cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26050/wdcc/st2_mpiomtp6m
title: STORMTIDE2: a concurrent simulation of circulation and tides with MPIOM/TP6M L40 forced with full tidal potential and NCEP reanalysis

description: Project: STORMTIDE2 - A concurrent simulation of circulation and tides with MPIOM/TP6M L40, forced by surface fluxes derived from NCEP/NCAR reanalysis and by luni-solar tidal potential. Summary: The concurrent simulation of ocean circulations and ocean tides is carried out with the Max-Planck Institute Ocean Model (MPIOM/TP6M L40 mpiom-1.6.3.) forced by the full luni-solar tidal potential as an additional body force and by the surface fluxes of momentum, heat and freshwater derived the NCEP/NCAR reanalysis for the period 1981-2012. A tripolar grid with a horizontal resolution of about 0.1 degrees is used. There are total 40 vertical levels in z-coordinates. More details can be find in Li and von Storch (2020). The file name of the data sets is composed as follows. STORMTIDE2_TP6ML40__3d_1hr__.nc There are five 3-dimensional hourly variables: sea_water_potential_temperature (tho), sea_water_salinity (sao), sea_water_x_velocity (u), sea_water_y_velocity (v) and upward_sea_water_velocity (w), for January, April, July and October of 2012. 2012 is the last year of the simulation. Storing four months hourly was doable at the time when the simulation is produced. One run is provided.
DOI: 10.4228/zalf.vjcp-vep3
title: A daily time-step observed and scenario climate dataset on a European grid for crop modelling applications (version 3)

description: Abstract: The data set contains daily time-step observed and scenario climate data on a European grid with 25 km x 25 km spatial resolution and is intended to be used for crop modelling applications. The dataset covers the period 1980-2010 for observations (for a baseline period of 1981-2010 and the year 1980 for crop model simulations with sowing dates in the autumn) and the periods 2040-2069 and 2070-2099 for 5 GCMs x 2 forcing scenarios (RCP4.5 and RCP8.5) and 2 GCMs with RCP2.6. The Joint Research Centre’s (JRC) Agri4Cast gridded dataset was used for the baseline. The scenarios have been calculated using an enhanced delta change method that applies changes in aspects of temperature and precipitation variability in addition to changes in mean climate.

Methods: The structure of the single tables refers to the gridcode of the model, for example the gridcode 35120 fits the location 35_120. This means the gridcode column is 120, the row 35. The explanation can be found in the properties of the kml file or the associated grid file. The dataset was generated by applying a “delta change factor” based on GCM outputs for RCP scenarios corresponding to the periods: 2040 until 2069 and from 2070 until 2099. As the “delta change factor” for these periods was applied to baseline observations for the period of 1.1.1980 to 31.12.2010, the dates for the respective scenario periods are still 1.1.1980 to 31.12.2010, due to the fact they are scenarios (ie, what would climate be relative to baseline climate). Pragmatically this avoids issues with different alignment of leap years between baseline and scenario periods. This is standard practice in climate data. Users of the data can adjust the dates as needed for their application.

Series Information: Version 3 (DOI: https://www.doi.org/10.4228/zalf.vjcp-vep3; consisting of 9220 grid cells) replaces two earlier versions by updating the method to estimate vapour pressure and dew point temperature and by expanding the study region to include Switzerland, Lichtenstein, the Balkan region and Kaliningrad. The Two older versions of the dataset are available: Version 1 (DOI: https://www.doi.org/10.4228/ZALF.DK.59; consisting of 8709 grid cells) that was used by Webber et al. (2018), and an updated version 2 (DOI: https://www.doi.org/10.4228/ZALF.DK.94; consisting of 8748 grid cells) that corrected an error in version 1 when accounting for altitude differences to agricultural cultivation areas. It is recommended to use the latest version 3, unless the data of version 1 are intended to be used for direct comparison with the results of Webber et al. (2018), https://www.doi.org/10.1038/s41467-018-06525-2.

For more information see Quality/Lineage in the Metadata

Download compressed package:
https://web.bonares.de/smartEditor/rest/upload/3117/macsur_v3.zip
DOI: 10.34740/kaggle/dsv/6004200
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.8068881
title: Curated GWAS summary statistics on East Asian ancestry on 19 blood count traits and glycemic traits

description: Genome wide curated summary statistics on 19 blood count traits and glycemic traits File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS: https://gitlab.pasteur.fr/statistical-genetics/jass GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/32888493#study_panel). GWAS of glycemic traits come from the (18) study downloadable from GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/34059833). Full description of the method used to derive this dataset can be found in
DOI: 10.5281/zenodo.8068880
title: Curated GWAS summary statistics on East Asian ancestry on 19 blood count traits and glycemic traits

description: Genome wide curated summary statistics on 19 blood count traits and glycemic traits File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS: https://gitlab.pasteur.fr/statistical-genetics/jass GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/32888493#study_panel). GWAS of glycemic traits come from the (18) study downloadable from GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/34059833). Full description of the method used to derive this dataset can be found in
DOI: 10.5281/zenodo.8047409
title: Antidepressant effect of the ethanol leaf extract of Alafia barteri in stressed mice

description: The antidepressant effect of the ethanol leaf extract of <em>Alafia barteri </em>in mice was compared with a standard antidepressant drug and a non steroidal anti-inflammatory drug using behavioural assessment models: the forced swim test,tail suspension test and the open field test.
DOI: 10.5281/zenodo.8047408
title: Antidepressant effect of the ethanol leaf extract of Alafia barteri in stressed mice

description: The antidepressant effect of the ethanol leaf extract of <em>Alafia barteri </em>in mice was compared with a standard antidepressant drug and a non steroidal anti-inflammatory drug using behavioural assessment models: the forced swim test,tail suspension test and the open field test.
DOI: 10.5281/zenodo.7433979
title: Cytoscape files - Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation

description: Cytoscape files of pathway enrichment analyses and PPI mapping associated with manuscript https://www.nature.com/articles/s41467-023-39241-7 <strong>Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics</strong> <strong>identify cancer-specific mechanisms of stress adaptation </strong> Anna Rodina<sup>1,11</sup>, Chao Xu<sup>1,11</sup>, Chander S. Digwal<sup>1,11</sup>, Suhasini Joshi<sup>1,11</sup>, Anand R. Santhaseela<sup>1</sup>, Sadik Bay<sup>1</sup>, Swathi Merugu<sup>1</sup>, Aftab Alam<sup>1</sup>, Pengrong Yan<sup>1</sup>, Chenghua Yang<sup>1,12</sup>, Tanaya Roychowdhury<sup>1</sup>, Palak Panchal<sup>1</sup>, Liza Shrestha<sup>1</sup>, Yanlong Kang<sup>1</sup>, Sahil Sharma<sup>1</sup>, Yogita Patel<sup>2</sup>, Justina Almadovar<sup>1</sup>, Adriana Corben<sup>3,13</sup>, Mary Alpaugh<sup>1,14</sup>, Shanu Modi<sup>4</sup>, Monica L. Guzman<sup>5</sup>, Teng Fei<sup>6</sup>, Tony Taldone<sup>1</sup>, Stephen D. Ginsberg<sup>7,8</sup>, Hediye Erdjument-Bromage<sup>9</sup>, Thomas A. Neubert<sup>9</sup>, Katia Manova-Todorova<sup>10</sup>, Jason C. Young<sup>2</sup>,<strong> </strong>Meng-Fu Bryan Tsou<sup>10</sup><strong>, </strong>Tai Wang<sup>1,*</sup>, Gabriela Chiosis<sup>1,4,*</sup> <strong>Abstract </strong> Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of first-in-class chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.
DOI: 10.5281/zenodo.7433980
title: Cytoscape files - Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation

description: Cytoscape files of pathway enrichment analyses and PPI mapping associated with manuscript https://www.nature.com/articles/s41467-023-39241-7 <strong>Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics</strong> <strong>identify cancer-specific mechanisms of stress adaptation </strong> Anna Rodina<sup>1,11</sup>, Chao Xu<sup>1,11</sup>, Chander S. Digwal<sup>1,11</sup>, Suhasini Joshi<sup>1,11</sup>, Anand R. Santhaseela<sup>1</sup>, Sadik Bay<sup>1</sup>, Swathi Merugu<sup>1</sup>, Aftab Alam<sup>1</sup>, Pengrong Yan<sup>1</sup>, Chenghua Yang<sup>1,12</sup>, Tanaya Roychowdhury<sup>1</sup>, Palak Panchal<sup>1</sup>, Liza Shrestha<sup>1</sup>, Yanlong Kang<sup>1</sup>, Sahil Sharma<sup>1</sup>, Yogita Patel<sup>2</sup>, Justina Almadovar<sup>1</sup>, Adriana Corben<sup>3,13</sup>, Mary Alpaugh<sup>1,14</sup>, Shanu Modi<sup>4</sup>, Monica L. Guzman<sup>5</sup>, Teng Fei<sup>6</sup>, Tony Taldone<sup>1</sup>, Stephen D. Ginsberg<sup>7,8</sup>, Hediye Erdjument-Bromage<sup>9</sup>, Thomas A. Neubert<sup>9</sup>, Katia Manova-Todorova<sup>10</sup>, Jason C. Young<sup>2</sup>,<strong> </strong>Meng-Fu Bryan Tsou<sup>10</sup><strong>, </strong>Tai Wang<sup>1,*</sup>, Gabriela Chiosis<sup>1,4,*</sup> <strong>Abstract </strong> Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of first-in-class chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.
DOI: 10.5281/zenodo.8066899
title: calibration chamber for numerical tools

description: The data set represents eight centrifuge tests which have been conducted to produce data for validation of numerical tools. The tests contain plate loading tests with special focus on large deformation. The data set includes the factual report, geotechnical report, measurement data, a summary of the video data and laser scans of the top of the model. More details can be found in the readme.txt file.
DOI: 10.5281/zenodo.8066898
title: calibration chamber for numerical tools

description: The data set represents eight centrifuge tests which have been conducted to produce data for validation of numerical tools. The tests contain plate loading tests with special focus on large deformation. The data set includes the factual report, geotechnical report, measurement data, a summary of the video data and laser scans of the top of the model. More details can be found in the readme.txt file.
DOI: 10.34740/kaggle/dsv/5994867
title: Aging-associated diseases Dataset
DOI: 10.5281/zenodo.8074717
title: Database of calcareous nannofossil species and genus taxonomic etymologies

description: This is a database of the etymology for all Cenozoic and Mesozoic species and genera of calcareous nannofossils that appear on the nannotax.org website. The database was constructed to ascertain implicit biases that can be shown in how taxa were named. The main goal of the research was to show how pervasive biases (particularly gender and place biases) are in micropaleontology.
DOI: 10.5281/zenodo.8074716
title: Database of calcareous nannofossil species and genus taxonomic etymologies

description: This is a database of the etymology for all Cenozoic and Mesozoic species and genera of calcareous nannofossils that appear on the nannotax.org website. The database was constructed to ascertain implicit biases that can be shown in how taxa were named. The main goal of the research was to show how pervasive biases (particularly gender and place biases) are in micropaleontology.
DOI: 10.4121/e1d89cb5-6872-48fc-be63-aadd687ee6f9.v1
title: Modified Swiss Dwellings: a Machine Learning-ready Dataset for Floor Plan Auto-Completion at Scale

description: <em>Modified Swiss Dwellings</em>The Modified Swiss Dwellings (MSD) dataset is a <strong>machine learning-ready dataset for floor plan auto-completion at scale</strong>. The MSD dataset is derived from the Swiss Dwellings database (v3.0.0). The MSD dataset (train split) contains <strong>4167 floor plans</strong> of <strong>single- as well as multi-unit building complexes across Switzerland</strong>, hence extending the building scale w.r.t. of other well know floor plan datasets like the RPLAN dataset. Since the MSD dataset will be part of a challenge @ ICCV in Paris, 2023, October 3, the <strong>test split is not yet made public</strong>. This will be added after the submission deadline of the challenge, which will be around mid September 2023.<br><em>Cleaning, filtering, and processing</em>All cleaning, filtering, and processing is done in Python. The Swiss Dwellings database is cleaned and filtered on residential building complexes that have a minimum room count (&gt;10) and have at least 2 "Zone 2" rooms (<em>e.g.</em>, living room, corridor, kitchen, dining). A graph extraction algorithm fully based on the `shapely` and `networkx` libraries in Python was developed to extract the access graphs from the filtered floor plans.<br><em>Dataset structure</em>The MSD dataset contains 4 folders: `graph_in` [&lt;index&gt;.pickle], `struct_in` [&lt;index&gt;.npy], `full_out` [&lt;index&gt;.npy], and `graph_out` [&lt;index&gt;.pickle]. Naming is consistent across all folders, meaning that an instance from `graph_in` with name "&lt;index&gt;.pickle" is from the same floor plan as an instance from `full_out` with name "&lt;index&gt;.npy".<br><em>Floor plan auto-completion</em>The MSD dataset is developed with the goal for the computer science community to develop (deep learning) models for the task of floor plan auto-completion. The floor plan auto-completion task takes as input the boundary of a building, the structural elements necessary for the building’s structural integrity, and a set of user constraints formalized in a graph structure, with the goal of automatically generating the full floor plan. Specifically, the goal is to learn the correlation between the the joint distribution of `graph_in` and `struct_in` with that of `full_out`. `graph_out` is provided when researchers want to use / develop methods from graph signal processing, or graph machine learning specifically.<br><em>GIthub guidelines</em>Github page.
DOI: 10.11583/dtu.22182694
title: HPLC-DAD chromatrograms of oxidized oceanic DOM used for lignin phenol analysis

description: The dataset is part of a paper in Limnology & Oceanography: Methods with the following title: "Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data" https://doi.org/10.1002/lom3.10561 The dataset contains HPLC-DAD chromatograms and metadata of the samples. The dataset is designed to run with our novel 2nd derivative/PARAFAC2 algorithm created for the decomposition of HPLC-DAD chromtograms for quantification of lignin phenols. The algorithm (matlab code) can be found using this DOI: https://doi.org/10.11583/DTU.22178825 However, the dataset may also be used for other interest.
DOI: 10.5281/zenodo.8064141
title: A fresh thermodynamic outlook of hydrogen production by water splitting from an exergy-based perspective

description: The upload files is the origin data and calculation procedure for the article titled "A fresh thermodynamic outlook of hydrogen production by water splitting from an exergy-based perspective".
DOI: 10.5281/zenodo.8064140
title: A fresh thermodynamic outlook of hydrogen production by water splitting from an exergy-based perspective

description: The upload files is the origin data and calculation procedure for the article titled "A fresh thermodynamic outlook of hydrogen production by water splitting from an exergy-based perspective".
DOI: 10.34740/kaggle/dsv/6004502
title: Bigbasket Customer Analytics
DOI: 10.5517/ccdc.csd.cc208wgn
title: CCDC 1855581: Experimental Crystal Structure Determination

description: Related Article: Chun-Feng Wang, Jin Chuan Wu, Qingxin Li|2022|Inorg.Chem.Front.|9|3251|doi:10.1039/D2QI00507G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bbpzc
title: CCDC 2155243: Experimental Crystal Structure Determination

description: Related Article: Janusz Lipkowski, Anna Bielejewska, Oliver Presly|2022|Carbohydr.Res.|520|108601|doi:10.1016/j.carres.2022.108601
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bm2nq
title: CCDC 2163293: Experimental Crystal Structure Determination

description: Related Article: Xu Huang, Xiaotong Li, Yu Tao, Songhao Guo, Jiazhen Gu, Huilong Hong, Yige Yao, Yan Guan, Yunan Gao, Chen Li, Xujie Lü, Yongping Fu|2022|J.Am.Chem.Soc.|144|12247|doi:10.1021/jacs.2c03443
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f68gd
title: CCDC 2240353: Experimental Crystal Structure Determination

description: Related Article: Jayakrishnan Menon|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fkpd3
title: CCDC 2251325: Experimental Crystal Structure Determination

description: Related Article: Adimulam Harinath, Himadri Karmakar, Devadkar Ajitaro Kisan, Hari Pada Nayek, Tarun K. Panda|2023|Org.Biomol.Chem.|21|4237|doi:10.1039/D3OB00453H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fvlpl
title: CCDC 2259890: Experimental Crystal Structure Determination

description: Related Article: Pengpeng Hu, Hong Mo, Saijie Song, Jing Wu, Jihui Li, Jian Shen|2023|J.Mater.Chem.B|11|4799|doi:10.1039/D2TB02551E
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.21694706
title: 2023 DCA pancreatic adenocarcinoma

description: Primary dataset for the corresponding manuscript.
DOI: 10.6084/m9.figshare.21694706.v2
title: 2023 DCA pancreatic adenocarcinoma

description: Primary dataset for the corresponding manuscript.
DOI: 10.6084/m9.figshare.21694706.v1
title: DCA pancreatic adenocarcinoma

description: Primary dataset for the corresponding manuscript.