Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Dataset related to publication: Robust radiative cooling via surface phonon coupling-enhanced emissivity from SiO2 micropillar arrays

Dataset related to the publication:Zhenmin Ding, Xin Li, Hulin Zhang, Dukang Yan, Jérémy Werlé, Ying Song, Lorenzo Pattelli, Jiupeng Zhao, Hongbo Xu, Yao Li. Robust radiative cooling via surface phonon coupling-enhanced emissivity from SiO2 micropillar arrays. International Journal of Heat and Mass Transfer, 220, 125004 (2024). doi: <a href="https://doi.org/10.1016/j.ijheatmasstransfer.2023.125004">10.1016/j.ijheatmasstransfer.2023.125004</a>The repository contains MATLAB/Octave scripts to perform rigorous coupled-wave analysis (RCWA) simulations for a SiO2 layer decorated with micropillars.The main script runs a series of rigorous electromagnetic simulations over the atmospheric transparency window wavelength range (8-13 µm) for all combination of three main structural parameters (pillar diameter, spacing and height), within a user-defined range.Running the code requires the RETICOLO v9 RCWA code:<blockquote>Jean-Paul Hugonin, & Philippe Lalanne. (2021). Light-in-complex-nanostructures/RETICOLO: V9. Zenodo. <a href="https://doi.org/10.5281/zenodo.4419063">https://doi.org/10.5281/zenodo.4419063</a></blockquote>

opencc-by-4.0Nov 2023View details →

Data and scripts for the publication "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations"

Coils and VMEC configurations for the three stellarator configurations presented in "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations", including the optimization scripts used to find the coils and plot scripts used to produce the figures in the text.

opencc-by-4.0Sep 2023View details →

Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023).

Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023), DOI 10.1038/s41467-023-41718-4, include images, data used for generate that images, input files, converged potential files used for the calculations on SPR-KKR package 8.6. and raw data files.

opencc-by-4.0Jun 2023View details →

Simulated query reads used for benchmarks in Metabuli publication.

Simulated query reads used for synthetic benchmarks in Metabuli publication.Prokaryote Subspecies Inclusion Test<ul><li>Illumina paired-end short reads<ul><li>Simulated using Mason2 based on NovaSeq 6000's error rates.</li><li>prokaryote_inclusion_reads_1.fna.gz</li><li>prokaryote_inclusion_reads_2.fna.gz</li></ul></li><li>PacBio HiFi long reads (Sequel II CCS)<ul><li>Multi-pass reads were simulated using PBSIM3 based on Sequel Il's error profile.</li><li>CCS reads were generated using PacBio's CCS-read tool.</li><li>prokaryote_inclusion_sequel_ccs.fq.gz</li></ul></li><li>PacBio Sequel II long reads<ul><li>Simulated using PBSIM3 based on Sequel Il's error profile.</li><li>prokaryote_inclusion_sequel.fq.gz</li></ul></li><li>ONT long reads<ul><li>Simulated using PBSIM3 based on ONT's error profile.</li><li>prokaryote_inclusion_ont.fq.gz</li></ul></li></ul>Prokaryote Subspecies exclusion Test<ul><li>Illumina paired-end short reads<ul><li>Simulated using Mason2 based on NovaSeq 6000's error rates.</li><li>prokaryote_ss-exclusion_reads_1.fna.gz</li><li>prokaryote_ss-exclusion_reads_2.fna.gz</li></ul></li><li>PacBio HiFi long reads (Sequel II CCS)<ul><li>Multi-pass reads were simulated using PBSIM3 based on Sequel Il's error profile.</li><li>CCS reads were generated using PacBio's CCS-read tool.</li><li>prokaryote_ss-exclusion_sequel_ccs.fq.gz</li></ul></li><li>PacBio Sequel II long reads<ul><li>Simulated using PBSIM3 based on Sequel Il's error profile.</li><li>prokaryote_ss-exclusion_sequel.fq.gz</li></ul></li><li>ONT long reads<ul><li>Simulated using PBSIM3 based on ONT's error profile.</li><li>prokaryote_ss-exclusion_ont.fq.gz</li></ul></li></ul>Prokaryote Species Exclusion Test<ul><li>Illumina paired-end short reads<ul><li>Simulated using Mason2 based on NovaSeq 6000's error rates.</li><li>prokaryote_exclusion_reads_1.fna.gz</li><li>prokaryote_exclusion_reads_2.fna.gz</li></ul></li><li>PacBio HiFi long reads (Sequel II CCS)<ul><li>Multi-pass reads were simulated using PBSIM3 based on Sequel Il's error profile.</li><li>CCS reads were generated using PacBio's CCS-read tool.</li><li>prokaryote_sp-exclusion_sequel_ccs.fq.gz</li></ul></li><li>PacBio Sequel II long reads<ul><li>Simulated using PBSIM3 based on Sequel Il's error profile.</li><li>prokaryote_sp-exclusion_sequel.fq.gz</li></ul></li><li>ONT long reads<ul><li>Simulated using PBSIM3 based on ONT's error profile.</li><li>prokaryote_exclusion_ont.fq.gz</li></ul></li></ul>

opencc-by-4.0Dec 2023View details →

PISM model output data from Garbe et al. (Nature, 2020) publication

This dataset contains the <a href="https://www.pism.io">PISM</a> model output data of the Antarctic Ice Sheet hysteresis simulations published and discussed in<a href="https://doi.org/10.1038/s41586-020-2727-5">Garbe, J., Albrecht, T., Levermann, A., Donges, J. F., and Winkelmann, R. The hysteresis of the Antarctic Ice Sheet. Nature 585(7826), 2020.</a>A detailed description of the individual file contents is given in `README.txt` below. The corresponding PISM model code used for these simulations is archived <a href="https://doi.org/10.5281/zenodo.3956431">here</a>.In case of questions, feel free to contact me at <a href="mailto:julius.garbe@pik-potsdam.de">julius.garbe@pik-potsdam.de</a>.

opencc-by-4.0Jan 2024View details →

Data for the publication "Sodium Triflate Water-in-Salt Electrolyte in Advanced Battery Applications: A First-principles Based Molecular Dynamics Study"

The datasets 'CONTCAR_aiMLMD' and 'CONTCAR_AIMD' represent the final structures obtained from the aiMLMD and AIMD simulations, respectively. These simulations were conducted using VASP at T=333K and c=9.25 m. The datasets 'NP.rdf' and 'MSD_NP.xlsx' represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. The associated MD simulation was performed using a nonpolarizable force field in the LAMMPS package at T=333K and c=9.25 m. The file 'dataNP.lmp' includes the initial configuration for this simulation. The GROMOS parameters were employed for LJ interactions of sodium and all other force field parameters were set according to Table 1 in the manuscript. The datasets 'P.rdf' and 'MSD_P.xlsx,' respectively, represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. These data were obtained employing the Drude oscillator model in the LAMMPS package at T=333K and c=10 m. The file 'dataP.lmp' includes the initial configuration for this simulation. The simulation was conducted using the optimal force field parameters 'Sys. 1,' as described in table 3 of the manuscript. The second column in the files 'NP.rdf' and 'NP.rdf' represents the distance from sodium. The subsequent odd columns display the radial distribution functions for the Na-C, Na-F, Na-S, Na-O, Na-Na, Na-Hw, and Na-Ow pairs, while the even columns present the coordination numbers for the same atom pairs.

opencc-by-4.0Apr 2023View details →

Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"

Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany. Journal of Archaeological Science 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a> The repository contains: <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. Please refer to the latest version of this repository for the most up-to-date R code.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>

opencc-by-4.0Jan 2024View details →

Data for a publication "Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials"

<div> These data are published as part of the paper: “Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials” published in journal: “Materials”.  </div> <div> This repository contains one folder, namely: “concentration ICP_MS”  </div> <div> This folder contains further data relevant to the results published in the paper, which are described in a separate file inside.    Preprint evolution (versions). 2024-01-31-V2; <a href="https://doi.org/10.20944/preprints202401.2053.v2" target="_blank" rel="noopener">(https://doi.org/10.20944/preprints202401.2053.v2</a>) - the acknowledgement was modified as well as the data availability mentioning the Zenodo repository with the dataset as well as the availability of the datasets generated during and/or analyzed during the current study on reasonable request from corresponding author. </div> <div>   </div>

opencc-by-4.0Mar 2024View details →

Dataset 1 for Publication: Separation-dependent near-field effects in Mie scattering spectra of two optically trapped aerosol droplets

Dataset for Publication: ASCII files of Mie spectra for each experimentally analysed run, calibrated wavelength files, and brightfield images at each interdroplet separation.

opencc-by-4.0Nov 2022View details →

FOPPA: A database of French Open Public Procurement Award notices

The FOPPA database (French Open Public Procurement Award notices) is a database constituted in the framework of the ANR DeCoMaP project (ANR-19-CE38-0004). It contains public procurement notices published in France from 2010 to 2020. It relies on a subset of the TED database. These data have a number of issues, the most serious being that the unique ID of most involved agents are missing. We performed a number of operations to solve these issues and obtain a usable database. These operations and their outcomes are described in detail in the following technical report: L. Potin, V. Labatut, R. Figueiredo, C. Largeron & P. H. Morand. FOPPA: A database of French Open Public Procurement Award notices, Technical Report, Avignon Université, 2022. ⟨<a href="https://hal.archives-ouvertes.fr/hal-03796734">hal-03796734</a>⟩ The report also describes the database itself.    Source code: The source code implementing these operations is publicly available as a GitHub repository: <a href="https://github.com/CompNet/FoppaInit/releases/tag/v1.0.3">https://github.com/CompNet/FoppaInit/releases/tag/v1.0.3</a>   Cite as: If you use these data or the associated source code, please cite the following article : Potin, L.; Labatut, V.; Morand, P.-H. & Largeron, C. FOPPA: an Open Database of French Public Procurement Award Notices From 2010-2020. Scientific Data 10:303, 2023. DOI: <a href="http://doi.org/10.1038/s41597-023-02213-z">10.1038/s41597-023-02213-z</a>  ⟨<a href="https://hal.archives-ouvertes.fr/hal-04101350">hal-04101350</a>⟩ <code>@Article{Potin2023,</code> <code>  author    = {Potin, Lucas and Labatut, Vincent and Morand, Pierre-Henri and Largeron, Christine},</code> <code>  title     = {{FOPPA}: an Open Database of French Public Procurement Award Notices From 2010-2020},</code> <code>  journal   = {Scientific Data},</code> <code>  year      = {2023},</code> <code>  volume    = {10},</code> <code>  pages     = {303},</code> <code>  doi       = {10.1038/s41597-023-02213-z},</code> <code>}</code>

Fetal exposure to the Ukraine famine of 1932-1933 and adult Type 2 Diabetes Mellitus (Public data and analytical code)

Abstract The short-term impact of famines on death and disease is well documented but it is difficult to estimate their potential long-term impact. We used the setting of the man-made Ukrainian Holodomor famine of 1932-1933 to examine the relationship between prenatal famine and adult Type 2 diabetes mellitus (T2DM). This ecological study included 128,225 T2DM cases diagnosed between 2000-2008 among 10,186,016 male and female Ukrainians born between 1930 and 1938. Individuals who were born in the first half-year of 1934, and hence exposed in early gestation to the mid-1933 peak famine period, had a larger than two-fold likelihood of T2DM (OR 2.21; 95% CI 2.00-2.45) compared to unexposed controls. There was a dose-response relationship between severity of famine exposure and adult T2DM risk comparing individuals born in regions with severe, very severe, and extreme famine to births in the no-famine region.   Description of the data and analytical code In exploratory analyses we first examined whether the odds for T2DM were elevated for any month of birth in the period January 1930 to December 1938 in any of the four regions of varying famine intensity. This was achieved by comparing, within each region, the T2DM odds for births in any month and year of birth relative to the T2DM odds for births in the same month combining all other years of birth. The analysis served to identify potential relations of famine with specific months and years of birth, controlling for month of birth effects. We observed increased T2DM odds ratios for births between January and June 1934 in famine-exposed oblasts, with smaller increases for births in 1935 and 1936 in these months. Our findings suggested that in multivariate modelling statistical control for month of birth effects could be accomplished by adjusting for the January-June period. Our findings are presented in the data file '01 Odds Ratio for T2DM Over Time' and show the odds ratios (ORs) for Type 2 Diabetes Mellitus (T2DM) comparing the region-specific T2DM odds for each birth year and month relative to births in the same months but combining all other years of birth. The R syntax file '01 Odds of T2DM Over Time Figure' provides the code necessary to reproduce the figure.   For confirmatory analyses we employed a Difference-in-Differences approach to quantify associations between prenatal exposure to famine and T2DM, taking into account year of birth, half-year of birth (Jan-Jun vs Jul-Dec), region, and their interactions. This analysis was conducted initially for each gender separately and then for both genders combined, adjusting for We carried out sensitivity analyses to assess potential changes in T2DM odds arising from the use of pre-famine births vs post-famine births as controls.  Our findings are presented in the data file '02 Ukraine Famine 1932-33 Main Data'. Information on the number of T2DM cases by gender, region of residence, and year and month of birth 1930-1938 in Ukraine was collected by the national Ukraine Diabetes Register (Komisarenko Institute of Endocrinology and Metabolism, Kyiv) between 2000-2008. The number of births in the same subgroups, representing the populations at risk for T2DM, was estimated by demographic population reconstruction methods as reported in the publication. We classified the birth counts by year of birth, the semi-annual birth period (January-June vs. July-December), region of birth, and gender. The SPSS syntax file titled '02 Ukraine Famine 1932-33 Main Analysis' provides the code to replicate our main findings as presented in the publication.   In a separate analysis we visualized by a meta-regression approach the relation between famine intensity at the oblast level in 1933 and the odds for adult T2DM.  The data required for the replication of our findings are included in the file '03 Odds Ratio for T2DM and Famine Intensity at Oblast Level'. The R syntax file titled '03 Ukraine Famine 1932-33 Meta-regression' provides details on conducting the meta-regression using the R package ‘metafor’.   Funding Ukraine State complex program Diabetes Mellitus, project number 0106U000844 (M.K.). Holodomor Research and Education Consortium in Canada (L.H.L., O.W.). NIDI-NIAS Fellowship of the Royal Netherlands Academy of Sciences (L.H.L.). National Institute of Aging R01 AG028593 (L.H.L.). National Institute of Aging R01 AG06687 (L.H.L.).   Sharing/Access information Data sharing and use are unrestricted with acknowledgement of the original publication and listing of the funding sources as per the above. Researchers are encouraged to contact the Principal Investigators (PIs) for consultations on data structure and use as needed (L.H. Lumey, <a href="mailto:lumey@columbia.edu">lumey@columbia.edu</a>; Oleh Wolowyna, <a href="mailto:olehw@aol.com">olehw@aol.com</a>).

LAMASUS NUTS-level agricultural data derived from public FADN 1989-2009 (SGM) & 2004-2020 (SO)

This dataset comprises spatial and temporal agricultural data compiled from the Farm Accountancy Data Network (FADN) Public Database available at the FADN-region level and further disaggregated using Corine Land Cover (CLC) information about agricultural area. Processing to NUTS level: CLC data layers were used to overlay what is defined as "Agricultural areas" in CLC level 1classification (#2**) with FADN and NUTS regions. The overlay allows to calculate area-weighted shares and further to allocate FADN farm weights to the NUTS level. This allows the application of weights at the NUTS granularity. Please keep in mind, this is only possible under the assumption of heterogeneous farms within the FADN region.  File description: The dataset consists of eight files, corresponding to four different levels of NUTS coding (NUTS 0-3) according to the 2016 NUTS specification and each of those for the two different sampling periods.  FADN data from 2004 onwards, standard results calculated for farms grouped according to EU typology of agricultural holdings based on standard output (SO).  FADN data from 1989 to 2009, standard results calculated for farms grouped according to EU typology of agricultural holdings based on standard gross margin (SGM).  For each csv file, the following columns are included:  Identifier: <ol> <li>NUTS Code: The unique identifier for the NUTS (2016) region</li> <li>Year: The year of the data point</li> </ol> Variables:       3. weighting: number of farms represented       4. and after: SE standard result variables, for detailed description, please have a look at the accompanied xlsx: variable_description_zenodo.xlsx Source information: The raw data for the public Farm Accountancy Data Network (FADN) can be accessed through the official platform using the following link: <a href="https://agridata.ec.europa.eu/extensions/FADNPublicDatabase/FADNPublicDatabase.html" target="_new">FADN Public Database</a>. The CLC layers for the weighting of the spatial disaggregation can be accessed via the Copernicus homepage undern the following link: <a href="https://land.copernicus.eu/en/products/corine-land-cover">https://land.copernicus.eu/en/products/corine-land-cover</a>   <div> This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 50-57, belonging to section 3.6 Public FADN Data. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.</a> </div>

Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"

Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.

opencc-by-4.0Jun 2023View details →

Raw gel images accompanying the publication: Koralewska et al, NAR 2024, Short 2'-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production in vitro and in vivo, DOI 10.1093/nar/gkae284

A set of raw gel images used in the article: Koralewska et al. Short 2’-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production in vitro and in vivo, NAR 2024,  DOI 10.1093/nar/gkae284.

BeauAMP : processing and consolidation of open data on public procurement in France (2015-2023)

This accurate and comprehensive dataset encapsulates the main information published on the BOAMP website (the official journal for public procurement notices in France) from 2015 to 2023, enriched with the individual characteristics of contracting authorities and holders of public contracts. After converting the notices into a processed table, we use a machine learning algorithm to estimate the SIRETs (i.e. national identifiers) of the contracting parties, so that we can merge the open data on public procurement with individual information on public and private agents (size, legal status, main activity, geolocation...). Finally, we estimate the geolocation of foreign firms. The dataset contains about 300,000 public contracts and describes more than 1,000,000 interactions between approximately 16,000 public entities and 130,000 companies. It covers over 100 variables on the contract features, the outcome of the award procedure, the characteristics of contracting authorities and the characteristics of awarded firms.   See similar data from 2024 : https://zenodo.org/records/17187786

Source data for publication "Bacteria use exogenous peptidoglycan as a danger signal to trigger biofilm formation"

This dataset contains the source data for the figures in the following publication:  Title: Bacteria use exogenous peptidoglycan as a danger signal to trigger biofilm formation Authors: Sanika Vaidya, Dibya Saha, Daniel K.H. Rode, Gabriel Torrens, Mads F. Hansen, Praveen K. Singh, Eric Jelli, Kazuki Nosho, Hannah Jeckel, Stephan Göttig, Felipe Cava, Knut Drescher Journal: Nature Microbiology, 2025   Description of the dataset:  The data is organized by figures in the publication receferenced above. For each figure, there is a XLSX-file that contains the processed data and a ZIP-archive that contains the raw image data. The ZIP-archive also contains readme documents with more detailed descriptions for every set of raw image data.  Example: For Figure 1 in the main text of the publication, there following data are available: <ul> <li>raw data: Figure_01.zip</li> <li>processed data: Processed_Source_Data_Figure1.xlsx</li> </ul> Similarly, XLSX-files and ZIP-archives are available for the main text Figures 1-6. For the Extended Data (ED) Figures 1-10, there are XLSX-files available that present the data shown in each figure. Only some of the Extended Data Figures present results based on image data - therefore raw image data ZIP-archives are only available for ED Figures 3, 6, 7, 8, 9, 10.

Publications supported by ELIXIR and ELIXIR Italy (during the periods 2011-2023 and 2015-2023)

Publications supported by ELIXIR and ELIXIR Italy (during the periods 2011-2023 and 2015-2023), retrieved from EuropePMC (Datasome) and curated by ELIXIR Hub and ELIXIR Italy.

opencc-by-4.0Jul 2024View details →

Dataset for a publication: "PLLA honeycombs activated by plasma and high energy excimer laser for stem cell support"

The dataset accompanies the article "PLLA Honeycombs Activated by Plasma and High-Energy Excimer Laser for Stem Cell Support." It is organized into several subfolders, each corresponding to a different analytical method used in the study, with data presented in the manuscript. The main folder is structured as follows: <ol> <li>AFM</li> <li>Contact Angle</li> <li>Zeta Potential</li> <li>SEM</li> <li>EDS</li> <li>XPS</li> <li>Cytocompatibility</li> </ol> Each subfolder contains the relevant data associated with the specific analysis.   For more details, please read the README - Description of data and analysis informations_PS.txt file.     Dataset versions: There are no newer versions so far.

Data complementing the publication: "Does total column ozone change during a solar eclipse?"

Data published in this zip file complement the publication "Does total column ozone change during a solar eclipse?" by Germar H. Bernhard, George T. Janson, Scott Simpson, Raúl R. Cordero,  Edgardo I. Sepúlveda Araya, Jose Jorquera, Juan A. Rayas, and Randall N. Lind, which will be published in the journal "Atmospheric Chemistry and Physics". A DOI of the publication will be added to this meta data description when available. The DOI of the publication's pre-print (paper under review) is: https://doi.org/10.5194/egusphere-2024-2659 The contents of the zip file are organized in the following four subdirectories: - Figures: This directory contains the figures of the paper in PDF and PNG format plus the data used for plotting the figures. - GUVis-3511 Data Processor: This directory contains the software for processing the raw data collected during the solar eclipses described in the publication as well as ancillary data used for processing and manuals describing the software. - Limb darkening functions: This directory contains the functions expressing the change in the spectral irradiance during the eclipses discussed in the publication as a function of time and wavelength. - Raw data: This directory contains the raw data measured during the eclipses discussed in the publication. Each subdirectory and subdirectories nested therein contains "readme.txt" (in English) and "léeme_Espanol.txt" (in Spanish) files with further information of the contents of each subdirectory.

GWAS Summary Statistics for Publication: Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data

This dataset contains summary statistics for genome-wide association studies (GWAS) conducted on genomic features derived from off-target reads in whole-exome sequencing (WES) data. The study utilized tools like Seeing Beyond the Target (SBT) and ImReP to construct novel phenotypic features from unmapped reads in ~50,000 participants in the UK Biobank. Features include mitochondrial DNA (mtDNA) copy number, ribosomal DNA (rDNA) copy number (5S, 18S, 28S), immune repertoire metrics (e.g., T-cell receptor alpha diversity), and microvial genome load (viral and fungal). Summary statistics can be used for replication studies, meta-analyses, or further exploration of these phenotypes.