cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/mtze-te13
title: Platform Harvest S-band January 2013

description: Progress toward the use of S-band SoOp in sea surface remote sensing was demonstrated in a 2012-2013 experiment based at the Harvest Oil Platform located at 34.469° N and 120.682° W, roughly 11 km from Point Conception, Santa Barbara, CA. Satellite transmissions from the XM-radio service were observed, using one channel each from the “Rhythm” (located above 85°W) and the “Blues” (115°W) satellites. Each downlink channel had a bandwidth of 1.886 MHz with a symbol rate of 1.64 Msps in Quadrature Phase Shift Key (QPSK) modulation. Two S-band antennas were deployed at approximately 27 m over the ocean surface. A LHCP antenna (Terk XM6) was pointed towards the satellite to receive the direct signal, while a RHCP antenna (Antcom 3M23R-A-XT2) was oriented towards the sea surface to receive the reflected signals. Following low noise amplifiers for both the direct and reflected antennas, a Universal Software Radio Peripheral 1 (USRP1) was then used to digitize, down-convert, and sample the measurements at an 4 MHz I/Q sample rate using 8-bit complex quantization. S-band data was recorded in two-minute blocks every two hours between September 25, 2012 and May 23, 2013. This dataset is the complex cross- and auto- correlations for the separated XM3 and XM4 channels, processed at a 4-ms coherent sampling time.
DOI: 10.48366/r603719
title: Analysis of the main characteristics of an educational project peculiarities

description: What are the significant features of international educational projects.
DOI: 10.6084/m9.figshare.23266643
title: <strong>Pisos bioclimáticos para la Cuenca Mediterránea como capas SIG de acceso abierto</strong>

description: Conjunto de datos asociado al artículo "<strong>Pisos bioclimáticos para la Cuenca Mediterránea como capas SIG de acceso abierto"</strong>
DOI: 10.34740/kaggle/dsv/6227095
title: Balochi Language to English Translation Dataset
DOI: 10.3886/e143182v1-140552
title: Data and Code for: The Effect of Immigration Restrictions on Local Labor Markets: Lessons from the 1920s Border Closure

description: <pre>In the 1920s, the United States substantially reduced immigration by imposing country-specic entry quotas. We compare local labor markets differentially exposed to the quotas due to variation in the national-origin mix of their immigrant population. US-born work- ers in areas losing immigrants did not benefit relative to workers in less exposed areas. Instead, in urban areas, European immi- grants were replaced with internal migrants and immigrants from Mexico and Canada. By contrast, farmers shifted toward capital- intensive agriculture and the immigrant-intensive mining industry contracted. These differences highlight the uneven effects of the quota system at the local level.<br></pre>
description: Continental United States. Smallest Geographic Unit: State Economic Area (SEA)
DOI: 10.5281/zenodo.8200711
title: depressigyra_globulus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8200710
title: depressigyra_globulus_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23805156
title: Data_Carnicer-Lombarte-et-al-2023_3

description: Analysed AFM, immunostain (cell culture and nerve tissue) data. Nerves consist of rat sciatic nerves, either healthy (naive) or injured by crush or transection injury. Cell cultures consist of primary Schwann cells derived from rat nerves cultured on substrates of a range of stiffnesses. Dataset contains:Analysis_all_AFM_data.xlsx : Nerve tissue stiffness values a characterised by AFM.Analysis_IHC_tissue.xlsx : Stain intensity characterisation of nerve tissue, immunostained for ECM markers (collagen I and fibronectin) and cell density (nuclear stain).Analysis_all_cell_culture.xlsx : Characterisation of immunostains of Schwann cells on cultured on substrates of different stiffnesses (0.1, 1, 10 or 50 kPa polyacrylamide substrates).
DOI: 10.6084/m9.figshare.23805156.v1
title: Data_Carnicer-Lombarte-et-al-2023_3

description: Analysed AFM, immunostain (cell culture and nerve tissue) data. Nerves consist of rat sciatic nerves, either healthy (naive) or injured by crush or transection injury. Cell cultures consist of primary Schwann cells derived from rat nerves cultured on substrates of a range of stiffnesses. Dataset contains:Analysis_all_AFM_data.xlsx : Nerve tissue stiffness values a characterised by AFM.Analysis_IHC_tissue.xlsx : Stain intensity characterisation of nerve tissue, immunostained for ECM markers (collagen I and fibronectin) and cell density (nuclear stain).Analysis_all_cell_culture.xlsx : Characterisation of immunostains of Schwann cells on cultured on substrates of different stiffnesses (0.1, 1, 10 or 50 kPa polyacrylamide substrates).
DOI: 10.5281/zenodo.8200719
title: depressigyra_globulus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8200718
title: depressigyra_globulus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8200678
title: OpenAlex Author Name Disambiguation V3 Data - Disambiguation Model

description: 5 Separate files used in the OpenAlex (https://openalex.org) V3 Author Name Disambiguation Model Creation: ORCID_hard_negative_pairs: Pairs of ORCIDs where either the full name, family name, or given name are a match and would therefore be more difficult to disambiguate. Disambiguator_all_possible_training_data: Dataset created which contains all possible features for modeling and all possible samples of data. Eventually, this was split into train/val/test and also processed more to create a better balance of positive to negative samples for our purposes. Disambiguator_final_train_data: Final data which the disambiguator was trained on. Disambiguator_final_val_data: Data which was used to test the model during training to optimize the features/hyperparameters chosen. Disambiguator_final_test_data: Final dataset which gave model performance indication after all hyperparameters were tuned and features were chosen. More details can be found at https://github.com/ourresearch/openalex-name-disambiguation
DOI: 10.5281/zenodo.8200679
title: OpenAlex Author Name Disambiguation V3 Data - Disambiguation Model

description: 5 Separate files used in the OpenAlex (https://openalex.org) V3 Author Name Disambiguation Model Creation: ORCID_hard_negative_pairs: Pairs of ORCIDs where either the full name, family name, or given name are a match and would therefore be more difficult to disambiguate. Disambiguator_all_possible_training_data: Dataset created which contains all possible features for modeling and all possible samples of data. Eventually, this was split into train/val/test and also processed more to create a better balance of positive to negative samples for our purposes. Disambiguator_final_train_data: Final data which the disambiguator was trained on. Disambiguator_final_val_data: Data which was used to test the model during training to optimize the features/hyperparameters chosen. Disambiguator_final_test_data: Final dataset which gave model performance indication after all hyperparameters were tuned and features were chosen. More details can be found at https://github.com/ourresearch/openalex-name-disambiguation
DOI: 10.5281/zenodo.8200741
title: panulirus_cygnus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8200740
title: panulirus_cygnus_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23805855
title: Structurally Similar Formats are Not Functionally Equivalent across Languages: Requests for Reconfirmation in Comparative Perspective: Supplementary R code documentation

description: This supplemental material, based on Cramér’s V, shows how the descriptive statistics in this journal article are reported in the form of relative frequencies of variables and statistical association measurements.<br>
DOI: 10.6084/m9.figshare.23805855.v1
title: Structurally Similar Formats are Not Functionally Equivalent across Languages: Requests for Reconfirmation in Comparative Perspective: Supplementary R code documentation

description: This supplemental material, based on Cramér’s V, shows how the descriptive statistics in this journal article are reported in the form of relative frequencies of variables and statistical association measurements.<br>
DOI: 10.5281/zenodo.8200601
title: Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions

description: This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions." The dataset is composed of the following files: * pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space * s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above * view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view" * s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue. * s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above. * viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg" * 0readme -- brief instructions on how to view the trajectories * colors.vmd -- utility script for VMD * covmacros.vmd -- VMD script to define coronavirus spike subdomains * fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles * diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations * pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br>
DOI: 10.5281/zenodo.8200600
title: Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions

description: This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions." The dataset is composed of the following files: * pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space * s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above * view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view" * s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue. * s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above. * viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg" * 0readme -- brief instructions on how to view the trajectories * colors.vmd -- utility script for VMD * covmacros.vmd -- VMD script to define coronavirus spike subdomains * fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles * diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations * pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br>
DOI: 10.5281/zenodo.7734367
title: DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

description: Datasets associated with https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009797
description: {"references": ["https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009797"]}
DOI: 10.5281/zenodo.7734368
title: DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

description: Datasets associated with https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009797
description: {"references": ["https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009797"]}
DOI: 10.5281/zenodo.8200663
title: Soil physical quality of a tropical sandy Ferralsol

description: Dados de pesquisa da dissertação de mestrado do primeiro autor, vinculado à Universidade Estadual de Maringá, Maringá, BR.
DOI: 10.5281/zenodo.8200662
title: Soil physical quality of a tropical sandy Ferralsol

description: Dados de pesquisa da dissertação de mestrado do primeiro autor, vinculado à Universidade Estadual de Maringá, Maringá, BR.
DOI: 10.5281/zenodo.8196121
title: Análise_Qualidade_da_Água_Baía_de_Guanabara_2018_2022

description: <strong>Título:</strong> Dados de Qualidade da Água e Sensoriamento Remoto da Baía de Guanabara (2018-2022) <strong>Descrição:</strong> Este conjunto de dados contém informações coletadas e geradas durante um estudo sobre a qualidade da água na Baía de Guanabara, Brasil, realizado de 2010 a 2022. O estudo utilizou uma abordagem integrada, combinando sensoriamento remoto, dados climáticos, dados hidrológicos e técnicas de Inteligência Artificial (IA) para entender as características hidrodinâmicas dos corpos d'água na Baía de Guanabara e seus efeitos na qualidade da água e nos processos biológicos e químicos. Os dados incluem: <strong>Dados de Sensoriamento Remoto:</strong> Imagens de satélite coletadas de 2018 a 2022, incluindo parâmetros de qualidade da água como temperatura da água, clorofila-a e turbidez. <strong>Dados Climáticos:</strong> Dados climáticos de 2018 a 2022, incluindo temperatura mínima do ar (tmin), temperatura máxima do ar (tmax) e precipitação (prec), coletados do Instituto Nacional de Meteorologia (INMET). <strong>Dados Hidrológicos:</strong> Dados brutos de 2018 a 2022, incluindo informações sobre vazão, nível de água e velocidade do fluxo, coletados do Instituto Estadual do Ambiente (INEA). Estes dados são disponibilizados para uso público com o objetivo de promover a pesquisa adicional e a validação dos resultados do estudo. Eles podem ser úteis para pesquisadores interessados em qualidade da água, sensoriamento remoto, mudanças climáticas, conservação de ecossistemas aquáticos e aplicações de Inteligência Artificial em ciências ambientais.
DOI: 10.5281/zenodo.8196122
title: Análise_Qualidade_da_Água_Baía_de_Guanabara_2018_2022

description: <strong>Título:</strong> Dados de Qualidade da Água e Sensoriamento Remoto da Baía de Guanabara (2018-2022) <strong>Descrição:</strong> Este conjunto de dados contém informações coletadas e geradas durante um estudo sobre a qualidade da água na Baía de Guanabara, Brasil, realizado de 2010 a 2022. O estudo utilizou uma abordagem integrada, combinando sensoriamento remoto, dados climáticos, dados hidrológicos e técnicas de Inteligência Artificial (IA) para entender as características hidrodinâmicas dos corpos d'água na Baía de Guanabara e seus efeitos na qualidade da água e nos processos biológicos e químicos. Os dados incluem: <strong>Dados de Sensoriamento Remoto:</strong> Imagens de satélite coletadas de 2018 a 2022, incluindo parâmetros de qualidade da água como temperatura da água, clorofila-a e turbidez. <strong>Dados Climáticos:</strong> Dados climáticos de 2018 a 2022, incluindo temperatura mínima do ar (tmin), temperatura máxima do ar (tmax) e precipitação (prec), coletados do Instituto Nacional de Meteorologia (INMET). <strong>Dados Hidrológicos:</strong> Dados brutos de 2018 a 2022, incluindo informações sobre vazão, nível de água e velocidade do fluxo, coletados do Instituto Estadual do Ambiente (INEA). Estes dados são disponibilizados para uso público com o objetivo de promover a pesquisa adicional e a validação dos resultados do estudo. Eles podem ser úteis para pesquisadores interessados em qualidade da água, sensoriamento remoto, mudanças climáticas, conservação de ecossistemas aquáticos e aplicações de Inteligência Artificial em ciências ambientais.
DOI: 10.5281/zenodo.8200794
title: Análise_Qualidade_da_Água_Baía_de_Guanabara_2018_2022

description: <strong>Título:</strong> Dados de Qualidade da Água e Sensoriamento Remoto da Baía de Guanabara (2018-2022) <strong>Descrição:</strong> Este conjunto de dados contém informações coletadas e geradas durante um estudo sobre a qualidade da água na Baía de Guanabara, Brasil, realizado de 2010 a 2022. O estudo utilizou uma abordagem integrada, combinando sensoriamento remoto, dados climáticos, dados hidrológicos e técnicas de Inteligência Artificial (IA) para entender as características hidrodinâmicas dos corpos d'água na Baía de Guanabara e seus efeitos na qualidade da água e nos processos biológicos e químicos. Os dados incluem: <strong>Dados de Sensoriamento Remoto:</strong> Imagens de satélite coletadas de 2018 a 2022, incluindo parâmetros de qualidade da água como temperatura da água, clorofila-a e turbidez. <strong>Dados Climáticos:</strong> Dados climáticos de 2018 a 2022, incluindo temperatura mínima do ar (tmin), temperatura máxima do ar (tmax) e precipitação (prec), coletados do Instituto Nacional de Meteorologia (INMET). <strong>Dados Hidrológicos:</strong> Dados brutos de 2018 a 2022, incluindo informações sobre vazão, nível de água e velocidade do fluxo, coletados do Instituto Estadual do Ambiente (INEA). Estes dados são disponibilizados para uso público com o objetivo de promover a pesquisa adicional e a validação dos resultados do estudo. Eles podem ser úteis para pesquisadores interessados em qualidade da água, sensoriamento remoto, mudanças climáticas, conservação de ecossistemas aquáticos e aplicações de Inteligência Artificial em ciências ambientais.
DOI: 10.22003/xfel.eu-data-900323-00
title: DSSC Commissioning
DOI: 10.22003/xfel.eu-data-900332-00
title: SQS commissioning
DOI: 10.5281/zenodo.7701637
title: Stressed economies respond more strongly to climate extremes - Data and Code Supplement

description: This repository provides data and code to reproduce the results of the publication "R. Middelanis, S. N. Willner, K. Kuhla, L. Quante, C. Otto, and A. Levermann (2023). Stressed economies respond more strongly to climate extremes. Environmental Research Letters." <strong>dependencies:</strong> a working environment is provided in environment.yml the Acclimate post-processing package can be downloaded from the respective GitHub repostory with <code>git@github.com:acclimate/post-processing.git</code>. Switch to the develop branch with <code>git checkout develop</code> and install the package with <code>conda develop .</code> from within the repository <strong>data:</strong> See <code>./data/README.md</code> for the required data and sources for those data that are not included in this repository. <strong>Steps to reproduce the results:</strong> 1. Generate Acclimate input data Direct loss time series are obtained from "Kuhla et al. (2021). Ripple resonance amplifies economic welfare loss from weather extremes. <em>Environmental Reserach Letters</em>". Acclimate input data (cf. <code>./data/README.md</code>) are generated with <code>./code/forcing.py</code> The input data used in the pubilcation are available at <code>./data/acclimate_input</code> 2. Run Acclimate the Acclimate model can be downloaded from the respective GitHub repository at https://github.com/acclimate/acclimate 3. Run the analyses Acclimate output files of the calibration runs and the scenario runs are aggregated with functions <code>aggregate_calibration_ensemble </code>and <code>aggregate_ensembles</code> in <code>./code/utils.py</code>, respectively. Aggregated ready-to-use output data is located in <code>./data/acclimate_output</code> All figures can be reproduced with <code>./code/plotting.py</code>
DOI: 10.5281/zenodo.7701636
title: Stressed economies respond more strongly to climate extremes - Data and Code Supplement

description: This repository provides data and code to reproduce the results of the publication "R. Middelanis, S. N. Willner, K. Kuhla, L. Quante, C. Otto, and A. Levermann (2023). Stressed economies respond more strongly to climate extremes. Environmental Research Letters." <strong>dependencies:</strong> a working environment is provided in environment.yml the Acclimate post-processing package can be downloaded from the respective GitHub repostory with <code>git@github.com:acclimate/post-processing.git</code>. Switch to the develop branch with <code>git checkout develop</code> and install the package with <code>conda develop .</code> from within the repository <strong>data:</strong> See <code>./data/README.md</code> for the required data and sources for those data that are not included in this repository. <strong>Steps to reproduce the results:</strong> 1. Generate Acclimate input data Direct loss time series are obtained from "Kuhla et al. (2021). Ripple resonance amplifies economic welfare loss from weather extremes. <em>Environmental Reserach Letters</em>". Acclimate input data (cf. <code>./data/README.md</code>) are generated with <code>./code/forcing.py</code> The input data used in the pubilcation are available at <code>./data/acclimate_input</code> 2. Run Acclimate the Acclimate model can be downloaded from the respective GitHub repository at https://github.com/acclimate/acclimate 3. Run the analyses Acclimate output files of the calibration runs and the scenario runs are aggregated with functions <code>aggregate_calibration_ensemble </code>and <code>aggregate_ensembles</code> in <code>./code/utils.py</code>, respectively. Aggregated ready-to-use output data is located in <code>./data/acclimate_output</code> All figures can be reproduced with <code>./code/plotting.py</code>
DOI: 10.17909/t9-w6tj-wp63
title: Beyond Ultra-deep Frontier Fields and Legacy Observations ("BUFFALO")

description: The Beyond Ultra-deep Frontier Fields and Legacy Observations program (BUFFALO, Steinhardt et al. 2020, HST Program ID 15117) is a 101-orbit Cycle 25 HST program of prime ACS/WFC + parallel WFC3/IR imaging around all 6 of the clusters in the Hubble Frontier Fields program (HFF, Lotz et al. 2017). The BUFFALO program significantly expands the area imaged around each HFF cluster by a factor of about 3-4 times, in a total of 5 filters (ACS/WFC F606W and F814W, and WFC3/IR F105W, F125W, and F160W). This additional area had not previously been extensively observed by HST but was already covered by deep multi-wavelength data sets, including Spitzer and Chandra. As with the original HFF program, BUFFALO is designed to take advantage of gravitational lensing from massive clusters to simultaneously find high-redshift galaxies that would otherwise lie below HST detection limits and model foreground clusters to study the properties of dark matter and galaxy assembly. The expanded area is providing the first opportunity to study both cosmic variance at high redshift and galaxy assembly in the outskirts of the large HFF clusters.
DOI: 10.6084/m9.figshare.21989720.v1
title: Ordosarchaeia illuminate the evolutionary complexity from methanogen to haloarchaea

description: These are the raw data and scripts for the manuscript <strong>Ordosarchaeia illuminate the evolutionary complexity from methanogen to haloarchaea</strong>.