10.1039/D0SC03078C
DOI: 10.5517/ccdc.csd.cc23r5mp
title: CCDC 1958692: Experimental Crystal Structure Determination

description: Related Article: Khetpakorn Chakarawet, T. David Harris, Jeffrey R. Long|2020|Chemical Science|11|8196|doi:10.1039/D0SC03078C
DOI: 10.5517/ccdc.csd.cc23r5nq
title: CCDC 1958693: Experimental Crystal Structure Determination

description: Related Article: Khetpakorn Chakarawet, T. David Harris, Jeffrey R. Long|2020|Chemical Science|11|8196|doi:10.1039/D0SC03078C
DOI: 10.6084/m9.figshare.9992054
title: Sleep-wake imaging ROI timeseries

description: Timeseries of sleep and wake segments from fast fMRI data (cortical gray matter ROI and CSF ROI) and EEG slow-delta amplitude envelope. All segments have been interpolated to same temporal resolution (200 ms). Segment index variable indicates sleep (1) or wake (2) segments. <br>variables are:alldata: segments x time x datatype datatypes are: 1) EEG 0.2-4 Hz amplitude envelope; 2) gray matter BOLD; 3) CSF signal<br>allstate: 1=sleep, 2=wake<br>Data are from Fultz et al., Science, 2019; full methods details are presented there.
DOI: 10.6084/m9.figshare.9992054.v1
title: Sleep-wake imaging ROI timeseries

description: Timeseries of sleep and wake segments from fast fMRI data (cortical gray matter ROI and CSF ROI) and EEG slow-delta amplitude envelope. All segments have been interpolated to same temporal resolution (200 ms). Segment index variable indicates sleep (1) or wake (2) segments. <br>variables are:alldata: segments x time x datatype datatypes are: 1) EEG 0.2-4 Hz amplitude envelope; 2) gray matter BOLD; 3) CSF signal<br>allstate: 1=sleep, 2=wake<br>Data are from Fultz et al., Science, 2019; full methods details are presented there.
DOI: 10.5517/ccdc.csd.cc23r5pr
title: CCDC 1958694: Experimental Crystal Structure Determination

description: Related Article: Khetpakorn Chakarawet, T. David Harris, Jeffrey R. Long|2020|Chemical Science|11|8196|doi:10.1039/D0SC03078C
DOI: 10.5517/ccdc.csd.cc23r5qs
title: CCDC 1958695: Experimental Crystal Structure Determination

description: Related Article: Khetpakorn Chakarawet, T. David Harris, Jeffrey R. Long|2020|Chemical Science|11|8196|doi:10.1039/D0SC03078C
DOI: 10.6084/m9.figshare.9991991
title: RAW_mzIdentML_Oct16_2019_PANX1 variants analysis

description: Tryptic in-gel digests were analyzed using RP LC (95 min gradient run) ESI Orbitrap Elite, in an FT/IT/CID Top 20 scheme. Raw data were analyzed using PEAKS Studio 8.5. Sequences from three PANX1 variants, Q5, Q5H, and Y150F, were added to Contaminants database (uniprot) and data searched against it. Trypsin was selected with 3 missed cleavages and one non-specific missed cleavage. Carbamidomethylation (C) was selected as a fixed modification and deamidation (NQ), oxidation (M), acetylation (Protein N-term), and phosphorylation (STY) were selected as variable modifications, with maximum 7 variable modifications per peptide allowed. Parents mass error tolerance was set to 15ppm and fragment mass error tolerance was set to 0.7Da. FDR was estimated using a Target-Decoy. Another search was performed with S-nitrosylation as a variable modification, in addition to all above mentioned modifications.
DOI: 10.6084/m9.figshare.9991991.v2
title: RAW_mzIdentML_Oct16_2019_PANX1 variants analysis

description: Tryptic in-gel digests were analyzed using RP LC (95 min gradient run) ESI Orbitrap Elite, in an FT/IT/CID Top 20 scheme. Raw data were analyzed using PEAKS Studio 8.5. Sequences from three PANX1 variants, Q5, Q5H, and Y150F, were added to Contaminants database (uniprot) and data searched against it. Trypsin was selected with 3 missed cleavages and one non-specific missed cleavage. Carbamidomethylation (C) was selected as a fixed modification and deamidation (NQ), oxidation (M), acetylation (Protein N-term), and phosphorylation (STY) were selected as variable modifications, with maximum 7 variable modifications per peptide allowed. Parents mass error tolerance was set to 15ppm and fragment mass error tolerance was set to 0.7Da. FDR was estimated using a Target-Decoy. Another search was performed with S-nitrosylation as a variable modification, in addition to all above mentioned modifications.
DOI: 10.6084/m9.figshare.9991991.v1
title: RAW_mzIdentML_Oct16_2019_PANX1 variants analysis

description: Tryptic in-gel digests were analyzed using C18 LC (95 min gradient run) ESI Orbitrap Elite, using an FT/IT/CID Top 20 scheme. Raw data were analyzed using PEAKS Studio 8.5. Sequences from three PANX1 variants, Q5, Q5H, and Y150F, were added to Contaminants database (uniprot) and data searched against it. Trypsin was selected with 3 missed cleavages and one non-specific missed cleavage. Carbamidomethylation (C) was selected as a fixed modification and deamidation (NQ), oxidation (M), acetylation (Protein N-term), and phosphorylation (STY) were selected as variable modifications, with maximum 7 variable modifications per peptide allowed. Parents mass error tolerance was set to 15ppm and fragment mass error tolerance was set to 0.7Da. FDR was estimated using a Target-Decoy.
DOI: 10.5517/ccdc.csd.cc24b95z
title: CCDC 1976100: Experimental Crystal Structure Determination

description: Related Article: Suven Das, Purak Das, Suvendu Maity, Prasanta Ghosh, Arpita Dutta|2021|J.Mol.Struct.|1224|129033|doi:10.1016/j.molstruc.2020.129033
DOI: 10.5517/ccdc.csd.cc24ds5j
title: CCDC 1978487: Experimental Crystal Structure Determination

description: Related Article: S. Amrutha, Lopamudra Giri, Sunil SeethaLekshmi, Sunil Varughese|2020|Cryst.Growth Des.|20|5086|doi:10.1021/acs.cgd.0c00266
DOI: 10.5517/ccdc.csd.cc24ds6k
title: CCDC 1978488: Experimental Crystal Structure Determination

description: Related Article: S. Amrutha, Lopamudra Giri, Sunil SeethaLekshmi, Sunil Varughese|2020|Cryst.Growth Des.|20|5086|doi:10.1021/acs.cgd.0c00266
DOI: 10.5522/04/9989741
title: CLEC-2/PDPN signalling phospho proteomics

description: Fold difference values from TMT proteomics of total protein and phosphopeptides, 5 biological replicates: control vs CLEC-2 stimulated. 15 minutes and 24 hour time points.Relating to Figure 3d<br>AbstractLymph nodes (LNs) work as filtering organs, constantly sampling peripheral cues. This is facilitated by the conduit network, a parenchymal tubular-like structure formed of bundles of aligned extracellular matrix (ECM) fibrils ensheathed by fibroblastic reticular cells (FRCs). LNs undergo 5-fold expansion with every adaptive immune response and yet these ECM-rich structures are not permanently damaged. Whether conduit integrity and filtering functions are affected during cycles of LN expansion and resolution is not known. Here we show that the conduit structure is disrupted during acute LN expansion but FRC-FRC contacts remain intact. In homeostasis, polarised FRCs adhere to the underlying substrate to deposit ECM ba-solaterally. ECM production by FRCs is regulated by the C-type lectin CLEC-2, expressed by dendritic cells (DCs), at transcriptional and secretory levels. Inflamed LNs maintain conduit size-exclusion, but flow becomes leaky, which allows soluble antigens to reach more antigen-presenting cells. We show how dynamic communication between peripheral tissues and LNs changes during immune responses, and describe a mechanism that enables LNs to prevent inflammation-induced fibrosis.<strong>Highlights</strong>FRCs use polarized microtubule networks to guide matrix depositionCLEC-2/PDPN controls matrix production at transcriptional and post-transcriptional levelsFRCs halt matrix production and decouple from conduits during acute LN expansionConduits leak soluble antigen during acute LN expansion
DOI: 10.6084/m9.figshare.9989807
title: Psych and Behav Factors- the POUNDSLOST_Supplementary Table 1.docx

description: <b>Supplementary Table 1</b> Baseline scores of food craving, hunger and satiety and three factor eating questionnaire (TFEQ) according to gender and race (n=811)
DOI: 10.5517/ccdc.csd.cc24ds8m
title: CCDC 1978490: Experimental Crystal Structure Determination

description: Related Article: S. Amrutha, Lopamudra Giri, Sunil SeethaLekshmi, Sunil Varughese|2020|Cryst.Growth Des.|20|5086|doi:10.1021/acs.cgd.0c00266
DOI: 10.5522/04/9989741.v1
title: CLEC-2/PDPN signalling phospho proteomics

description: Fold difference values from TMT proteomics of total protein and phosphopeptides, 5 biological replicates: control vs CLEC-2 stimulated. 15 minutes and 24 hour time points.Relating to Figure 3d<br>AbstractLymph nodes (LNs) work as filtering organs, constantly sampling peripheral cues. This is facilitated by the conduit network, a parenchymal tubular-like structure formed of bundles of aligned extracellular matrix (ECM) fibrils ensheathed by fibroblastic reticular cells (FRCs). LNs undergo 5-fold expansion with every adaptive immune response and yet these ECM-rich structures are not permanently damaged. Whether conduit integrity and filtering functions are affected during cycles of LN expansion and resolution is not known. Here we show that the conduit structure is disrupted during acute LN expansion but FRC-FRC contacts remain intact. In homeostasis, polarised FRCs adhere to the underlying substrate to deposit ECM ba-solaterally. ECM production by FRCs is regulated by the C-type lectin CLEC-2, expressed by dendritic cells (DCs), at transcriptional and secretory levels. Inflamed LNs maintain conduit size-exclusion, but flow becomes leaky, which allows soluble antigens to reach more antigen-presenting cells. We show how dynamic communication between peripheral tissues and LNs changes during immune responses, and describe a mechanism that enables LNs to prevent inflammation-induced fibrosis.<strong>Highlights</strong>FRCs use polarized microtubule networks to guide matrix depositionCLEC-2/PDPN controls matrix production at transcriptional and post-transcriptional levelsFRCs halt matrix production and decouple from conduits during acute LN expansionConduits leak soluble antigen during acute LN expansion
DOI: 10.6084/m9.figshare.9989807.v1
title: Psych and Behav Factors- the POUNDSLOST_Supplementary Table 1.docx

description: <b>Supplementary Table 1</b> Baseline scores of food craving, hunger and satiety and three factor eating questionnaire (TFEQ) according to gender and race (n=811)
DOI: 10.5517/ccdc.csd.cc24ds9n
title: CCDC 1978491: Experimental Crystal Structure Determination

description: Related Article: S. Amrutha, Lopamudra Giri, Sunil SeethaLekshmi, Sunil Varughese|2020|Cryst.Growth Des.|20|5086|doi:10.1021/acs.cgd.0c00266
DOI: 10.5517/ccdc.csd.cc24dsbp
title: CCDC 1978492: Experimental Crystal Structure Determination

description: Related Article: S. Amrutha, Lopamudra Giri, Sunil SeethaLekshmi, Sunil Varughese|2020|Cryst.Growth Des.|20|5086|doi:10.1021/acs.cgd.0c00266
DOI: 10.5517/ccdc.csd.cc24gncn
title: CCDC 1980291: Experimental Crystal Structure Determination

description: Related Article: S. Amrutha, Lopamudra Giri, Sunil SeethaLekshmi, Sunil Varughese|2020|Cryst.Growth Des.|20|5086|doi:10.1021/acs.cgd.0c00266
DOI: 10.6084/m9.figshare.9989552
title: eeg-volition-data-afterica.tar.gz

description: The data contains EEG recordings from 26 subjects under different conditions during making decision between options. There were 2 (or 1 in control condition) options displayed on the screen with reward probability: 20%, 80% or 100%.<br>Data is cleaned from eye artefact using ICA and contains 32 channels arranged in 10-20 system.<br>Data in EEGlab format. The experimental conditions are encoded as markers.
DOI: 10.6084/m9.figshare.9989558
title: Supplementary Table S1

description: Supplementary material for the article: Transcriptomic profiling identifies novel transcripts, isomorphs and lncRNAs in Paracoccidioides brasiliensis
DOI: 10.6084/m9.figshare.9989552.v1
title: eeg-volition-data-afterica.tar.gz

description: The data contains EEG recordings from 26 subjects under different conditions during making decision between options. There were 2 (or 1 in control condition) options displayed on the screen with reward probability: 20%, 80% or 100%.<br>Data is cleaned from eye artefact using ICA and contains 32 channels arranged in 10-20 system.<br>Data in EEGlab format. The experimental conditions are encoded as markers.
DOI: 10.6084/m9.figshare.12866852
title: Figure 5 from manuscript Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining

description: Dataset used to generate figure 5: QCM/QCC plots for SSCA. A) TF latent space (figure5/setA/Results/setATF_SIMLR/5/setA_StabilitySignificativityJittered.pdf). B) TF + miRNA latent space (figure5/setA/Results/setA_TF_mirna_SIMLR/5/setA_StabilitySignificativityJittered.pdf), C) TF + miRNA + IS latent space (figure5/setA/Results/setA_TF_mirna_IS_SIMLR/5/setA_StabilitySignificativityJittered.pdf), D) TF + miRNA + IS + Kinase (figure5/setA/Results/setA_TF_mirna_IS_Kinasi_SIMLR/5/setA_StabilitySignificativityJittered.pdf) latent space. Input counts table for SCA is log10 transformed.
DOI: 10.6084/m9.figshare.12866852.v1
title: Figure 5 from manuscript Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining

description: Dataset used to generate figure 5: QCM/QCC plots for SSCA. A) TF latent space (figure5/setA/Results/setATF_SIMLR/5/setA_StabilitySignificativityJittered.pdf). B) TF + miRNA latent space (figure5/setA/Results/setA_TF_mirna_SIMLR/5/setA_StabilitySignificativityJittered.pdf), C) TF + miRNA + IS latent space (figure5/setA/Results/setA_TF_mirna_IS_SIMLR/5/setA_StabilitySignificativityJittered.pdf), D) TF + miRNA + IS + Kinase (figure5/setA/Results/setA_TF_mirna_IS_Kinasi_SIMLR/5/setA_StabilitySignificativityJittered.pdf) latent space. Input counts table for SCA is log10 transformed.
DOI: 10.6084/m9.figshare.9989507
title: Figure_11_Flood_Hazard_Zones.zip

description: This file contains the raster datasets representing the flood hazard zones downstream of the reservoirs.<br>
DOI: 10.6084/m9.figshare.9989501
title: Figure_9_Reservoir_Storage.zip

description: This file contains the flood inundation depths extracted at sample locations for different storage capacities.
DOI: 10.6084/m9.figshare.9989504
title: Figure_10_Inundation_Differences.zip

description: This file contains the raster datasets for the flood inundation extent differences downstream of the reservoirs. <br>
DOI: 10.6084/m9.figshare.9989501.v1
title: Figure_9_Reservoir_Storage.zip

description: This file contains the flood inundation depths extracted at sample locations for different storage capacities.
DOI: 10.6084/m9.figshare.9989504.v1
title: Figure_10_Inundation_Differences.zip

description: This file contains the raster datasets for the flood inundation extent differences downstream of the reservoirs. <br>
DOI: 10.6084/m9.figshare.9989507.v1
title: Figure_11_Flood_Hazard_Zones.zip

description: This file contains the raster datasets representing the flood hazard zones downstream of the reservoirs.<br>
DOI: 10.6084/m9.figshare.9989495
title: Figure_7_Manning_n_Values.zip

description: This file contains flood inundation depths from different combinations of Manning's n values.
DOI: 10.6084/m9.figshare.9989498
title: Figure_8_Bathymetry_Factors.zip

description: This file contains the flood inundation depths extracted at four sample locations for different bathymetry correction factors.<br><br>
DOI: 10.6084/m9.figshare.9989558.v2
title: Pb18 - Supplementary Material.xlsx

description: Supplementary material for the article: Transcriptomic profiling identifies novel transcripts, isomorphs and lncRNAs in Paracoccidioides brasiliensis
DOI: 10.6084/m9.figshare.9989480
title: Database

description: data of the study
DOI: 10.6084/m9.figshare.9989489
title: Figure_5_Flood_Hazard_Zones.zip

description: This file contains the datasets used to prepare the flood hazard zones for a series of sensitivity parameters.
DOI: 10.6084/m9.figshare.9989495.v1
title: Figure_7_Manning_n_Values.zip

description: This file contains flood inundation depths from different combinations of Manning's n values.
DOI: 10.6084/m9.figshare.9989498.v1
title: Figure_8_Bathymetry_Factors.zip

description: This file contains the flood inundation depths extracted at four sample locations for different bathymetry correction factors.<br><br>
DOI: 10.6084/m9.figshare.9989477
title: Figure_6_Initial_Depths.zip

description: This file contains flood inundation depths extracted at four sample locations for selected initial water depths.
DOI: 10.6084/m9.figshare.9989480.v1
title: Database

description: data of the study
DOI: 10.6084/m9.figshare.9989477.v1
title: Figure_6_Initial_Depths.zip

description: This file contains flood inundation depths extracted at four sample locations for selected initial water depths.
DOI: 10.6084/m9.figshare.9989558.v3
title: Transcriptomic profiling identifies novel transcripts, isomorphs and lncRNAs in Paracoccidioides brasiliensis - Supplementary Table S1

description: Supplementary material for the article: Transcriptomic profiling identifies novel transcripts, isomorphs and lncRNAs in Paracoccidioides brasiliensis
DOI: 10.6084/m9.figshare.9989474
title: Figure_3_Flood_Model_Evaluation.zip

description: This folder contains the datasets used to make Figure 3 of the manuscript, which includes the shape-files for the Modeled extent, FEMA extent, and FEMA excluded from analysis.
DOI: 10.6084/m9.figshare.9989489.v1
title: Figure_5_Flood_Hazard_Zones.zip

description: This file contains the datasets used to prepare the flood hazard zones for a series of sensitivity parameters.
DOI: 10.6084/m9.figshare.9989402
title: Figure_4_Inundation_Area_Difference.zip

description: This file contains the simulated maximum flood extents used in Figure 4 of the manuscript.
DOI: 10.6084/m9.figshare.9989558.v4
title: Supplementary Table S1

description: Supplementary material for the article: Transcriptomic profiling identifies novel transcripts, isomorphs and lncRNAs in Paracoccidioides brasiliensis
DOI: 10.6084/m9.figshare.9989402.v2
title: Figure_4_Inundation_Area_Difference.zip

description: This file contains the simulated maximum flood extents used in Figure 4 of the manuscript.
DOI: 10.6084/m9.figshare.9989474.v1
title: Figure_3_Flood_Model_Evaluation.zip

description: This folder contains the datasets used to make Figure 3 of the manuscript, which includes the shape-files for the Modeled extent, FEMA extent, and FEMA excluded from analysis.
DOI: 10.6084/m9.figshare.9989384
title: 2019-Taylor-and-Francis-Researcher-Survey_Raw-Data_Figshare.xlsx

description: Dataset from the Taylor & Francis Researcher Survey 2019. The researcher survey was sent in July 2019 to authors who had articles published in Taylor & Francis journals between 2017 and 2019. 2,755 researchers responded, answering questions on a range of scholarly communication issues, including publishing habits, licenses, and the impact of future scenarios for making articles open access.
DOI: 10.6084/m9.figshare.9989312
title: github URI for the projects

description: list of URIs to retrieve the projects' code and attributes
DOI: 10.6084/m9.figshare.9989402.v1
title: Figure_4_Inundation_Area_Difference.zip

description: This file contains the simulated maximum flood extent used in Figure 4 of the manuscript
DOI: 10.6084/m9.figshare.9989312.v1
title: github URI for the projects

description: list of URIs to retrieve the projects' code and attributes
DOI: 10.6084/m9.figshare.9989231
title: Data set from Assessing the affective component of pain, and the efficacy of pain control, using conditioned place aversion in calves

description: Raw data in CSV format
DOI: 10.6084/m9.figshare.9989231.v1
title: Data set from Assessing the affective component of pain, and the efficacy of pain control, using conditioned place aversion in calves

description: Raw data in CSV format
DOI: 10.6084/m9.figshare.9989225
title: R code from Assessing the affective component of pain, and the efficacy of pain control, using conditioned place aversion in calves

description: The script used in r for data analysis
DOI: 10.6084/m9.figshare.9989225.v1
title: R code from Assessing the affective component of pain, and the efficacy of pain control, using conditioned place aversion in calves

description: The script used in r for data analysis
DOI: 10.6084/m9.figshare.9989183.v2
title: trajectory restoration algorithm

description: A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks
DOI: 10.6084/m9.figshare.9989006
title: Ecophysiology and genetic diversity in species of the bamboo <i>Chusquea</i> in the high Andes, Venezuela

description: <b>Background</b>: Woody bamboos of the genus <i>Chusquea</i> grow along a broad range of elevations in the Venezuelan Andes. Their growth-form and density vary along the cloud forest – páramo gradient. In this article, we related ecophysiological traits and population genetic diversity information to explain the distribution of growth-form patterns of <i>Chusquea</i> in the Merida Andes, Venezuela. <b>Aims</b>: We quantified differences in the ecophysiological response and genetic diversity of scandent cloud forest and shrub-like páramo bamboos of the genus <i>Chusquea</i>, taking in account the differences in their flowering patterns, growth-form and habitat. <b>Methods</b>: We related low temperature resistance, water relations and leaf gas exchange variables to the growth-form, habitat, flowering patterns and genetic diversity in species of <i>Chusquea</i>. The genetic diversity study was based on Inter Sequence Simple Repeats and Random Amplified Polymorphic DNA markers analysis of cloud forest and páramo populations. <b>Results</b>: Scandent cloud forest and shrub-like páramo species of <i>Chusquea</i> had a very similar ecophysiological response for all the variables analysed during wet and dry seasons and were capable of enduring freezing temperatures through moderate supercooling. Species associated with the cloud forest – páramo gradient maintained low stomatal conductance and transpiration rates that favoured high leaf water potentials, without limiting photosynthetic rates. Shrub-like bamboos growing above the continuous forest line had a small decline in net photosynthesis rates, associated with an increase in water use efficiency. Both scandent and shrub-like bamboos had a remarkably high genetic diversity, comparable to non-clonal species. <b>Conclusions</b>: Species of <i>Chusquea</i> in the Venezuelan Andes are a physiologically relatively homogeneous group across a broad elevation gradient. Population genetic diversity appears to be more related with their flowering pattern and habitat conditions than with their growth form.
DOI: 10.6084/m9.figshare.9989000
title: Raw data for oscillatory analysis, EEG

description: This is the raw data for the part 1 (neural oscillations, exposure) for Fantasmas_EEG project<br>