cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/grffnvg89n.1
title: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties; Niekamp et al.; part 1 of 5

description: This project is described in: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties (2023) and can be found on BioRxiv: https://www.biorxiv.org/content/10.1101/2023.10.26.564217v2

Here, we provide the experimental data together with the custom python code and ImageJ macros used for data analysis. Details on how the code works are given in the script itself.

This is part 1 of 5.

Part 1: Data for Figure 1, 2, and 3
Part 2: Data for Figure 4, 5, 6, and 7
Part 3: Data for Figure S1, S2, S3, S4, and S5
Part 4: Data for Figure S6
Part 5: Data for Figure S7
DOI: 10.17632/cp5nz8frn7.1
title: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties; Niekamp et al.; part 3 of 5

description: This project is described in: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties (2023) and can be found on BioRxiv: https://www.biorxiv.org/content/10.1101/2023.10.26.564217v2

Here, we provide the experimental data together with the custom python code and ImageJ macros used for data analysis. Details on how the code works are given in the script itself.

This is part 3 of 5.

Part 1: Data for Figure 1, 2, and 3
Part 2: Data for Figure 4, 5, 6, and 7
Part 3: Data for Figure S1, S2, S3, S4, and S5
Part 4: Data for Figure S6
Part 5: Data for Figure S7
DOI: 10.17632/chxp8x3ygn.1
title: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties; Niekamp et al.; part 4 of 5

description: This project is described in: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties (2023) and can be found on BioRxiv: https://www.biorxiv.org/content/10.1101/2023.10.26.564217v2

Here, we provide the experimental data together with the custom python code and ImageJ macros used for data analysis. Details on how the code works are given in the script itself.

This is part 4 of 5.

Part 1: Data for Figure 1, 2, and 3
Part 2: Data for Figure 4, 5, 6, and 7
Part 3: Data for Figure S1, S2, S3, S4, and S5
Part 4: Data for Figure S6
Part 5: Data for Figure S7
DOI: 10.17632/6nc5kd3h9b.1
title: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties; Niekamp et al.; part 5 of 5

description: This project is described in: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties (2023) and can be found on BioRxiv: https://www.biorxiv.org/content/10.1101/2023.10.26.564217v2

Here, we provide the experimental data together with the custom python code and ImageJ macros used for data analysis. Details on how the code works are given in the script itself.

This is part 5 of 5.

Part 1: Data for Figure 1, 2, and 3
Part 2: Data for Figure 4, 5, 6, and 7
Part 3: Data for Figure S1, S2, S3, S4, and S5
Part 4: Data for Figure S6
Part 5: Data for Figure S7
DOI: 10.17632/dp49md77xz.1
title: Main elements of the Responsible Investment system: subjects, objects and relevant financial instruments

description: The presented statistical data reflect new challenges in the field of Responsible Investment. This issue is of great importance to financial service providers, financial resource recipients, and academic researchers in the field of Sustainable Finance. Consequently, the content and structure of empirical data correspond to possible information requirements in this area, including information on Responsible Investments and other segments of the financial services market. The presented data relate to: a) the main elements of the Responsible Investment system; b) Instrumental structure of the Responsible Investment market; c) Global sustainable debt financing market for 2013-2023 (2024F); d) basic information on sustainable stock exchanges: dissemination of ESG reporting around the world (in 2024); e) data on the UN PRI signatures growth with additional dynamic indicators.
DOI: 10.17632/dywwnjv22h.1
title: Dataset - Image-Driven Prediction of Fatigue Crack Growth in Metal Materials via Spatiotemporal Neural Network

description: File #1 "DataFromDIC_CSV" contains displacement fields obtained from DIC measurement for 19 specimens. The data is saved in .csv format.
File #2 "DataAfterInterpolation_PT" contains interpolated displacement fields from the DIC results for 19 specimens. The data is saved in .pt format. They are used as input for training, validation and testing. We use open-source program "Griddata" to do the interpolation.
File #3 "TrainedModel_PT" contains the trained neural networks used in the paper.
File #4 "Cycles vs Crack Length.xlsx" shows the relations between fatigue cycles and crack lengths measured from .PT files. We only measured crack lengths for 4 specimens used in the paper.
File #5 "Readme.txt" shows the naming system for the folders in File #1 and #2.
DOI: 10.17632/65ywpf8tkb.1
title: Dataset DEA_Azizee134

description: The dataset population consists of SMEs in Malaysia. A total of 134 respondents responded through an online survey.
DOI: 10.17632/2g6wbhs6d8.1
title: Single-cell transcriptomic profiling reveals decreased ER Protein Reticulon3 drives the progression of renal fibrosis

description: We delve into the consequences of Rtn3 protein deficiency in CKD by studying Rtn3-null-induced CKD mice. Our single-cell transcriptome data profiles 47,885 cells from the renal cortex region of healthy and Rtn3-null mice. We observed Rtn3 deficiency reshapes both the spatial architectures and the expression profiles across various cell types, mirroring the CKD characteristics. Meanwhile, Rtn3 deficiency leads to Lars2 overexpression, causing mitochondrial dysfunction and a subsequent rise in ROS levels, prompting renal epithelial cells to shift from a functional to a fibrogenic state, driving renal fibrosis. And Rtn3 deficiency drives the EndoMT process, exacerbating renal fibrosis in CKD. Lastly, Rtn3 deficiency disrupts cell-cell communications, which are important for proper kidney function, and potentially exacerbates CKD progression. In conclusion, Rtn3 protein deficiency leads to significant alterations in spatial architectures and molecular profiles for various cell types, and disrupts cell-cell communications. Our findings offer a holistic view of Rtn3 protein's intricate role in CKD and emphasize its potential therapeutic significance.
DOI: 10.5281/zenodo.7665639
title: Parental Engagement and Relationships (PEAR) in Early Childhood (EC). Implementation study: Parents' views

description: Data and codebook files related to collecting parents' views within the implementation study of the project Parental Engagement and Relationships (PEAR) in Early Childhood (EC). The following files are shared below.

- Full quantitative data and selected qualitative data (Microsoft Office Excel file)

- Codebook for coding the qualitative data (pdf file)

Files are named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925.
DOI: 10.5281/zenodo.7665640
title: Parental Engagement and Relationships (PEAR) in Early Childhood (EC). Implementation study: Parents' views

description: Data and codebook files related to collecting parents' views within the implementation study of the project Parental Engagement and Relationships (PEAR) in Early Childhood (EC). The following files are shared below.

- Full quantitative data and selected qualitative data (Microsoft Office Excel file)

- Codebook for coding the qualitative data (pdf file)

Files are named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925.
DOI: 10.34740/kaggle/dsv/7630571
title: Ethiopian_kale android app code
DOI: 10.5281/zenodo.7032089
title: Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets

description: This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (article).All results were computed using the R package pairedGSEA, which utilized DESeq2 (Love et al., 2014), DEXSeq (Anders et al., 2012), and fgsea (Korotkevich et al., 2019).See limma results here: https://doi.org/10.5281/zenodo.8162214Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title.The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS".All datasets were obtained from a local copy of the ARCHS4 v11 database of  transcript counts (Lachmann et al., 2018).
DOI: 10.5281/zenodo.7032090
title: Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets

description: This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (preprint).<br> All results were computed using the R package pairedGSEA, which utilized DESeq2 (Love et al., 2014), DEXSeq (Anders et al., 2012), and fgsea (Korotkevich et al., 2019).<br> See limma results here: https://doi.org/10.5281/zenodo.7866420<br> <br> Each .RDS file contains a list with three objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, and a 'gene_set' object with over-representation results.<br> <br> The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS".<br> <br> All datasets were obtained from a local copy of the ARCHS4 v11 database of transcript counts (Lachmann et al., 2018).
description: {"references": ["Lachmann, A., Torre, D., Keenan, A.B. et al. Massive mining of publicly available RNA-seq data from human and mouse. Nat Commun 9, 1366 (2018). https://doi.org/10.1038/s41467-018-03751-6", "Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)", "Anders, S., Reyes, A., & Huber, W. (2012). Detecting differential usage of exons from RNA-seq data. Genome Research, 22(10), 2008\u20132017. https://doi.org/10.1101/gr.133744.111", "G. Korotkevich, V. Sukhov, A. Sergushichev. Fast gene set enrichment analysis. bioRxiv (2019), doi:10.1101/060012"]}
DOI: 10.5281/zenodo.7866420
title: Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets (Limma)

description: OBS! This is the limma results of the analysis. See https://doi.org/10.5281/zenodo.7032090 for the DESeq2/DEXSeq results.<br> <br> This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (preprint).<br> All results were computed using the R package pairedGSEA, which utilized Limma (Ritchie et al., 2015) and fgsea (Korotkevich et al., 2019).<br> <br> Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title.<br> <br> The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS".<br> <br> All datasets were obtained from a local copy of the ARCHS4 v11 database of transcript counts (Lachmann et al., 2018).
description: {"references": ["Lachmann, A., Torre, D., Keenan, A.B. et al. Massive mining of publicly available RNA-seq data from human and mouse. Nat Commun 9, 1366 (2018). https://doi.org/10.1038/s41467-018-03751-6", "Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)", "Anders, S., Reyes, A., & Huber, W. (2012). Detecting differential usage of exons from RNA-seq data. Genome Research, 22(10), 2008\u20132017. https://doi.org/10.1101/gr.133744.111", "G. Korotkevich, V. Sukhov, A. Sergushichev. Fast gene set enrichment analysis. bioRxiv (2019), doi:10.1101/060012"]}
DOI: 10.5281/zenodo.8162214
title: Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets (Limma)

description: OBS! This is the limma results of the analysis. See https://doi.org/10.5281/zenodo.8162229 for the DESeq2/DEXSeq results.This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (article).All results were computed using the R package pairedGSEA, which utilized Limma (Ritchie et al., 2015) and fgsea (Korotkevich et al., 2019).Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title.The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS".All datasets were obtained from a local copy of the ARCHS4 v11 database of  transcript counts (Lachmann et al., 2018).
DOI: 10.5281/zenodo.8162229
title: Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets

description: This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (article).All results were computed using the R package pairedGSEA, which utilized DESeq2 (Love et al., 2014), DEXSeq (Anders et al., 2012), and fgsea (Korotkevich et al., 2019).See limma results here: https://doi.org/10.5281/zenodo.8162214Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title.The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS".All datasets were obtained from a local copy of the ARCHS4 v11 database of  transcript counts (Lachmann et al., 2018).
DOI: 10.25625/1u6xme/9orjuj
title: Rasterized_Landuse.tif

description: :unav
DOI: 10.25625/1u6xme/bts7xp
title: la_landscape_metrics_final.csv

description: :unav
DOI: 10.25625/1u6xme/oolh3b
title: style_landuse_LA_plots_500m_buffer.qml

description: :unav
DOI: 10.25625/1u6xme/ipbyuk
title: landuse_LA_plots_500m_buffer.gpkg

description: :unav
DOI: 10.25625/1u6xme/kh5cvu
title: README_landuse_raster.txt

description: :unav
DOI: 10.25625/1u6xme/d05moq
title: README_landscape_metrics.txt

description: :unav
DOI: 10.25625/1u6xme/s1ac2d
title: landuse_LA_plots_500m_buffer.gpkg-journal

description: :unav
DOI: 10.25625/1u6xme/ue3a33
title: Screenshot 2023-09-28 151716.png

description: :unav
DOI: 10.25625/1u6xme/crntwh
title: Screenshot 2023-09-28 152628.png

description: :unav
DOI: 10.25625/1u6xme/hv9s3m
title: Screenshot 2023-09-28 152603.png

description: :unav
DOI: 10.25625/1u6xme/q012jo
title: Screenshot 2023-09-28 151652.png

description: :unav
DOI: 10.25625/1u6xme
title: Landscape Assessment (LA) - Land use and landscape metrics (500m buffers around LA plots)

description: Land use classification and landscape metrics for 500m buffer landscapes around the centroids of 122 plots of the EFForTS-Landscape Assessment. Based on manual classifications by trained students and reviewed by local expert.
DOI: 10.6073/pasta/6c4f69748959fff364ecf89fc293922f
title: Seedling Survey of the CTFS-ForestGEO Plot at Harvard Forest since 2017

description: Within a species, trees can vary in size by many orders of magnitude, from tiny seedlings to multi-ton adults. This intraspecific variation in tree size has important consequences for forest structure and function, as it governs patterns of tree abundance, growth rate and energy flux. Recent theoretical models make predictions about size-energy patterns of forests but available evidence indicates deviation from predictions for the smallest and largest trees. The goal of this project is to examine seedling and canopy tree demography at Harvard Forest and explore their theoretical implications. We will link early remote sensing data from the National Ecological Observatory Network (NEON) with boots-on-the-ground measurements at Harvard Forest plots. We will also fit demographic models of the trees of this forest in transition.
DOI: 10.5281/zenodo.10143576
title: GBS and phenotype data for MASPOT population, a panel of tetraploid potato clones
DOI: 10.6073/pasta/d0bc57386a32da55690eba86efbbad69
title: Candida Pseudoglaebosa Ecology in Sarracenia Purpurea Pitcher Plants at Harvard Forest since 2021

description: Fungi and bacteria are common members of the microbiomes of carnivorous plants. In the pitcher plant Sarracenia purpurea these microorganisms enter carnivorous pitchers shortly after pitchers develop, and may be relevant for pitcher functioning. We are sampling pitchers repeatedly over several years to better understand the culturable diversity in this habitat, with a focus on the common pitcher yeast Candida pseudoglaebosa and fungi that have the potential to interact with it.
 C. pseudoglaebosa dominates pitcher plants by arriving early in pitchers, and we are interested in how this yeast’s populations change over time and in response to other pitcher microorganisms. Between 2021 and 2023, we collected pitcher water from Tom Swamp in Harvard Forest and cultured 118 yeast and bacteria colonies from this water. We have found C. pseudoglaebosa and some other yeasts (Papiliotrema, Rhodotorula, and Sporidiobolus); microbial identification is ongoing. We are planning to investigate changes in C. pseudoglaebosa genetic diversity and changes in C. pseudoglaebosa interactions with other pitcher microorganisms over time.
DOI: 10.60893/figshare.jap.25047869
title: Understanding melting of Ti crystal with spherical voids from molecular dynamics simulations

description: This file contain eleven figures supporting manuscript.
DOI: 10.60893/figshare.jap.25047869.v1
title: Understanding melting of Ti crystal with spherical voids from molecular dynamics simulations

description: This file contain eleven figures supporting manuscript.
DOI: 10.60893/figshare.jap.25047920
title: Supplement Number 1

description: Supplementary information
DOI: 10.60893/figshare.jap.25047920.v1
title: Supplement Number 1

description: Supplementary information
DOI: 10.60893/figshare.jap.25043762
title: Structure files and spreadsheet of properties

description: The supplemental material contains structure files used in this work and a spreadsheet of properties referenced in the figures of the manuscript. For more instruction, please view the README file contained in the supplemental material.
DOI: 10.25781/vqng-f828
title: WEB SCENE Literature Review: Fine-scale spatial and temporal trends in Red Sea coral reef research
DOI: 10.5281/zenodo.10665321
title: Measurements of plots in medieval villages

description: In order to determine the measuring unit used for laying out these villages, we applied thecosine quantogram method. Calculations using the cosine quantogram method were performed (supplemented by bootstrap confidence intervals) on selected measurement series, which included distances between lines with similarangles. Our measurements were based on terrain models generated from LiDAR data. Theaccuracy of measurements was naturally limited not only by the regularity of surface traces detected, but also by the resolution of the digital terrain model (0.5 m raster).
DOI: 10.5281/zenodo.10665322
title: Measurements of plots in medieval villages

description: In order to determine the measuring unit used for laying out these villages, we applied thecosine quantogram method. Calculations using the cosine quantogram method were performed (supplemented by bootstrap confidence intervals) on selected measurement series, which included distances between lines with similarangles. Our measurements were based on terrain models generated from LiDAR data. Theaccuracy of measurements was naturally limited not only by the regularity of surface traces detected, but also by the resolution of the digital terrain model (0.5 m raster).
DOI: 10.5281/zenodo.10523910
title: Balancing Progress and Responsibility: Assessing the Sustainability Trade-offs of AI-based Systems
DOI: 10.5281/zenodo.10523911
title: Balancing Progress and Responsibility: Assessing the Sustainability Trade-offs of AI-based Systems
DOI: 10.5281/zenodo.10665337
title: Balancing Progress and Responsibility: Assessing the Sustainability Trade-offs of AI-based Systems
DOI: 10.5281/zenodo.7187850
title: Data and code for intuitive movement-based prosthesis control in virtual reality

description: This repository contains data and code for


Intuitive movement-based prosthesis control enables arm amputees to reach naturally in virtual reality


Effie Segas1, Sébastien Mick1,2, Vincent Leconte1, Rémi Klotz3, Daniel Cattaert1, Aymar de Rugy1


1 Univ. Bordeaux, CNRS, INCIA, UMR 5287, F-33000 Bordeaux, France


2 ISIR UMR 7222, Sorbonne Université, CNRS, Inserm, F-75005, France


3 CMPR Tour de Gassies, F-33520 Bruges, France


 


 


It contains a dataset (DataOnline_2022_SPCA21 folder) of three experiments of able-bodied participants (Exp1 and Exp2 folders) and amputee participants (Exp3 folder) performing a pick-and-placed task in a virtual reality environment with or without movements-based Artificial Neural Networks (ANN) control involved. More information about these experiments could be find in the link publication (see related identifiers section).


Basic code files to perform data analysis and ANN training are provided in the CodeOnline_2022_SPCA21 folder. 


All the information needed to understand the structure of the DataOnline_2022_SPCA21 and CodeOnline_2022_SPCA21 folders and files are provided in CodeOnline_2022_SPCA21 folder.


The file SummaryOfFiles gives a description of all the files of the DataOnline_2022_SPCA21 folder, a file tree is also provided at the end of the document.


The files MainDataExplained and SensorsDataExplained list and give a description of the variables recorded during experiments in the phase files (e.g. PHASE.json and PHASE_sensors.json respectively).


The three ExpXFilesWorkflow files (with X =1,2,3) give an overview of the work-flow of experimental files creation during the X experiment.


The CodeExplanations file lists and gives a description of code files available in the CodeOnline_2022_SPCA21 folder. The DependencyTree file shows the code organisation. The GuideInstall file contains information needed to run the code files properly.
description: This work was supported by the CNRS interdisciplinary project RoBioVis, and the ANR-DGA-ASTRID grant CoBioPro (ANR-20-ASTR-0012-1).