cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25sksb
title: CCDC 2019612: Experimental Crystal Structure Determination

description: Related Article: Katerina Fourtaka, Elias Christoforides, Pavlos Tzamalis, Kostas Bethanis|2021|J.Mol.Struct.|1234|130169|doi:10.1016/j.molstruc.2021.130169
DOI: 10.5517/ccdc.csd.cc25wfm4
title: CCDC 2022366: Experimental Crystal Structure Determination

description: Related Article: Katerina Fourtaka, Elias Christoforides, Pavlos Tzamalis, Kostas Bethanis|2021|J.Mol.Struct.|1234|130169|doi:10.1016/j.molstruc.2021.130169
DOI: 10.5517/ccdc.csd.cc26vpwn
title: CCDC 2051452: Experimental Crystal Structure Determination

description: Related Article: M. Rajkumar, K. Parameswaran, E. Suresh, A. Chandramohan|2021|J.Mol.Struct.|1234|130170|doi:10.1016/j.molstruc.2021.130170
DOI: 10.5517/ccdc.csd.cc25z2hr
title: CCDC 2024904: Experimental Crystal Structure Determination

description: Related Article: Zohreh Razmara, Maciej Kubicki|2021|J.Mol.Struct.|1234|130143|doi:10.1016/j.molstruc.2021.130143
DOI: 10.5517/ccdc.csd.cc26kx2s
title: CCDC 2042995: Experimental Crystal Structure Determination

description: Related Article: Cheng-Qiang Qiu, Le-Qian Li, Shu-Li Yao, Sui-Jun Liu, Hui Xu, Teng-Fei Zheng|2021|Polyhedron|199|115100|doi:10.1016/j.poly.2021.115100
DOI: 10.5281/zenodo.4600959
title: Guanine‐Stabilized Formamidinium Lead Iodide Perovskites

description: Data related to the publication: "Guanine‐Stabilized Formamidinium Lead Iodide Perovskites" containing: - projected densities of states - isosurfaces of HOMO and LUMO levels
DOI: 10.5281/zenodo.4600958
title: Guanine‐Stabilized Formamidinium Lead Iodide Perovskites

description: Data related to the publication: "Guanine‐Stabilized Formamidinium Lead Iodide Perovskites" containing: - projected densities of states - isosurfaces of HOMO and LUMO levels
DOI: 10.6084/m9.figshare.13491540
title: Anexo 1. Análisis del frame y el monomito de Greta Thunberg.docx

description: Greta Thunberg's frame and storytelling analysis.
DOI: 10.6084/m9.figshare.13491540.v1
title: Anexo 1. Análisis del frame y el monomito de Greta Thunberg.docx

description: Greta Thunberg's frame and storytelling analysis.
DOI: 10.21227/qhq1-gp87
title: Distribution Test Systems for Reliability Assessment

description: Distribution test systems with 24, 54, 86, and 136 nodes, which can be used for distribution system reliability evaluation.
DOI: 10.6084/m9.figshare.6815705.v6
title: JARVIS-DFT 2D dataset (jdft_2d.json)

description: Website: https://www.ctcms.nist.gov/~knc6/JVASP.html<br><br>Dataset for 2D materials<br>#########################################Lading the dataset:<br>import os,jsonimport pandas as pdf = open(os.path.join(os.getcwd(),'jdft_2d-4-26-2020.json'),'r')data2d=json.load(f)f.close()df=pd.DataFrame(data2d)<br>#########################################For more details about using the dataset, use the jupyter-notebooks:https://github.com/usnistgov/jarvis
DOI: 10.5281/zenodo.4603089
title: Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS

description: All the processed data and codes from the manuscript "Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS" are uploaded here. Please see the README for details.
DOI: 10.5281/zenodo.4603218
title: Dataset for Nutrient complexity triggers transitions between solitary and colonial growth in bacterial populations

description: Dataset for Nutrient complexity triggers transitions between solitary and colonial growth in bacterial populations Published in the ISME Journal. Dataset includes: 1. Analysed source data for figures 2. Raw images and segmentation data that were used to generate the analysis
DOI: 10.5281/zenodo.4603217
title: Dataset for Nutrient complexity triggers transitions between solitary and colonial growth in bacterial populations

description: Dataset for Nutrient complexity triggers transitions between solitary and colonial growth in bacterial populations Published in the ISME Journal. Dataset includes: 1. Analysed source data for figures 2. Raw images and segmentation data that were used to generate the analysis
DOI: 10.5519/qd.pna3s0r8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (96 records)
DOI: 10.6084/m9.figshare.6815699.v5
title: JARVIS-DFT 3D dataset (jdft_3d.json)

description: Website: https://www.ctcms.nist.gov/~knc6/JVASP.html<br><br>#########################################Lading the dataset:<br>import os, jsonimport pandas as pdimport numpy as npf = open(os.path.join(os.getcwd(),'jdft_3d-4-26-2020.json'),'r')data3d=json.load(f)f.close()df=pd.DataFrame(data3d) form_enp=np.array(df['formation_energy_peratom'].replace('na',np.nan).dropna().values,dtype='float')#########################################For more details about using the dataset, use the jupyter-notebooks:https://github.com/usnistgov/jarvis<br><br>The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
DOI: 10.6084/m9.figshare.14213393.v1
title: Temporo-cerebellar connectivity

description: Lesion maps used for lesion analysis with NPM (Mricron), Design file and behavioral data (SPSS file).
DOI: 10.34740/kaggle/dsv/2020249
title: Rainfall in Pakistan
DOI: 10.6084/m9.figshare.12743687.v5
title: Balancing selection maintains ancient polymorphisms at conserved enhancers for the olfactory receptor genes of a Chinese marine fish

description: These files consist of supplementary figs. S8-S12 and supplementary tables S8, S12, S19, S20, and S21.<br>
DOI: 10.17632/x44n47vgcg.1
title: Data for: Model identification for ARMA time series through convolutional neural networks

description: LUA Code for training, testing and benchmarking
DOI: 10.17632/3nrxw26p8z.1
title: Data for: The effect of sediment lithology on oxygen isotope composition and phase transformation of marine biogenic opal.

description: These are d18O measurements of silica phases (biogenic amorphous opal, opal-A', opal-CT, and microquartz chert) from a set of drilled sediments from Ocean Drilling Program (ODP) sites 1049-1053. These measurements were made after separation of the silica phases from bulk sediment, cleaning, and measurements with laser fluorination mass spectrometry.
DOI: 10.5281/zenodo.4603340
title: Ooh Na Na Alternative Identifiers

description: A dump of the alternative identifiers extracted from the OBO Foundry as a three column TSV - prefixes, primary identifiers, and alt identifiers. Generated with the following code: <pre><code class="language-bash">pip install pyobo pyobo database alts</code></pre>
DOI: 10.5281/zenodo.4603343
title: Ooh Na Na Synonyms

description: This is a gzipped three-column TSV file that has prefixes, identifiers, and synonyms for lots of biomedical entities drawn from the terminologies and ontologies in PyOBO. It was generated with the following code in the shell: <pre><code class="language-bash">pip install pyobo pyobo database synonyms</code></pre> <pre>Any silly name suggestions that include literary references are welcome :) </pre>
DOI: 10.13012/b2idb-3074705_v1
title: Farmers’ Perceptions of Marginal Land for Biofuel Crops

description: The dataset includes the survey results about farmers’ perceptions of marginal land availability and the likelihood of a land pixel being marginal based on a machine learning model trained from the survey. Two spreadsheet files are the farmer and farm characteristics (marginal_land_survey_data_shared.xlsx), and the existing land use of marginal lands (land_use_info_sharing.xlsx). <b>Note:</b> the blank cells in these two spreadsheets mean missing values in the survey response. The GeoTiff file includes two bands, one the marginal land likelihood in the Midwestern states (0-1), the other the dominant reason of land marginality (0-5; 0 for farm size, 1 for growing season precipitation, 2 for root zone soil water capacity, 3 for average slope, 4 for growing season mean temperature, and 5 for growing season diurnal range of temperature). To read the data, please use a GIS software such as ArcGIS or QGIS.
DOI: 10.5281/zenodo.4603348
title: Robot handle grasping failure data

description: <strong>Short summary</strong> Accompanying dataset for our paper <code>A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.</code> <strong>Contents</strong> The dataset includes a single zip archive, containing data from the experiment described in the paper (conducted with a Toyota HSR). The zip archive contains three subdirectories: <em>handle_grasping_failure_database</em>: A dump of a MongoDB database containing data from the handle grasping experiment, including ground-truth grasping failure annotations <em>pre_arm_motion_images</em>: Images collected from the robot's hand camera before moving the robot's hand towards the handle <em>pregrasp_images</em>: Images collected from the robot's hand camera just before closing the gripper for grasping The image names include the time stamp at which the images were taken; this allows matching each image with the execution data in the database. <strong>Database usage</strong> After unzipping the archive, the database can be restored with the command mongorestore handle_grasping_failure_database This will create a MongoDB database with the name <em>drawer_handle_grasping_failures</em>. Code for processing the data and failure analysis can be found in our GitHub repository.
DOI: 10.5281/zenodo.4603347
title: Robot handle grasping failure data

description: <strong>Short summary</strong> Accompanying dataset for our paper <code>A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.</code> <strong>Contents</strong> The dataset includes a single zip archive, containing data from the experiment described in the paper (conducted with a Toyota HSR). The zip archive contains three subdirectories: <em>handle_grasping_failure_database</em>: A dump of a MongoDB database containing data from the handle grasping experiment, including ground-truth grasping failure annotations <em>pre_arm_motion_images</em>: Images collected from the robot's hand camera before moving the robot's hand towards the handle <em>pregrasp_images</em>: Images collected from the robot's hand camera just before closing the gripper for grasping The image names include the time stamp at which the images were taken; this allows matching each image with the execution data in the database. <strong>Database usage</strong> After unzipping the archive, the database can be restored with the command mongorestore handle_grasping_failure_database This will create a MongoDB database with the name <em>drawer_handle_grasping_failures</em>. Code for processing the data and failure analysis can be found in our GitHub repository.
DOI: 10.6084/m9.figshare.14213486
title: Multilocus genotypes of Fremont cottonwood

description: Microsatellite genotypes of Fremont cottonwoods in the Verde River area of Arizona.
DOI: 10.5281/zenodo.4603363
title: Inspector Javert's Xref Database

description: It would have been really useful for Inspector Javert to find Jean Valjean if he had the cross-references between the identifies of all of the people he met in Montreuil-sur-Mer to the prisoner database (all he needed was 24601!). Since this dataset has a long name, you can feel free to abbreviate it as IJXD. We have the same problem in bioinformatics, so this is a database of cross-references extracted from OBO Foundry and other sources by PyOBO. It is a gzipped five-column TSV file that has source namespace, source identifier, target namespace, target identifier, and provenance. Each has been normalized so cross-references from different sources can be integrated and traversed. It was generated with the following code in the shell: <pre>pip install pyobo pyobo obo xrefs</pre> More information on this blog post: https://cthoyt.com/2020/04/19/inspector-javerts-xref-database.html.
DOI: 10.6084/m9.figshare.14213498
title: ANEXO 1.docx

description: Código desarrollado en R para el proceso ETL
DOI: 10.6084/m9.figshare.14213498.v1
title: ANEXO 1.docx

description: Código desarrollado en R para el proceso ETL
DOI: 10.5281/zenodo.4593916
title: Accompanying dataset for: "A Simple Machine Learning Framework for Citation Screening of Aging and Longevity Research Studies"

description: Dataset related to "Dasatinib and Quercetin Senolytic Therapy Risk-Benefit Analysis" published by Forever Healthy Foundation.
description: MySQL database dump of longevity_research database including dasatinib_and_quercetin_senolytic_therapy table
DOI: 10.5281/zenodo.4593915
title: Accompanying dataset for: "A Simple Machine Learning Framework for Citation Screening of Aging and Longevity Research Studies"

description: Dataset related to "Dasatinib and Quercetin Senolytic Therapy Risk-Benefit Analysis" published by Forever Healthy Foundation.
description: MySQL database dump of longevity_research database including dasatinib_and_quercetin_senolytic_therapy table
DOI: 10.6084/m9.figshare.14213501
title: Métricas de evaluación de los modelos

description: Métricas de evaluación de los modelos
DOI: 10.5281/zenodo.3968984
title: Robot action execution model learning data

description: <strong>Short summary</strong> This dataset accompanies our paper <code>A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Representation and Experience-Based Learning of Explainable Models for Robot Action Execution," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.</code> <strong>Contents</strong> There are three zip archives included, each of them a dump of a MongoDB database corresponding to one of the three experiments in the paper: Grasping a drawer handle (<em>handle_drawer_logs.zip</em>) Grasping a fridge handle (<em>handle_fridge_logs.zip</em>) Pulling an object (<em>pull_logs.zip</em>) All three experiments were performed with a Toyota HSR. Only the data necessary for learning the models used in our experiments are included here. <strong>Usage</strong> After unzipping the archives, each database can be restored with the command mongorestore [directory_name] This will create a MongoDB database with the name of the directory (<em>handle_drawer_logs</em>, <em>handle_fridge_logs</em>, and <em>pull_logs</em>). Code for processing the data and model learning can be found in our GitHub repository.
DOI: 10.5281/zenodo.3968983
title: Robot action execution model learning data

description: <strong>Short summary</strong> This dataset accompanies our paper <code>A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Representation and Experience-Based Learning of Explainable Models for Robot Action Execution," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.</code> <strong>Contents</strong> There are three zip archives included, each of them a dump of a MongoDB database corresponding to one of the three experiments in the paper: Grasping a drawer handle (<em>handle_drawer_logs.zip</em>) Grasping a fridge handle (<em>handle_fridge_logs.zip</em>) Pulling an object (<em>pull_logs.zip</em>) All three experiments were performed with a Toyota HSR. Only the data necessary for learning the models used in our experiments are included here. <strong>Usage</strong> After unzipping the archives, each database can be restored with the command mongorestore [directory_name] This will create a MongoDB database with the name of the directory (<em>handle_drawer_logs</em>, <em>handle_fridge_logs</em>, and <em>pull_logs</em>). Code for processing the data and model learning can be found in our GitHub repository.
DOI: 10.6084/m9.figshare.14213507.v1
title: Estructura del Logistic Model Tree

description: Logistic Model Tree
DOI: 10.6084/m9.figshare.14213507
title: Estructura del Logistic Model Tree

description: Logistic Model Tree
DOI: 10.5281/zenodo.4603376
title: Ooh Na Na

description: This is a gzipped three-column TSV file that has 138,749,325 prefixes, identifiers, and names for lots of biomedical entities, drawing from the OBO Foundry, ontologies in the Ontology Lookup Service, and many other nomenclature consortia that just haven't made it to the prime-time of standardized goodness. Ultimately, this dataset helps answer the question: what's my name? It's really a lot of work to get this stuff, so I tried to make it easy. It was generated with the following code in the shell: <pre>pip install pyobo obo database names</pre> More information on how and why this resource was made is available at https://cthoyt.com/2020/04/18/ooh-na-na.html.
description: The 1.2.0 version includes the addition of PubChem, ChEBML, and NPASS
DOI: 10.6084/m9.figshare.14213513.v1
title: Código en R y gráfica del árbol generado por el algoritmo rpart

description: Código en R y gráfica del árbol generado por el algoritmo rpart
DOI: 10.6084/m9.figshare.14213513
title: Código en R y gráfica del árbol generado por el algoritmo rpart

description: Código en R y gráfica del árbol generado por el algoritmo rpart
DOI: 10.6084/m9.figshare.14213495
title: Isolation and selection of microalgal species, native to Zimbabwe, with potential use in biodiesel production.

description: The data deposited herein shows the triplicate measurements of the growth and lipid yield capabilities of microalgal strains isolated from Manyame River, Zimbabwe.
DOI: 10.17632/j7yrp753y7.1
title: Data for: Better to be indirect? Testing the accuracy and cost-savings of indirect surveys

description: This is the replication data from the paper "Better to be Indirect?..." by Jennifer Alix-Garcia, Katharine R.E. Sims, and Laura Costica. Further description of the sample and data is provided within the paper. Furthermore, a larger version of this dataset is available in the World Bank MicrodataBank.
DOI: 10.5281/zenodo.4603393
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.
DOI: 10.6084/m9.figshare.14213543
title: Ribosomal protein database of Serratia marcescens strain SMO3

description: This work presents the ribosomal protein database of <i>Serratia marcescens </i>strain SMO3. Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.6084/m9.figshare.14213543.v1
title: Ribosomal protein database of Serratia marcescens strain SMO3

description: This work presents the ribosomal protein database of <i>Serratia marcescens </i>strain SMO3. Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.5517/ccdc.csd.cc1mnmyv
title: CCDC 1509388: Experimental Crystal Structure Determination

description: Related Article: Milene Lopes Silva, Róbson Ricardo Teixeira, Fabrício Marques Oliveira, Luciano Moura Guimarães, Felipe Terra Martins|2021|J.Heterocycl.Chem.|58|777|doi:10.1002/jhet.4214
DOI: 10.5517/ccdc.csd.cc1mnmzw
title: CCDC 1509389: Experimental Crystal Structure Determination

description: Related Article: Milene Lopes Silva, Róbson Ricardo Teixeira, Fabrício Marques Oliveira, Luciano Moura Guimarães, Felipe Terra Martins|2021|J.Heterocycl.Chem.|58|777|doi:10.1002/jhet.4214
DOI: 10.5517/ccdc.csd.cc21nfhn
title: CCDC 1896471: Experimental Crystal Structure Determination

description: Related Article: Vasanthi Palanisamy, Palash Sanphui, Vaskuri G. S. Sainaga Jyothi, Nalini R Shastri, Geetha Bolla, Kandhan Palanisamy, Muthuramalingam Prakash, Venu R. Vangala|2021|Cryst.Growth Des.|21|1548|doi:10.1021/acs.cgd.0c01344
DOI: 10.5517/ccdc.csd.cc21nfjp
title: CCDC 1896472: Experimental Crystal Structure Determination

description: Related Article: Vasanthi Palanisamy, Palash Sanphui, Vaskuri G. S. Sainaga Jyothi, Nalini R Shastri, Geetha Bolla, Kandhan Palanisamy, Muthuramalingam Prakash, Venu R. Vangala|2021|Cryst.Growth Des.|21|1548|doi:10.1021/acs.cgd.0c01344
DOI: 10.5517/ccdc.csd.cc21nflr
title: CCDC 1896474: Experimental Crystal Structure Determination

description: Related Article: Vasanthi Palanisamy, Palash Sanphui, Vaskuri G. S. Sainaga Jyothi, Nalini R Shastri, Geetha Bolla, Kandhan Palanisamy, Muthuramalingam Prakash, Venu R. Vangala|2021|Cryst.Growth Des.|21|1548|doi:10.1021/acs.cgd.0c01344
DOI: 10.5517/ccdc.csd.cc21nfms
title: CCDC 1896475: Experimental Crystal Structure Determination

description: Related Article: Vasanthi Palanisamy, Palash Sanphui, Vaskuri G. S. Sainaga Jyothi, Nalini R Shastri, Geetha Bolla, Kandhan Palanisamy, Muthuramalingam Prakash, Venu R. Vangala|2021|Cryst.Growth Des.|21|1548|doi:10.1021/acs.cgd.0c01344
DOI: 10.5517/ccdc.csd.cc21nfnt
title: CCDC 1896476: Experimental Crystal Structure Determination

description: Related Article: Vasanthi Palanisamy, Palash Sanphui, Vaskuri G. S. Sainaga Jyothi, Nalini R Shastri, Geetha Bolla, Kandhan Palanisamy, Muthuramalingam Prakash, Venu R. Vangala|2021|Cryst.Growth Des.|21|1548|doi:10.1021/acs.cgd.0c01344
DOI: 10.5517/ccdc.csd.cc258yfv
title: CCDC 2004597: Experimental Crystal Structure Determination

description: Related Article: Jules Moutet, Jules Schleinitz, Léo La Droitte, Maxime Tricoire, Fabrice Pointillart, Frédéric Gendron, Thomas Simler, Carine Clavaguéra, Boris Le Guennic, Olivier Cador, Grégory Nocton|2021|Angew.Chem.,Int.Ed.|60|6042|doi:10.1002/anie.202015428
DOI: 10.5517/ccdc.csd.cc258ygw
title: CCDC 2004598: Experimental Crystal Structure Determination

description: Related Article: Jules Moutet, Jules Schleinitz, Léo La Droitte, Maxime Tricoire, Fabrice Pointillart, Frédéric Gendron, Thomas Simler, Carine Clavaguéra, Boris Le Guennic, Olivier Cador, Grégory Nocton|2021|Angew.Chem.,Int.Ed.|60|6042|doi:10.1002/anie.202015428
DOI: 10.5517/ccdc.csd.cc258yhx
title: CCDC 2004599: Experimental Crystal Structure Determination

description: Related Article: Jules Moutet, Jules Schleinitz, Léo La Droitte, Maxime Tricoire, Fabrice Pointillart, Frédéric Gendron, Thomas Simler, Carine Clavaguéra, Boris Le Guennic, Olivier Cador, Grégory Nocton|2021|Angew.Chem.,Int.Ed.|60|6042|doi:10.1002/anie.202015428