cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57770/ynyjq3
title: Chemical interference with DSIF complex formation lowers synthesis of mutant huntingtin gene products and curtails mutant phenotypes

description: Earlier work has shown that siRNA-mediated reduction of the SUPT4H or SUPT5H proteins, which interact to form the DSIF complex and facilitate transcript elongation by RNA polymerase II (RNAPII), can decrease expression of mutant gene alleles containing nucleotide repeat expansions differentially. Using luminescence and fluorescence assays, we identified chemical compounds that interfere with the SUPT4H-SUPT5H interaction and then investigated their effects on synthesis of mRNA and protein encoded by mutant alleles containing repeat expansions in the huntingtin gene (HTT), which causes the inherited neurodegenerative disorder, Huntington's Disease (HD). Here we report that such chemical interference can differentially affect expression of HTT mutant alleles, and that a prototypical chemical, 6-azauridine (6-AZA), that targets the SUPT4H-SUPT5H interaction can modify the biological response to mutant HTT gene expression. Selective and dose-dependent effects of 6-AZA on expression of HTT alleles containing nucleotide repeat expansions were seen in multiple types of cells cultured in vitro, and in a Drosophila melanogaster animal model for HD. Lowering of mutant HD protein and mitigation of the Drosophila "rough eye" phenotype associated with degeneration of photoreceptor neurons in vivo were observed. Our findings indicate that chemical interference with DSIF complex formation can decrease biochemical and phenotypic effects of nucleotide repeat expansions.
DOI: 10.5281/zenodo.7057595
title: INLAND DVGM output - SSP1-RCP2.6 and SSP3-RCP8.5

description: We used the INLAND DGVM model to simulate the water and carbon cycle in the present climate and also under two different CC and LUC scenarios. To stabilize the carbon and water stocks, we used a 500-years spin up run, with dynamic vegetation, performed with historical data from CRU-JRA (Climatic Research Unit and Japanese reanalysis - detailed by Harris, 2019). After that, we started the two different scenario and recorded the model outputs from 2016 to 2095 (80 years), in a 0.5 X 0.5 degrees grid (latitude AND longitude). The meteorological forcing and CO<sub>2</sub> concentration are taken from HADGEM2-ES (Collins <em>et al.</em>, 2011), from ISIMIP<sup><sup>[1]</sup></sup> Project (Inter-Sectorial Impact Model Intercomparison Project), from 2b Repository (https://data.isimip.org/), described at Frieler et al., 2017. We choose to use the Bias corrected data. As input of land use and land cover maps, we used the refined regional scenarios provided by (Bezerra et al., 2022), data available at https://zenodo.org/record/5123560#.YVJFzzlv80M (Bezerra et al., 2021), [1] https://www.isimip.org/documents/564/ISIMIP2b_protocol_210131_noEnergy_IV_JS-1_IV_pmp-1.pdf
DOI: 10.5281/zenodo.7057596
title: INLAND DVGM output - SSP1-RCP2.6 and SSP3-RCP8.5

description: We used the INLAND DGVM model to simulate the water and carbon cycle in the present climate and also under two different CC and LUC scenarios. To stabilize the carbon and water stocks, we used a 500-years spin up run, with dynamic vegetation, performed with historical data from CRU-JRA (Climatic Research Unit and Japanese reanalysis - detailed by Harris, 2019). After that, we started the two different scenario and recorded the model outputs from 2016 to 2095 (80 years), in a 0.5 X 0.5 degrees grid (latitude AND longitude). The meteorological forcing and CO<sub>2</sub> concentration are taken from HADGEM2-ES (Collins <em>et al.</em>, 2011), from ISIMIP<sup><sup>[1]</sup></sup> Project (Inter-Sectorial Impact Model Intercomparison Project), from 2b Repository (https://data.isimip.org/), described at Frieler et al., 2017. We choose to use the Bias corrected data. As input of land use and land cover maps, we used the refined regional scenarios provided by (Bezerra et al., 2022), data available at https://zenodo.org/record/5123560#.YVJFzzlv80M (Bezerra et al., 2021), [1] https://www.isimip.org/documents/564/ISIMIP2b_protocol_210131_noEnergy_IV_JS-1_IV_pmp-1.pdf
DOI: 10.25403/upresearchdata.19145087.v2
title: Genetic diversity, population structure and clonal verification in South African avocado cultivar germplasm using single nucleotide polymorphism (SNP) markers

description: Formatted data of 326 genotyped individuals including industry suspected variety, discriminant analysis of principal components (DAPC) assigned variety, assigned affinity propagation groups, silhouette scores, and genotype data. This datasets also include DAPC analysis of 159 genotypes indicating the cluster’s composition from K = 2 until K = 7 a) Bayesian information criterion (BIC) plot b) DAPC scatterplot - The eigenvalues and variance of each PC are found within parentheses on each axis. Individuals are represented as dots c) Genomic composition plot - Each thin vertical line in the bar plot represents one individual and each colour represents one inferred ancestral population. The length of each colour in a vertical bar represents the proportion of that individual’s ancestry that is derived from the inferred ancestral population corresponding to that colour. The same colour in different individuals indicates that they belong to the same cluster, indicating admixture. <br> <br> <br> <br> <br>
DOI: 10.25403/upresearchdata.19145087
title: Genetic diversity, population structure and clonal verification in South African avocado cultivar germplasm using single nucleotide polymorphism (SNP) markers

description: Formatted data of 326 genotyped individuals including industry suspected variety, discriminant analysis of principal components (DAPC) assigned variety, assigned affinity propagation groups, silhouette scores, and genotype data. This datasets also include DAPC analysis of 159 genotypes indicating the cluster’s composition from K = 2 until K = 7 a) Bayesian information criterion (BIC) plot b) DAPC scatterplot - The eigenvalues and variance of each PC are found within parentheses on each axis. Individuals are represented as dots c) Genomic composition plot - Each thin vertical line in the bar plot represents one individual and each colour represents one inferred ancestral population. The length of each colour in a vertical bar represents the proportion of that individual’s ancestry that is derived from the inferred ancestral population corresponding to that colour. The same colour in different individuals indicates that they belong to the same cluster, indicating admixture. <br> <br> <br> <br> <br>
DOI: 10.25403/upresearchdata.19145087.v1
title: Genetic structure analysis and assessment of susceptibility to Fusarium euwallaceae of popular avocado cultivars

description: Formatted avocado SNP data, including affinity propagation groups, silhouette scores, genotype data, mislabelled individuals (red) and excluded clonal individuals (yellow).<br>
DOI: 10.14272/ompmadrxwahdgv-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (2,2,2-trifluoro-N-(2-iodophenyl)acetamide)
title: InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, CDCl3 [77.16 ppm] ppm) δ = 154.9 (q, J = 37.8 Hz), 139.4 (+, CH), 135.8, 129.8 (+, CH), 128.02 (+, CH), 122.3 (+, CH), 115.8 (q, J = 288.9 Hz), 90.4.
DOI: 10.14272/ompmadrxwahdgv-uhfffaoysa-n/chmo0000596
title: distortionless enhancement with polarization transfer (DEPT) (2,2,2-trifluoro-N-(2-iodophenyl)acetamide)
title: InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/ompmadrxwahdgv-uhfffaoysa-n/chmo0000597
title: 19F nuclear magnetic resonance spectroscopy (19F NMR) (2,2,2-trifluoro-N-(2-iodophenyl)acetamide)
title: InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)

description: dataset for 19F nuclear magnetic resonance spectroscopy (19F NMR) 19F NMR (376 MHz, CDCl3, ppm) δ = -75.82.
DOI: 10.14272/ompmadrxwahdgv-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (2,2,2-trifluoro-N-(2-iodophenyl)acetamide)
title: InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)

description: dataset for mass spectrometry (MS) MS (EI, 70 eV, 20 °C), m/z (%): 316 (6) [M+1]+, 315 (79) [M]+, 189 (9), 188 (100), 168 (17), 119 (12), 102 (11), 91 (19), 90 (5), 69 (6), 64 (7), 63 (6). HRMS (C8H5F3INO): calcd 314.9363, found 314.9361.
DOI: 10.14272/ompmadrxwahdgv-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (2,2,2-trifluoro-N-(2-iodophenyl)acetamide)
title: InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3293 (w), 3116 (vw), 2924 (vw), 2853 (vw), 1704 (s), 1674 (w), 1581 (m), 1538 (m), 1462 (w), 1438 (m), 1421 (w), 1341 (w), 1272 (m), 1239 (w), 1203 (m), 1156 (vs), 1113 (s), 1041 (w), 1016 (s), 949 (w), 911 (s), 875 (w), 860 (w), 836 (w), 765 (s), 755 (vs), 734 (vs), 700 (w), 647 (s), 618 (vs), 605 (s), 550 (s), 535 (m), 509 (s), 473 (w), 449 (w), 435 (s), 411 (w), 398 (w) cm–1.
DOI: 10.14272/ompmadrxwahdgv-uhfffaoysa-n.1
title: 2,2,2-trifluoro-N-(2-iodophenyl)acetamide
title: InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)

description: This dataset contains experimental data from a sample (2,2,2-trifluoro-N-(2-iodophenyl)acetamide, InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 19F nuclear magnetic resonance spectroscopy (19F NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) distortionless enhancement with polarization transfer (DEPT) distortionless enhancement with polarization transfer (DEPT) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/ompmadrxwahdgv-uhfffaoysa-n/chmo0000596.1
title: distortionless enhancement with polarization transfer (DEPT) (2,2,2-trifluoro-N-(2-iodophenyl)acetamide)
title: InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/ompmadrxwahdgv-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2,2,2-trifluoro-N-(2-iodophenyl)acetamide)
title: InChI=1S/C8H5F3INO/c9-8(10,11)7(14)13-6-4-2-1-3-5(6)12/h1-4H,(H,13,14)

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3 [7.26 ppm] ppm) δ = 8.28 (bs, 1H), 8.20 (dd, J = 8.3, 1.5 Hz, 1H), 7.84 (dd, J = 8.0, 1.5 Hz, 1H), 7.42 (td, J = 7.9, 1.5 Hz, 1H), 6.98 (td, J = 7.7, 1.6 Hz, 1H). Impurities: spectrum contains water signal at 1.58 ppm.
DOI: 10.5281/zenodo.5911069
title: Data set for the article 'Robust replication initiation from coupled homeostatic mechanisms'

description: This data set contains the data of the submitted article "Robust replication initiation from coupled homeostatic mechanisms". The data was generated using simulations in python that are linked below. Experiments indicate that E. coli controls replication initiation via titration and activation of the initiator protein DnaA. We study by mathematical modelling how these two mechanisms interact to generate robust replication-initiation cycles.
DOI: 10.5281/zenodo.5911070
title: Data set for the article 'Robust replication initiation from coupled homeostatic mechanisms'

description: This data set contains the data of the submitted article "Robust replication initiation from coupled homeostatic mechanisms". The data was generated using simulations in python that are linked below. Experiments indicate that E. coli controls replication initiation via titration and activation of the initiator protein DnaA. We study by mathematical modelling how these two mechanisms interact to generate robust replication-initiation cycles.
DOI: 10.5281/zenodo.7057532
title: Data set for the article 'Robust replication initiation from coupled homeostatic mechanisms'

description: This data set contains the data of the submitted article "Robust replication initiation from coupled homeostatic mechanisms". The data was generated using simulations in python that are linked below. Experiments indicate that E. coli controls replication initiation via titration and activation of the initiator protein DnaA. We study by mathematical modelling how these two mechanisms interact to generate robust replication-initiation cycles.
DOI: 10.5281/zenodo.7057659
title: The French Covid-19 vaccination policy did not solve vaccination inequities a nationwide study on 64.5 million people

description: Data and code to reproduce the analysis of our article. # Contents `code/`: Analysis code. <br> The main analysis file is `vaccination-indicators.Rmd`. <br> Some results are exported in `out*.RData` files. `data/`: Data used for the analysis. <br> The data are treated by the `code/0_INSEE_predictors.R` script, and saved as `code/data_indicators.RData`, which is the file used for analysis. `ms/`: Manuscript files; they are outdated (the ms was later modified with Word), but `ms.Rmd` contains code to reproduce the figures and some numerical values given in the text. # Data Sources - Vaccination data from Assurance Maladie: <br> - EPCI: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/donnees-devaccination-par-epci/&gt;<br> - Paris, Marseille, Lyon: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/donnees-de-vaccination-parcommune/information/&gt; - Geographic information:<br> - EPCI: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/georef-france-epci/&gt;<br> - Paris, Marseille, Lyon: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/georef-france-commune-arrondissement-municipal/&gt; - Socio-economic indicators from INSEE: &lt;https://www.insee.fr/fr/statistiques/5359146#consulter&gt; - 2017 Presidential election:<br> - &lt;https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-definitifs-du-1er-tour-par-communes/#resource-d282e53a-d273-425d-95bb-8a0d7632c79a-header&gt; <br> https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-du-2eme-tour-2/<br> - Paris: &lt;https://opendata.paris.fr/explore/dataset/elections-presidentielles-2017-1ertour/export/?disjunctive.id_bvote&disjunctive.num_circ&disjunctive.num_quartier&disjunctive.num_arrond&sort=-num_arrond&gt; <br> - Marseille: &lt;https://trouver.datasud.fr/dataset/82a6d91c-c81d-423c-9a4a-3f76d121c8ce/resource/03e2ef07-c2d0-41dd-b503-26910ecb15c3/download/marseille_presidentielles2017_tour1.csv&gt; <br> - Lyon: &lt;https://www.interieur.gouv.fr/Elections/Les-resultats/Presidentielles/elecresult__presidentielle-2017/(path)/presidentielle-2017/084/069/069L.html&gt;
DOI: 10.5281/zenodo.7057660
title: The French Covid-19 vaccination policy did not solve vaccination inequities a nationwide study on 64.5 million people

description: Data and code to reproduce the analysis of our article. # Contents `code/`: Analysis code. <br> The main analysis file is `vaccination-indicators.Rmd`. <br> Some results are exported in `out*.RData` files. `data/`: Data used for the analysis. <br> The data are treated by the `code/0_INSEE_predictors.R` script, and saved as `code/data_indicators.RData`, which is the file used for analysis. `ms/`: Manuscript files; they are outdated (the ms was later modified with Word), but `ms.Rmd` contains code to reproduce the figures and some numerical values given in the text. # Data Sources - Vaccination data from Assurance Maladie: <br> - EPCI: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/donnees-devaccination-par-epci/&gt;<br> - Paris, Marseille, Lyon: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/donnees-de-vaccination-parcommune/information/&gt; - Geographic information:<br> - EPCI: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/georef-france-epci/&gt;<br> - Paris, Marseille, Lyon: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/georef-france-commune-arrondissement-municipal/&gt; - Socio-economic indicators from INSEE: &lt;https://www.insee.fr/fr/statistiques/5359146#consulter&gt; - 2017 Presidential election:<br> - &lt;https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-definitifs-du-1er-tour-par-communes/#resource-d282e53a-d273-425d-95bb-8a0d7632c79a-header&gt; <br> https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-du-2eme-tour-2/<br> - Paris: &lt;https://opendata.paris.fr/explore/dataset/elections-presidentielles-2017-1ertour/export/?disjunctive.id_bvote&disjunctive.num_circ&disjunctive.num_quartier&disjunctive.num_arrond&sort=-num_arrond&gt; <br> - Marseille: &lt;https://trouver.datasud.fr/dataset/82a6d91c-c81d-423c-9a4a-3f76d121c8ce/resource/03e2ef07-c2d0-41dd-b503-26910ecb15c3/download/marseille_presidentielles2017_tour1.csv&gt; <br> - Lyon: &lt;https://www.interieur.gouv.fr/Elections/Les-resultats/Presidentielles/elecresult__presidentielle-2017/(path)/presidentielle-2017/084/069/069L.html&gt;
DOI: 10.5281/zenodo.7057679
title: The French Covid-19 vaccination policy did not solve vaccination inequities a nationwide study on 64.5 million people

description: Data and code to reproduce the analysis of our article. # Contents `code/`: Analysis code. <br> The main analysis file is `vaccination-indicators.Rmd`. <br> Some results are exported in `out*.RData` files. `data/`: Data used for the analysis. <br> The data are treated by the `code/0_INSEE_predictors.R` script, and saved as `code/data_indicators.RData`, which is the file used for analysis. `ms/`: Manuscript files; they are outdated (the ms was later modified with Word), but `ms.Rmd` contains code to reproduce the figures and some numerical values given in the text. # Data Sources - Vaccination data from Assurance Maladie: <br> - EPCI: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/donnees-devaccination-par-epci/&gt;<br> - Paris, Marseille, Lyon: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/donnees-de-vaccination-parcommune/information/&gt; - Geographic information:<br> - EPCI: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/georef-france-epci/&gt;<br> - Paris, Marseille, Lyon: &lt;https://datavaccin-covid.ameli.fr/explore/dataset/georef-france-commune-arrondissement-municipal/&gt; - Socio-economic indicators from INSEE: &lt;https://www.insee.fr/fr/statistiques/5359146#consulter&gt; - 2017 Presidential election:<br> - &lt;https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-definitifs-du-1er-tour-par-communes/#resource-d282e53a-d273-425d-95bb-8a0d7632c79a-header&gt; <br> https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-du-2eme-tour-2/<br> - Paris: &lt;https://opendata.paris.fr/explore/dataset/elections-presidentielles-2017-1ertour/export/?disjunctive.id_bvote&disjunctive.num_circ&disjunctive.num_quartier&disjunctive.num_arrond&sort=-num_arrond&gt; <br> - Marseille: &lt;https://trouver.datasud.fr/dataset/82a6d91c-c81d-423c-9a4a-3f76d121c8ce/resource/03e2ef07-c2d0-41dd-b503-26910ecb15c3/download/marseille_presidentielles2017_tour1.csv&gt; <br> - Lyon: &lt;https://www.interieur.gouv.fr/Elections/Les-resultats/Presidentielles/elecresult__presidentielle-2017/(path)/presidentielle-2017/084/069/069L.html&gt;
DOI: 10.6084/m9.figshare.19765849.v3
title: Data and code for aging C57BL/6J proteomics manuscript

description: Data and code used for the aging C57BL/6J proteomics manuscript. Data includes quantitative protein profiling from 10 tissues (kidney, liver, fat, spleen, lung, heart, skeletal muscle, striatum, cerebellum, and hippocampus) from 20 C57BL/6J mice, representing balanced groups by sex and age (18 and 8 months-old). Code for processing data, running analysis, and producing figures are included. Code and data to locally run an interactive data viewer (RShiny application) is also included.
DOI: 10.6084/m9.figshare.21026443.v1
title: RNA-Seq data and pseudotargeted lipidomic analysis (LC-MS data) in "Retinoids inhibit ferroptosis through their radical-trapping properties and RAR-SCD-mediated monounsaturated fatty acids generation".

description: In our study titled "Retinoids inhibit ferroptosis through their radical-trapping properties and RAR-SCD-mediated monounsaturated fatty acids generation", we compared the gene expression pattern (Figure 4) and abundance of lipids (Figure 5 and S5) between Cas-NC and RETSAT-KO A549 cells treated with DMSO, IKE, or RSL3 using RNA-Seq and pseudotargeted lipidomic analysis respectively.
DOI: 10.6084/m9.figshare.21030280
title: pseudastur_occidentalis_decontaminated_FSCR.fasta.gz

description: ........
DOI: 10.6084/m9.figshare.21030280.v1
title: pseudastur_occidentalis_decontaminated_FSCR.fasta.gz

description: ........
DOI: 10.6084/m9.figshare.21030250
title: pionites_leucogaster_decontaminated_FSCR.fasta.gz

description: ........
DOI: 10.6084/m9.figshare.21030250.v1
title: pionites_leucogaster_decontaminated_FSCR.fasta.gz

description: ........
DOI: 10.7910/dvn/6cjijt/sk6wsu
title: 01009Roberto-Friendships-PaperData.pdf

description: Questions and typed responses
DOI: 10.23729/060b26a6-c44a-4243-942c-556c09e24627
title: Helford Black Mass preview

description: Samples are recycled Li batteries crushed, i.e. "black mass". They were imaged to investigate their structure in 3D beyond the SEM images. Preview of PAS-dataset https://doi.org/10.23729/f58168b8-641e-49cb-8c50-e8fdcb7995a6
DOI: 10.5281/zenodo.7056645
title: Ground Penetrating Radar (GPR) survey of four artificial ice reservoirs (icestupas)

description: <strong>Objective</strong> The objective of this project is to establish that non–invasive subsurface imaging with ground penetrating radar (GPR) is a viable method to map internal structure of artificial ice reservoirs(AIR) and to estimate volume. Particular objectives were the following: <strong>Volume validation : </strong>GPR surveys could be used to validate volumes of AIRs. <strong>Spatial density resolution : </strong>GPR data could help identify air, snow and ice layers within the AIR. <strong>GPR Field Work</strong> GPR survey has been conducted on four Ice Stupas at locations namely Phaterak, Gangles, Kullum and Takmachik in March 2020 as part of an Swiss Polar Institute funded expedition. <strong>Why Ground Penetrating Radar?</strong> Ice is very transparent to GPR signals, allowing tremendous penetration. GPR is also sensitive to subtle changes in the properties of ice layers. This makes it a powerful tool to image the internal structure of glaciers and ice sheets at a scale of meters or hundreds of meters. The basic principle of a pulsed GPR system is to send an electromagnetic signal into the ground and to record the signal reflections as a function of their two-way travel time. Partial reflections of the electromagnetic wave recorded as internal reflection horizons (IRH) occur at vertical discontinuities in the dielectric material. From polar studies, IRH are known to coincide with variations in density, acidity (Robin et al., 1969), liquid water content (Forster et al., 2014) and changes in crystal orientation fabric. <strong>Archive contents</strong> The archives contents are organized in four separate directories. Each directory contains GPR data of one icestupa. Apart from this, a preliminary report is also attached produced by the GPR company (SHIJAY PROJECTS).