cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22280494.v1
title: Time is money: Examining the time cost and associated charges of common performance validity tests

description: <b>Objective:</b> This study presents data on the time cost and associated charges for common performance validity tests (PVTs). It also applies an approach from cost effectiveness research to comparison of tests that incorporates cost and classification accuracy. <b>Method:</b> A recent test usage survey was used to identify PVTs in common use among adult neuropsychologists. Data on test administration and scoring time were aggregated. Charges per test were calculated. A cost effectiveness approach was applied to compare pairs of tests from three studies using data on test administration time and classification accuracy operationalized as improvement in posterior probability beyond base rate. Charges per unit increase in posterior probability over base rate were calculated for base rates of invalidity ranging from 10 to 40%. <b>Results:</b> Ten commonly used PVTs measures showed a wide range in test administration and scoring time from 1 to 3 minutes to over 40 minutes with associated charge estimates from $4 to $284. Cost effectiveness comparisons illustrated the nuance in test selection and benefit of considering cost in relation to outcome rather than prioritizing time (i.e. cost minimization) classification accuracy alone. <b>Conclusions:</b> Findings extend recent research efforts to fill knowledge gaps related to the cost of neuropsychological evaluation. The cost effectiveness approach warrants further study in other samples with different neuropsychological and outcome measures.
DOI: 10.34740/kaggle/dsv/5173201
title: Predict Pakistan Elections 2018
DOI: 10.5281/zenodo.7738338
title: Aspectos clínico-epidemiológicos de pacientes con tuberculosis, Guantánamo 2013-2018

description: Base de datos de Aspectos clínico-epidemiológicos de pacientes con tuberculosis, Guantánamo 2013-2018. Las variables que aparecen son: edad, sexo, grupos de riesgo (fumadores, alcohólicos, desnutridos, retrasados mentales, reclusos, sida y gestantes), municipio al que pertenece y forma clínica (pulmonar, extrapulmonar). Los datos inherentes a cada una de las variables se obtuvieron de la base de datos del Centro Provincial de Higiene, Epidemiología y Microbiología Guantánamo, Cuba.
DOI: 10.5281/zenodo.7738337
title: Aspectos clínico-epidemiológicos de pacientes con tuberculosis, Guantánamo 2013-2018

description: Base de datos de Aspectos clínico-epidemiológicos de pacientes con tuberculosis, Guantánamo 2013-2018. Las variables que aparecen son: edad, sexo, grupos de riesgo (fumadores, alcohólicos, desnutridos, retrasados mentales, reclusos, sida y gestantes), municipio al que pertenece y forma clínica (pulmonar, extrapulmonar). Los datos inherentes a cada una de las variables se obtuvieron de la base de datos del Centro Provincial de Higiene, Epidemiología y Microbiología Guantánamo, Cuba.
DOI: 10.34740/kaggle/dsv/5173247
title: Pakistan Corona Virus Dataset
DOI: 10.3886/e145101v2-134040
title: Weighted Crosswalks for NAICS and SIC Industry Codes

description: We provide weighted crosswalk files for the purpose of bridging Standard Industrial Classification (SIC) codes and North American Industry Classification System (NAICS) codes. SIC codes were the standard industry classification system for decades, but they eventually couldn’t keep up with the changing industrial structure of the 1990s. NAICS codes took over in 1997 and quickly became the reporting system for most government statistics in the US and elsewhere. This switch poses a problem for researchers since it imposes an artificial break in time series data. Unweighted crosswalk tables can help connect the two systems, but the many splits and merges create mappings that are not one-to-one. For situations that require one-to-one translations, researchers find themselves guessing as to the best match. And for situations where splitting and merging is acceptable, choosing weights sometimes feels arbitrary. The crosswalks included here contain weighting variables that make it possible to smoothly bridge between systems and construct consistent time series in a nonarbitrary way. Three different weighting schemes are included. The first based on employment, the second based on number of establishments, and the third based on total payroll.
DOI: 10.25345/c5rj4948r
title: MassIVE MSV000091492 - Analysis of the N-glycosylation profiles of the spike proteins from the Alpha, Beta, Gamma, and Delta variants of SARS-CoV-2
title: N-glycosylation plays an important role in the structure and function of membrane and secreted proteins. Viral proteins used in cell entry are often extensively glycosylated to assist in protein folding, provide stability, and shield the virus from immune recognition by its host (described as a 'glycan shield'). The SARS-CoV-2 spike protein (S) is a prime example, having 22 potential sites of N-glycosylation per protein monomer, as predicted from the primary sequence. In this report we conducted a comprehensive mass spectrometric analysis of the N-glycosylation profiles of recombinant spike proteins derived from four common SARS-CoV-2 variants classified as Variant of Concern, including Alpha, Beta, Gamma, and Delta along with D614G variant as a control. Our data reveal that the amino acid substitutions and deletions between variants impacts the abundance and type of glycans on glycosylation sites of the spike protein. Some of the N-glycosylation sequons in S showed differences between SARS-CoV-2 variants in the distribution of glycan forms. In comparison with our previously reported site-specific glycan analysis on the S-D614G and its ancestral protein, glycan types on later variants showed high similarity on the site-specific glycan content to S-D614G. Detailed site-specific glycan analysis of a wide variety of SARS-CoV variants provides useful information toward the understanding of the role of protein glycosylation on viral protein structure and function.
DOI: 10.5281/zenodo.7736981
title: Species-colored Themisto v3 index with 640k bacterial genomes

description: This is a Themisto v3 [1] index containing the 639,981 high-quality genomes from 661k bacterial genomes dataset of Blackwell et al. [2]. The index contains all distinct 31-mers of the dataset (both strands). There are 71 billion distinct 31-mers in the data (35.5 billion reverse complement pairs). Each k-mer is annotated with the set of species identifiers that contain that 31-mer. The species identifiers are called colors. There are 2340 distinct colors in the dataset, so the color identifiers range from 0 to 2339. To pseudoalign reads.fastq against the index using 16 threads, install Themisto v3, and use the following command: <pre><code class="language-bash">themisto pseudoalign -q reads.fastq -i themisto_640k/index -t 16 --temp-dir .</code></pre> This will output one line of space-separated integers per read in the input. The first integer on a line is the zero-based rank of the read in the fastq file, and the rest of the integers are the identifiers of colors that are compatible with the read. The file color_names.csv lists the species name and the taxid for each color. The pseudoalignment counts <strong>should not be directly used as abundance estimates</strong> because they only describe which reads are <em>compatible</em> with which species, and a single read may be compatible with many. We recommend using mSWEEP to estimate abundances based on the pseudoalignment data: https://github.com/PROBIC/mSWEEP. -- The index was constructed with Themisto v3.0.0 using the following command line parameters: <pre><code class="language-bash">themisto build -i input_file_list.txt --file-colors --reverse-complements -o 640k_bacteria -m 512000 -t 48 -k 31 --temp-dir temp --verbose -d 20</code></pre> The file source_accessions.txt lists the accession numbers of assemblies included in the database. [1] Alanko, J. N., Vuohtoniemi, J., Maklin, T., & Puglisi, S. J. (2023). Themisto: a scalable colored k-mer index for sensitive pseudoalignment against hundreds of thousands of bacterial genomes. bioRxiv, 2023-02. [2] Blackwell, G. A., Hunt, M., Malone, K. M., Lima, L., Horesh, G., Alako, B. T., ... & Iqbal, Z. (2021). Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences. PLoS biology, 19(11), e3001421.
DOI: 10.5281/zenodo.7736980
title: Species-colored Themisto v3 index with 640k bacterial genomes

description: This is a Themisto v3 [1] index containing the 639,981 high-quality genomes from 661k bacterial genomes dataset of Blackwell et al. [2]. The index contains all distinct 31-mers of the dataset (both strands). There are 71 billion distinct 31-mers in the data (35.5 billion reverse complement pairs). Each k-mer is annotated with the set of species identifiers that contain that 31-mer. The species identifiers are called colors. There are 2340 distinct colors in the dataset, so the color identifiers range from 0 to 2339. To pseudoalign reads.fastq against the index using 16 threads, install Themisto v3, and use the following command: <pre><code class="language-bash">themisto pseudoalign -q reads.fastq -i themisto_640k/index -t 16 --temp-dir .</code></pre> This will output one line of space-separated integers per read in the input. The first integer on a line is the zero-based rank of the read in the fastq file, and the rest of the integers are the identifiers of colors that are compatible with the read. The file color_names.csv lists the species name and the taxid for each color. The pseudoalignment counts <strong>should not be directly used as abundance estimates</strong> because they only describe which reads are <em>compatible</em> with which species, and a single read may be compatible with many. We recommend using mSWEEP to estimate abundances based on the pseudoalignment data: https://github.com/PROBIC/mSWEEP. -- The index was constructed with Themisto v3.0.0 using the following command line parameters: <pre><code class="language-bash">themisto build -i input_file_list.txt --file-colors --reverse-complements -o 640k_bacteria -m 512000 -t 48 -k 31 --temp-dir temp --verbose -d 20</code></pre> The file source_accessions.txt lists the accession numbers of assemblies included in the database. [1] Alanko, J. N., Vuohtoniemi, J., Maklin, T., & Puglisi, S. J. (2023). Themisto: a scalable colored k-mer index for sensitive pseudoalignment against hundreds of thousands of bacterial genomes. bioRxiv, 2023-02. [2] Blackwell, G. A., Hunt, M., Malone, K. M., Lima, L., Horesh, G., Alako, B. T., ... & Iqbal, Z. (2021). Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences. PLoS biology, 19(11), e3001421.
DOI: 10.3886/e186501v1
title: The Demographic Trade Database

description: The Demographic Trade Database is an ongoing project compiling multiple sources of information on bilateral trade, macroeconomic variables, and population demographics into easy to use resource. This data is made available to provide researchers with the tools needed to further explore questions relating to how underlying population movements affect the bilateral flow of trade between countries, and the degree to which projected changes in demographics may affect the global trade landscape in years to come.<br>
DOI: 10.3886/e186501
title: The Demographic Trade Database

description: The Demographic Trade Database is an ongoing project compiling multiple sources of information on bilateral trade, macroeconomic variables, and population demographics into easy to use resource. This data is made available to provide researchers with the tools needed to further explore questions relating to how underlying population movements affect the bilateral flow of trade between countries, and the degree to which projected changes in demographics may affect the global trade landscape in years to come.<br>TradeDemography.dta provides the main dataset which contains trade and demographic values for 39,601 country-pairs from 1970-2019. The dataset TradeDemography_wProj.dta has the same economic, but also includes medium variant projections for demographic variables from 2020-2100. <br><br>For a detailed documentation and discussion of constructed demographic variables: https://www.josephkopecky.com/<br><br><br>Source used to construct this database come from the CEPII Gravity database and the UN World Population Prospects database:<br><br>Conte, M., P. Cotterlaz and T. Mayer (2022), "The CEPII Gravity database". CEPII Working Paper N°2022-05, July 2022. <br>United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections. UN DESA/POP/2022/TR/NO. 4.<br><br><br><br><br>
DOI: 10.3886/e186501v2
title: The Demographic Trade Database

description: The Demographic Trade Database is an ongoing project compiling multiple sources of information on bilateral trade, macroeconomic variables, and population demographics into easy to use resource. This data is made available to provide researchers with the tools needed to further explore questions relating to how underlying population movements affect the bilateral flow of trade between countries, and the degree to which projected changes in demographics may affect the global trade landscape in years to come.<br>TradeDemography.dta provides the main dataset which contains trade and demographic values for 39,601 country-pairs from 1970-2019. The dataset TradeDemography_wProj.dta has the same economic, but also includes medium variant projections for demographic variables from 2020-2100. <br><br>For a detailed documentation and discussion of constructed demographic variables: https://www.josephkopecky.com/<br><br><br>Source used to construct this database come from the CEPII Gravity database and the UN World Population Prospects database:<br><br>Conte, M., P. Cotterlaz and T. Mayer (2022), "The CEPII Gravity database". CEPII Working Paper N°2022-05, July 2022. <br>United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections. UN DESA/POP/2022/TR/NO. 4.<br><br><br><br><br>
DOI: 10.3886/e186501v3
title: The Demographic Trade Database

description: The Demographic Trade Database is an ongoing project compiling multiple sources of information on bilateral trade, macroeconomic variables, and population demographics into easy to use resource. This data is made available to provide researchers with the tools needed to further explore questions relating to how underlying population movements affect the bilateral flow of trade between countries, and the degree to which projected changes in demographics may affect the global trade landscape in years to come.<br>TradeDemography.dta provides the main dataset which contains trade and demographic values for 39,601 country-pairs from 1970-2019. The dataset TradeDemography_wProj.dta has the same economic, but also includes medium variant projections for demographic variables from 2020-2100. <br><br>For a detailed documentation and discussion of constructed demographic variables: https://www.josephkopecky.com/<br><br><br>Source used to construct this database come from the CEPII Gravity database and the UN World Population Prospects database:<br><br>Conte, M., P. Cotterlaz and T. Mayer (2022), "The CEPII Gravity database". CEPII Working Paper N°2022-05, July 2022. <br>United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections. UN DESA/POP/2022/TR/NO. 4.<br><br><br><br><br>
DOI: 10.3886/e186561
title: The MacroDemography Database

description: The MacroDemography Database is an ongoing project aimed combining long-run macroeconomic and demographic data in a readily usable format for researchers aiming to explore questions relating to the relationship between economies and their underlying age structure. By enriching the set of demographic variables used in long run economic analysis while taking advantage of the contribution of the contribution of the Jordà-Schularick-Taylor Macrohistory Database in providing a rich set of macroeconomic variables for a panel of countries it is possible to revisit many questions in the literature on the economic implications of population aging that were not possible in the past. My aim is to continue to update this database with various sources of demographic variable to improve the completeness of the panel over time. <br><br>At present the data contains an unbalanced panel of 18 countries spanning the time period from 1870 to 2018. The dataset "MacroDemography.dta" is the main data, while "MacroDemography_wProjections" appends median variant projections for population data from 2020 to 2100. <br><br>This dataset uses the following sources that should be cited when using the relevant statistics. Please visit the source websites for more information and https://www.josephkopecky.com/ where updates will be regularly posted. <br><br>For Macroeconomic Data:Òscar Jordà, Moritz Schularick, and Alan M. Taylor. 2017. “Macrofinancial History and the New Business Cycle Facts.” in NBER Macroeconomics Annual 2016, volume 31, edited by Martin Eichenbaum and Jonathan A. Parker. Chicago: University of Chicago Press. For rates of return data: Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, and Alan M. Taylor. 2019. “The Rate of Return on Everything, 1870–2015.” Quarterly Journal of Economics, 134(3), 1225-1298. For data on bank balance sheet ratios:<br>Òscar Jordà, Björn Richter, Moritz Schularick, and Alan M. Taylor. 2021. "Bank capital redux: solvency, liquidity, and crisis." The Review of Economic Studies, 88(1), 260-286. Much of the demographic data comes from the Human Mortality database:<br>HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org (data downloaded on 15/03/2023). US Data Population data pre-1933 comes from the US census:https://www.census.gov/data/tables/time-series/demo/popest/pre-1980-national.html<br>Data on projections comes from the UN Population Prospects data:United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections. UN DESA/POP/2022/TR/NO. 4.<br><br><br><br><br>
DOI: 10.3886/e186561v1
title: The MacroDemography Database

description: The MacroDemography Database is an ongoing project aimed combining long-run macroeconomic and demographic data in a readily usable format for researchers aiming to explore questions relating to the relationship between economies and their underlying age structure. By enriching the set of demographic variables used in long run economic analysis while taking advantage of the contribution of the contribution of the Jordà-Schularick-Taylor Macrohistory Database in providing a rich set of macroeconomic variables for a panel of countries it is possible to revisit many questions in the literature on the economic implications of population aging that were not possible in the past. My aim is to continue to update this database with various sources of demographic variable to improve the completeness of the panel over time. <br><br>At present the data contains an unbalanced panel of 18 countries spanning the time period from 1870 to 2018. The dataset "MacroDemography.dta" is the main data, while "MacroDemography_wProjections" appends median variant projections for population data from 2020 to 2100. <br><br>This dataset uses the following sources that should be cited when using the relevant statistics. Please visit the source websites for more information and https://www.josephkopecky.com/ where updates will be regularly posted. <br><br>For Macroeconomic Data:Òscar Jordà, Moritz Schularick, and Alan M. Taylor. 2017. “Macrofinancial History and the New Business Cycle Facts.” in NBER Macroeconomics Annual 2016, volume 31, edited by Martin Eichenbaum and Jonathan A. Parker. Chicago: University of Chicago Press. For rates of return data: Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, and Alan M. Taylor. 2019. “The Rate of Return on Everything, 1870–2015.” Quarterly Journal of Economics, 134(3), 1225-1298. For data on bank balance sheet ratios:<br>Òscar Jordà, Björn Richter, Moritz Schularick, and Alan M. Taylor. 2021. "Bank capital redux: solvency, liquidity, and crisis." The Review of Economic Studies, 88(1), 260-286. Much of the demographic data comes from the Human Mortality database:<br>HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org (data downloaded on 15/03/2023). US Data Population data pre-1933 comes from the US census:https://www.census.gov/data/tables/time-series/demo/popest/pre-1980-national.html<br>Data on projections comes from the UN Population Prospects data:United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections. UN DESA/POP/2022/TR/NO. 4.<br><br><br><br><br>
DOI: 10.5517/ccdc.csd.cc28by8v
title: CCDC 2095887: Experimental Crystal Structure Determination

description: Related Article: Buthanapalli Ramakrishna, D. Divya, Udit Kumar, Shaikh M. Mobin, Bala Manimaran|2022|J.Organomet.Chem.|972|122371|doi:10.1016/j.jorganchem.2022.122371
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.cc2983j7
title: CCDC 2122958: Experimental Crystal Structure Determination

description: Related Article: Bin Shi, Jia-Bin Liu, Ze-Tian Wang, Liang Wang, Yu Lan, Liang-Qiu Lu, Wen-Jing Xiao|2022|Angew.Chem.,Int.Ed.|61|e202117215|doi:10.1002/anie.202117215
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.cc2c5f00
title: CCDC 2179951: Experimental Crystal Structure Determination

description: Related Article: Prasun Acharya, Arun Kuila, Ushasi Pramanik, Venkatesha R. Hathwar, Paula Brandao, Saptarshi Mukherjee, Swapan Maity, Tithi Maity, Ribhu Maity, Bidhan Chandra Samanta|2023|RSC Advances|13|7632|doi:10.1039/D2RA08341H
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.22280638
title: Supplemental table S1.xlsx

description: <strong>Supplemental Table S1.</strong> <strong>Supporting data for Figure 1.</strong> Proteome data set from iWAT of control and iAdRiKO mice 4 weeks after tamoxifen treatment.
DOI: 10.6084/m9.figshare.22280638.v1
title: Supplemental table S1.xlsx

description: <strong>Supplemental Table S1.</strong> <strong>Supporting data for Figure 1.</strong> Proteome data set from iWAT of control and iAdRiKO mice 4 weeks after tamoxifen treatment.