cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25390/caryinstitute.11771583.v3
title: Cary Environmental Monitoring Program Air Chemistry Data: 1988-2020

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, water pollution and streamwater hydrology. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, the Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: The following is a standard citation for referencing data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program:<br>Cary Institute of Ecosystem Studies, Environmental Monitoring Program. 2019 (or current year). Cary Institute of Ecosystem Studies, Box AB, Millbrook, NY 12545, www.caryinstitute.org.<br>Those wishing to publish data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact Data Manager Vicky Kelly, kellyv@caryinstitute.org.
DOI: 10.5281/zenodo.6394952
title: Accipiter hiogaster decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6394951
title: Accipiter hiogaster decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5145746
title: Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication

description: The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below. <code>year</code> = year of publication (YYYY) <code>date</code> = date (YYYY-MM-DD) of publication <code>title</code> = full paper title from journal website <code>authors</code> = list of authors comma-separated <code>n_authors</code> = number of authors (integer between 1 and 27) <code>col_code</code> = color-issue classification (see below) <code>volume</code> = Journal volume <code>start_page</code> = first page of paper (consecutive) <code>end_page</code> = last page of paper (consecutive) <code>base_url</code> = base url to access the PDF of the paper with <code>/volume/start_page/year/</code> <code>filename</code> = specific file name of the paper PDF (e.g. <code>hess-9-111-2005.pdf</code>) Color classification is stored in the <code>col_code</code> variable with: <code>0</code> = chromatic and issue-free, <code>1</code> = red-green issues, <code>2</code>= rainbow issues and <code>bw</code>= black and white paper. See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.
description: Further URLs: https://github.com/modche/rainbow_hydrology
description: {"references": ["Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology \u2013 guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549\u20134565, https://doi.org/10.5194/hess-25-4549-2021, 2021."]}
DOI: 10.5281/zenodo.5145745
title: Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication

description: The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below. <code>year</code> = year of publication (YYYY) <code>date</code> = date (YYYY-MM-DD) of publication <code>title</code> = full paper title from journal website <code>authors</code> = list of authors comma-separated <code>n_authors</code> = number of authors (integer between 1 and 27) <code>col_code</code> = color-issue classification (see below) <code>volume</code> = Journal volume <code>start_page</code> = first page of paper (consecutive) <code>end_page</code> = last page of paper (consecutive) <code>base_url</code> = base url to access the PDF of the paper with <code>/volume/start_page/year/</code> <code>filename</code> = specific file name of the paper PDF (e.g. <code>hess-9-111-2005.pdf</code>) Color classification is stored in the <code>col_code</code> variable with: <code>0</code> = chromatic and issue-free, <code>1</code> = red-green issues, <code>2</code>= rainbow issues and <code>bw</code>= black and white paper. See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.
description: Further URLs: https://github.com/modche/rainbow_hydrology
description: {"references": ["Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology \u2013 guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549\u20134565, https://doi.org/10.5194/hess-25-4549-2021, 2021."]}
DOI: 10.5281/zenodo.5015065
title: Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments

description: These are the data and code to go with "<strong>Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments</strong>" <strong>Figure 01 takes the following data/scripts:</strong> 20210610_Thally02_Figure01_plot_and_stats.R with track_keeper.csv and corr_Response growth .csv . These files contain growth rates per transfers for all selection environments throughout the experiment and growth rates in correlated environments during reciprocal transplants, respectively. <strong>Figure 02 takes the following data/scripts:</strong> 20210610_Thally02_Figure02_plot_and_stats.R with 20161120_res_logis_t000.csv and 20161123_resloglint300.csv . These files contain the output of the shapes of the growth curves (i.e. information on lag time , growth at µmax, K etc) for all samples in all selection environments at t0 and t300, respectively <strong>The remaining figures - position not clear at time of submission - take the following data/script. </strong> For plasticity in FRRF data, the script 20181204_FRRF_plasticity.R takes the FRRF raw data contained in allfvfmdata_thally_t300_t000.csv . Extracted parameters are in files CvaluesThally02.csv, psiPSI_slope_intercept.csv, and rP_extracted_values.csv and can be analysed using the R script extracted parameter plots.R . R script FRRF visualisation only .R is for visualisation only, as the title suggests. For comparing plasticity/growth , the data are in 20170327_giantbigtable.csv , and can be visualised/analysed in plast vs growth.R In order to recreate the AMOVAS based on SNVs, use all_variants_fixed-only_using_5x_depth_threshold.cvs with amova thally02.R For additional information, please contact elisa.schaum@uni-hamburg.de
DOI: 10.5281/zenodo.6394964
title: Essai

description: This is a trial
DOI: 10.6084/m9.figshare.19352048
title: Data sets for "Correlation of plastic events with local structure in jammed packings across spatial dimensions"

description: Plotted from "Correlation of plastic events with local structure in jammed packings across spatial dimensions".<br>Unless otherwise stated, all data are pickled python dictionaries, so one may e.g. do<br><br>```<br>import pickle<br><br>with open(filename, "rb") as f:<br> data = pickle.load(f)<br><br>print(data)<br>```<br><br>to see which data are present in the file.<br>## CZ_data.pkl and CS_data.pkl<br>These dictionaries contain the data used in Fig. 1 and Fig. S4. For example, to reproduce the main panels of Fig. 1, plot ``data["perc_list"][dim]`` vs. ``data["P_list"][dim]``, with ``data["std_list"][dim]`` as the error bar.<br><br>## corr_lengths.pkl<br>The data of Fig. 2. The pickle contains a tuple of four dictionaries, which contain Pressure, D2min correlation length, rescaled d2min correlation length, and Softness correlation length respectively.<br><br>## corrfuncs<br>Raw correlation function data as seen in Fig S7-S11, and thus used to generate the data of Fig. 2. Subdirectories specify system parameters – note phi=packing fraction, Lp=minus log base ten of pressure, and then within them each seed has a subdirectory, and each simulation frame containing a plastic event has its own file.<br><br>## PRZ_binned_std.pkl and PRZ_binned_mean.pkl<br>The data of figure 3, with Z scaled by standard deviation or mean respectively.<br><br>## PRZ_all.pkl<br>The data of figure S2.<br><br>## Z_dist.pkl<br>The data of figure S3.<br><br><br>
DOI: 10.6084/m9.figshare.19352048.v1
title: Data sets for "Correlation of plastic events with local structure in jammed packings across spatial dimensions"

description: Plotted from "Correlation of plastic events with local structure in jammed packings across spatial dimensions".<br>Unless otherwise stated, all data are pickled python dictionaries, so one may e.g. do<br><br>```<br>import pickle<br><br>with open(filename, "rb") as f:<br> data = pickle.load(f)<br><br>print(data)<br>```<br><br>to see which data are present in the file.<br>## CZ_data.pkl and CS_data.pkl<br>These dictionaries contain the data used in Fig. 1 and Fig. S4. For example, to reproduce the main panels of Fig. 1, plot ``data["perc_list"][dim]`` vs. ``data["P_list"][dim]``, with ``data["std_list"][dim]`` as the error bar.<br><br>## corr_lengths.pkl<br>The data of Fig. 2. The pickle contains a tuple of four dictionaries, which contain Pressure, D2min correlation length, rescaled d2min correlation length, and Softness correlation length respectively.<br><br>## corrfuncs<br>Raw correlation function data as seen in Fig S7-S11, and thus used to generate the data of Fig. 2. Subdirectories specify system parameters – note phi=packing fraction, Lp=minus log base ten of pressure, and then within them each seed has a subdirectory, and each simulation frame containing a plastic event has its own file.<br><br>## PRZ_binned_std.pkl and PRZ_binned_mean.pkl<br>The data of figure 3, with Z scaled by standard deviation or mean respectively.<br><br>## PRZ_all.pkl<br>The data of figure S2.<br><br>## Z_dist.pkl<br>The data of figure S3.<br><br><br>
DOI: 10.5281/zenodo.6394985
title: Essai

description: This is a trial
DOI: 10.5281/zenodo.6394963
title: Essai

description: This is a trial
DOI: 10.5281/zenodo.6394924
title: Metabolic and lipidomic data of patients with idiopathic pulmonary fibrosis and healthy volunteers

description: The metabolomic / lipidomic datasets used in the manuscript draft entiteld: "Changes in Serum Metabolomics in Idiopathic Pulmonary Fibrosis and effect of approved antifibrotic medication" by Benjamin Seeliger, Alfonso Carleo, Pedro David Wendel-Garcia, Jan Fuge, Ana Montes Worboys, Sven Schuchardt, Maria Molina-Molina and Antje Prasse Data is untransformed and missing data were imputated. All values are in µmol/L.
DOI: 10.5281/zenodo.6394923
title: Metabolic and lipidomic data of patients with idiopathic pulmonary fibrosis and healthy volunteers

description: The metabolomic / lipidomic datasets used in the manuscript draft entiteld: "Changes in Serum Metabolomics in Idiopathic Pulmonary Fibrosis and effect of approved antifibrotic medication" by Benjamin Seeliger, Alfonso Carleo, Pedro David Wendel-Garcia, Jan Fuge, Ana Montes Worboys, Sven Schuchardt, Maria Molina-Molina and Antje Prasse Data is untransformed and missing data were imputated. All values are in µmol/L.
DOI: 10.5281/zenodo.6394915
title: Estimating alpha, beta, and gamma diversity through deep learning

description: The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.
DOI: 10.5281/zenodo.5792186
title: Estimating alpha, beta, and gamma diversity through deep learning

description: The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.
DOI: 10.25345/c5x921n91
title: MassIVE MSV000089151 - Recurring exposure to low humidity induces transcriptional and protein level changes in the vocal folds of rabbits
title: Voice disorders are an important human health condition. Hydration is a commonly recommended preventive measure for voice disorders though it is unclear how vocal fold dehydration, is harmful at the cellular level. Airway surface dehydration can result from exposure to low humidity air. Here we have used a recurring 8-hour low humidity exposure over 15 days to mimic an occupational exposure to a low humidity environment. Exposure to moderate humidity was the control condition. Full thickness soft-tissue samples, including the vocal folds and surrounding laryngeal tissue, were collected for molecular analysis. RT-qPCR demonstrated a significant upregulation of MUC4 (mucin 4) and SCL26A9 (chloride channel) and a large fold-change though statistically non-significant upregulation of SCNNA1 (epithelial sodium channel). Proteomic analysis demonstrated differential regulation of proteins clustering into prospective functional groups of muscle structure and function, oxidative stress response, and the protein chaperonin stress response. Together, the data demonstrate that recurring exposure to low humidity is sufficient to induce both transcriptional and translational level changes in laryngeal tissue and suggest that low humidity exposure induces cellular stress at the level of the vocal folds.
DOI: 10.3886/e166343
title: social acceptance of human excreta

description: The study explored the three studies as part of a PhD of the principal investigator as follows:<br>Behavioral intentions of rural farmers to recycle human excreta in agriculturePsychometric analysis of the ecological dispositions of rural farming communities in South Africa: Implications for human excreta reuse in agriculture<br>Demand assessment and willingness to pay for human excreta derived co-compost. <b></b>
description: No weighting was used
description: Response Rates: The response rate was 100%
description: Presence of Common Scales: Several Likert-type scales were used
description: 341 farmers were randomly selected from a population of about 80 000 households
description: Farmers aged 18 and over living in rural South Africa with names<br>. Smallest Geographic Unit: Household
description: on-site questionnaire; cross sectional survey data
DOI: 10.3886/e166343v1
title: social acceptance of human excreta

description: The study explored the three studies as part of a PhD of the principal investigator as follows:<br>Behavioral intentions of rural farmers to recycle human excreta in agriculturePsychometric analysis of the ecological dispositions of rural farming communities in South Africa: Implications for human excreta reuse in agriculture<br>Demand assessment and willingness to pay for human excreta derived co-compost. <b></b>
description: No weighting was used
description: Response Rates: The response rate was 100%
description: Presence of Common Scales: Several Likert-type scales were used
description: 341 farmers were randomly selected from a population of about 80 000 households
description: Farmers aged 18 and over living in rural South Africa with names<br>. Smallest Geographic Unit: Household
description: on-site questionnaire; cross sectional survey data
DOI: 10.5281/zenodo.6394973
title: Dataset Timeline Covid-19

description: O projeto Timeline Covid-19 tem como objetivo produzir, através da recuperação de informações online, notícias veiculadas pela grande mídia e pelas instituições federais a respeito de acontecimentos e fatos ocorridos em solo brasileiro ligados à Covid-19. Consiste em uma linha do tempo iniciada no dia 26 de fevereiro de 2020, data da confirmação do primeiro caso do novo coronavírus no Brasil, e não possui uma data final pré-definida, continuando a ser alimentada até o momento [24 mar 2022]. A planilha é o <em>dataset</em> base para a Timeline (usando tecnologia Timeline JS do KnightLab da Northwestern University, EUA) publicada no portal do Ministério da Ciência, Tecnologia e Inovações no combate à COVID-19 &lt;http://covid19.mctic.gov.br/graf/&gt;. Neste <em>dataset</em> está presente o conteúdo da linha do tempo completa. Na planilha, as colunas de A a I são relacionadas à data de início e encerramento de um evento. Como a proposta da linha do tempo é tratar de notícias diárias, apenas uma data foi mantida para marcar seu acontecimento e não denotar sua continuidade. As demais colunas consistem em: <strong>Headline</strong>: o título da notícia, sintetiza o conteúdo da reportagem e o exibe nas duas seções da linha do tempo; <strong>Text</strong>: uma breve sinopse ou descrição do que trata a notícia para ser exibida no detalhamento da linha do tempo; <strong>Media</strong>: link para a imagem exibida na descrição da notícia presente na segunda seção; <strong>Media Credit</strong>: indicação do veículo de informação que publicou a notícia referendada; <strong>Media Caption</strong>: link para a notícia publicada; <strong>Background</strong>: código para a cor a ser utilizada no fundo da seção inferior da linha do tempo. Disponibilizamos aqui atualizações da planilha onde cada versão é mais completa que sua antecessora. Além disso, também promovemos o trabalho de troca dos links que quebraram ao longo do tempo.
description: Pesquisa realizada no âmbito do Laboratório em Rede de Humanidades Digitais (LARHUD), IBICT. Com apoio (financiamento) do CNPq e da FAPERJ.
DOI: 10.5281/zenodo.3908601
title: Dataset Timeline Covid-19

description: O projeto Timeline Covid-19 tem como objetivo produzir, através da recuperação de informações online, notícias veiculadas pela grande mídia e pelas instituições federais a respeito de acontecimentos e fatos ocorridos em solo brasileiro ligados à Covid-19. Consiste em uma linha do tempo iniciada no dia 26 de fevereiro de 2020, data da confirmação do primeiro caso do novo coronavírus no Brasil, e não possui uma data final pré-definida, continuando a ser alimentada até o momento [24 mar 2022]. A planilha é o <em>dataset</em> base para a Timeline (usando tecnologia Timeline JS do KnightLab da Northwestern University, EUA) publicada no portal do Ministério da Ciência, Tecnologia e Inovações no combate à COVID-19 &lt;http://covid19.mctic.gov.br/graf/&gt;. Neste <em>dataset</em> está presente o conteúdo da linha do tempo completa. Na planilha, as colunas de A a I são relacionadas à data de início e encerramento de um evento. Como a proposta da linha do tempo é tratar de notícias diárias, apenas uma data foi mantida para marcar seu acontecimento e não denotar sua continuidade. As demais colunas consistem em: <strong>Headline</strong>: o título da notícia, sintetiza o conteúdo da reportagem e o exibe nas duas seções da linha do tempo; <strong>Text</strong>: uma breve sinopse ou descrição do que trata a notícia para ser exibida no detalhamento da linha do tempo; <strong>Media</strong>: link para a imagem exibida na descrição da notícia presente na segunda seção; <strong>Media Credit</strong>: indicação do veículo de informação que publicou a notícia referendada; <strong>Media Caption</strong>: link para a notícia publicada; <strong>Background</strong>: código para a cor a ser utilizada no fundo da seção inferior da linha do tempo. Disponibilizamos aqui atualizações da planilha onde cada versão é mais completa que sua antecessora. Além disso, também promovemos o trabalho de troca dos links que quebraram ao longo do tempo.
description: Pesquisa realizada no âmbito do Laboratório em Rede de Humanidades Digitais (LARHUD), IBICT. Com apoio (financiamento) do CNPq e da FAPERJ.