Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Dataset - Targeted social immunity is associated with a pathogen-specific erosion of individual immune function in the black garden ant

Datasets and code used to generate the figures. Files sorted by figure panel number. <a href="../api/records/12820609/draft/files/Videos_grooming_.zip/content" target="_blank" rel="noopener noreferrer">Videos_grooming_.zip</a> contains the original videos used for the grooming analysis.

Data from: Species-soil relationships across Amazonia: niche specificity and consistency in understory ferns

One of the two data files contains occurrence and abundance information for Adiantum and Lindsaea fern species in 1215 sampling units distributed across Amazonia. The other data file contains metadata and soil base cation concentration values. Explanations of the data columns are in the README.txt file.

Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains

Antibodies are a popular and powerful class of therapeutic due to their ability to exhibit high affinity and specificity to target proteins. However, the majority of antibody therapeutics are not genetically human, with initial therapeutic designs typically obtained from animal models. Humanisation of these precursors is essential to reduce immunogenic risks when administered to humans. To aid humanisation we developed <a href="https://github.com/oxpig/Humatch" target="_blank" rel="noopener">Humatch</a>, a computational tool designed to offer experimental-like joint humanisation of heavy and light chains in seconds. Humatch consists of three lightweight Convolutional Neural Networks (CNNs) trained to identify human heavy V-genes, light V-genes, and well-paired antibody sequences with near-perfect accuracy. We show that these CNNs, alongside germline similarity, can be used for fast humanisation that aligns well with known experimental data. Throughout the humanisation process, a sequence is guided towards a specific target gene and away from others via multiclass CNN outputs and gene-specific germline data. This guidance ensures final humanised designs do not sit `between' genes, a trait that is not naturally observed. Humatch's optimisation towards specific genes and good VH/VL pairing increases the chances that final designs will be stable and express well and reduces the chances of immunogenic epitopes forming between the two chains. Here we share the data used to train and evaluate Humatch and the CNN weights and germline likeness lookup arrays that guide its humanisation.

Data for "Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil"

This dataset contains soybean yield, soil property values, and apparent electrical conductivity (ECa) from two farms located in the South of Brazil. The data description and methods can be found in the paper: Bottega, E. L., Safanelli, J. L., Zeraatpisheh, M., Amado, T. J. C., Queiroz, D. M. de, & Oliveira, Z. B. de. (2022). Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil. In Agronomy (Vol. 12, Issue 6, p. 1390). MDPI AG. https://doi.org/10.3390/agronomy12061390 Abstract: Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p < 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca2+, Mg2+, SB, Al3+, H+ + Al3+, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca2+, Mg2+, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs accordingly.

Metaproteomics reveals age-specific alterations of gut microbiome in hamsters with SARS-CoV-2 infection

The gut microbiome's pivotal role in health and disease is well-established. SARS-CoV-2 infection often causes gastrointestinal symptoms and is associated with changes of the microbiome in both human and animal studies. While hamsters serve as important animal models for coronavirus research, there exists a notable void in functional characterization of their microbiomes with metaproteomics. In this study, we present a workflow for analyzing the hamster gut microbiome, including a metagenomics-derived hamster gut microbial protein database and a data-independent acquisition metaproteomics method. Using this workflow, we identified 32419 protein groups from the fecal microbiomes of young and old hamsters infected with SARS-CoV-2 . We showed age-specific changes in the expressions of microbiome functions and host proteins associated with microbiomes, providing further functional insight into the dysbiosis and aberrant cross-talks between the microbiome and host in SARS-CoV-2 infection. Altogether this study established and demonstrated the capability of metaproteomics for the study of hamster microbiomes.

GNSS deep SNR retrievals of marine atmosphere boundary layer (MABL) specific humidity

This folder contains 5 prediction files ended with *_v2.h5. These files can be loaded using the provided code "prediction_data_loader.py". All variables are in their respective physical units. The *.tgz file contains the training and validation codes as well as sample training and validation datasets from METOP-B satellite. Please refer to the paper for details. All variables had been normalized in the training and validation datasets so no real physical meaning attached. Reference: <a href="https://publications.copernicus.org/">Gong, J., Wu, D. L., Badalov, M., Ganeshan, M., and Zheng, M.: A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals, Atmos. Meas. Tech., 18, 4025–4043, https://doi.org/10.5194/amt-18-4025-2025, 2025.</a>   POC: Jie.Gong@nasa.gov 10/17/2024   Update on 08/27/2025: The final paper has been published on AMT. Please see updated reference information above. --- THE END ---

opencc-by-4.0Dec 2023View details →

Primary MS data and secondary data for: "Post-proline cleaving enzymes also show specificity to reduced cysteine"

raw LC-MS/MS data (Bruker Daltonics - timsTOF Pro and tims TOF SCP) and ESI-MS data (Bruker Daltonics - 15T ESI/MALDI FT-ICR MS) and related MASCOT generic files (.mgf) and search results (.csv) plus corresponding custom databases (*.fasta). Protein identifications were performed using MASCOT or PEAKS programs. Data were used to: <ul> <li>extract cleavage preferences of Clarity Ferm AnPEP, ProAlanase or Neprosin. Target samples were: protein mixture (BSA, cytC, bCA2, myoglobin, 14-3-3) or human serum from a healthy donor or HEK cell lysate or human insulin or oxidized bovine insulin beta chain</li> <li>profile protein content of Clarity Ferm AnPEP and quantify the proteins</li> </ul> Additional data are under Zenodo entry 10.5281/zenodo.13985598 Linked to the publication:  Postproline Cleaving Enzymes also Show Specificity to Reduced Cysteine. Kalaninová Z, Porta&scaron;iková JM, Jirečková B, Polák M, Nováková J, Kavan D, Novák P, Man P. Anal. Chem. 2024, 96, 48, 19084–19092 doi: 10.1021/acs.analchem.4c04277.

Online example database generated representing nuclear astrophysics models predictions of correlations between stable/stable abundances of specific isotopes.

Library of figures created using the SIMPLE code (Stellar Interpretation for Meteoritic data and PLotting). The SIMPLE stellar database includes 18 core-collapse supernova models with 3 different initial masses of 15, 20, 25 solar masses, all of solar metallicity and non-rotating stars. The 6 sets are the following: <ul> <li>Rauscher et al. 2002 [Ra02] (<a href="https://ui.adsabs.harvard.edu/abs/2002ApJ...576..323R/abstract">https://ui.adsabs.harvard.edu/abs/2002ApJ...576..323R/abstract</a>),</li> <li>Pignatari et al. 2016 [Pi16] (<a href="https://ui.adsabs.harvard.edu/abs/2016ApJS..225...24P/abstract">https://ui.adsabs.harvard.edu/abs/2016ApJS..225...24P/abstract</a>),</li> <li>Sieverdin et al. 2018 [Si18] (<a href="https://ui.adsabs.harvard.edu/abs/2018ApJ...865..143S/abstract">https://ui.adsabs.harvard.edu/abs/2018ApJ...865..143S/abstract</a>),</li> <li>Limongi & Chieffi 2018 [LC18] (<a href="https://ui.adsabs.harvard.edu/abs/2018ApJS..237...13L/abstract">https://ui.adsabs.harvard.edu/abs/2018ApJS..237...13L/abstract</a>),</li> <li>Ritter et al. 2018 [Ri18] (<a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.480..538R/abstract">https://ui.adsabs.harvard.edu/abs/2018MNRAS.480..538R/abstract</a>),</li> <li>Lawson et al. 2022 [La22] (<a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..886L/abstract">https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..886L/abstract</a>)</li> </ul> The figures can be divided into two types. The first shows the structure of the ejecta and the abundance of the selected isotopes. The layers are automatically detected using SIMPLE based on the abundances of the main fuels (H-1, He-4, C-12, O-16, Ne-20, Si-28) from the supernova model ejecta. The code names the different layers based on the schematic diagram in Schofield et al. 2022 (<a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.1803S/abstract">https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.1803S/abstract</a>).  Ni and Fe isotopes are plotted in the figures. The abundances shown include the radiogenic contribution from unstable isotopes. The SIMPLE code is designed to compare stellar data with measurements from meteorites. To achieve this, abundances in mass fractions need to be converted into isotopic ratios using specific units. See Lugaro et al. 2023 (<a href="https://ui.adsabs.harvard.edu/abs/2023EPJA...59...53L/abstract">https://ui.adsabs.harvard.edu/abs/2023EPJA...59...53L/abstract</a>) for details. For the specific case of Ni64 ratios, in comparison with model data we report the measured meteoritic anomaly by Steele et al 2012 (<a href="https://ui.adsabs.harvard.edu/abs/2012ApJ...758...59S/abstract">https://ui.adsabs.harvard.edu/abs/2012ApJ...758...59S/abstract</a>) as a continuous horizontal line. The same is done for the Fe54 ratios, with reference measurements by Hopp et al 2022 (<a href="https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717245H/abstract">https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717245H/abstract</a>).  In the database the abundance plots are identified as structure_<model reference>_<initial mass>_<element>_<decayed or undecayed>.png. In particular, the available reference model options are Ra02, Pi16, Si18, LC18, Ri18, La22;  the initial mass of the progenitors are 15, 20 or 25 (solar masses). The third part of the filenames are the plotted elements (in this case Ni or Fe) and then if they are decayed or undecayed. In this database we only consider the decayed species, which means that the radioactive isotopes whose decay can add to the abundance of the selected isotopes were considered. In this plots the x-axis represents the total mass from the core and the y-axis is the mass fraction on a logarithmic scale. For the slopes the same name scheme applies, but they are identified as slopes_<model reference>_<initial mass>_<element>_<decayed or undecayed>.png, and on the y-axis there are the slope values insteas of abundances.

Primary MS data and secondary data for: "Post-proline cleaving enzymes also show specificity to reduced cysteine" Part2

raw LC-MS/MS data (Bruker Daltonics - tims TOF SCP) and related PEAKS search results (*.csv) plus corresponding custom database (*.fasta). Protein identifications were performed using PEAKS programs. Data were used to: <ul> <li>extract cleavage preferences of Clarity Ferm AnPEP. The target sample was HEK cell lysate.</li> </ul> These data are related to https://doi.org/10.5281/zenodo.13938580.

Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behaviour

This work presents an analysis based on training AI models directly on devices to make personalized predictions tailored to individual usage patterns, ensuring that each user benefits from a personalized approach to battery management. By integrating these AI-based insights, mobile devices can proactively manage power consumption, improving battery performance and user satisfaction. This personalized, intelligent approach to battery management represents a significant advance in optimizing device efficiency and addresses the growing demand for longer-lasting mobile technology.

Life stage-specific effects of heat stress on spermatogenesis and oogenesis in Drosophila melanogaster

#Life stage-specific effects of heat stress on spermatogenesis and oogenesis in Drosophila melanogaster ##Authors Abhishek Meena, Alessio N. De Nardo, Komal Maggu, Sonja Sbilordo, Benjamin Eggs, Rawaa Al Toma Sho, Stefan Lüpold ## Citation of associated article Meena, A., Maggu, K., De Nardo, A.N., Sbilordo, S.H., Eggs, B., Al Toma Sho, R., Lüpold, S., 2024. Life stage-specific effects of heat stress on spermatogenesis and oogenesis in Drosophila melanogaster. Journal of Thermal Biology 125, 104001. https://doi.org/10.1016/j.jtherbio.2024.104001   ##Overview This dataset was collected to assess the impact of heat stress across various life stages on reproductive performance in Drosophila melanogaster.  The study focuses on stage- and sex-specific reproductive metrics such as: mating success, fertility, fecundity, hatching success, which for females were summed over four days and for males were assayed for four consecutive 24-hour periods, each with a different female.  The dataset is structured to analyze these outcomes at different temperatures and developmental stages, providing insights into sex- and stage-specific vulnerabilities to heat stress.  The analyses are performed in an R Markdown (.Rmd) file, using data stored in a CSV file ##Contents The dataset includes the following files: Rscript.Rmd: An R Markdown file containing code for analyzing sex- and stage-specific reproductive fitness based on the data in Data.csv. Data.csv: A CSV file with the raw data used for the analysis. ##Data Description Note: NA's in each data file represent missing data (data not available).  The Data.csv file contains the following columns: Unique.ID: Unique identifier for each experimental individual. SampleID: Identifier for each treatment group. Temperature: Thermal exposure temperature (measured in degrees Celsius) for 4 hours, with levels: 24.5°C, 28°C, 32°C, 36°C, and 38°C. Lifestage: Developmental stage during which thermal treatment was applied. Sex: Sex of the individual exposed to the thermal treatment. Day: Days post adult treatment exposure. Block: Experimental block identifier for randomized grouping. Total: Total number of eggs laid within 24 hours of oviposition. Hatched: Number of eggs that successfully hatched. Unhatched: Number of eggs that did not hatch (derived from the difference between Total and Hatched). ##Usage To analyze the dataset, open and run the respective R Markdown (Rmd) files in RStudio or any compatible R Markdown environment. The Rmd files contain all the necessary code to reproduce the analyses described in the overview.

opencc-by-4.0Apr 2022View details →

Table 2 in Demodex bialoviensis sp. nov. (Acariformes, Demodecidae) a new, specific parasite of the European bison Bison bonasus (Artiodactyla, Bovidae)

Table 2 Morphometric comparison between Demodex bialoviensis sp. nov. and Demodex bisonianus.<table><tbody><tr><th>Feature/Species</th><th>Demodex bialoviensis sp. nov.</th><th>Demodex bisonianus</th></tr></tbody><tbody><tr><th>Source</th><td>Present study</td><td></td><td>Kadulski and Izdebska</td></tr><tr><th></th><td></td><td></td><td>(1996)</td><td></td></tr><tr><th>Sex Sample size</th><td>Males (n =12)</td><td>Females (n =</td><td>Males</td><td>Females</td></tr><tr><th></th><td></td><td>34)</td><td>(n =</td><td>(n =20)</td></tr><tr><th></th><td></td><td></td><td>20)</td><td></td></tr><tr><th>Body total length</th><td>176</td><td>239</td><td>517, SD</td><td>534, SD</td></tr><tr><th></th><td>(158–198), SD</td><td>(200–268), SD</td><td>23 a</td><td>20 a</td></tr><tr><th></th><td>13</td><td>15</td><td></td><td></td></tr><tr><th>Body total width</th><td>31 (30–35), SD</td><td>35 (30–40), SD</td><td>63, SD 9</td><td>68, SD 9</td></tr><tr><th></th><td>2</td><td>2</td><td></td><td></td></tr><tr><th>Body length to</th><td>5.6:1</td><td>6.8:1</td><td>8.2:1b</td><td>7.9:1b</td></tr><tr><th>width ratio</th><td>(4.9–6.4:1), SD</td><td>(5.2–7.9:1), SD</td><td></td><td></td></tr><tr><th></th><td>0.6:1</td><td>0.6:1</td><td></td><td></td></tr><tr><th>Opisthosoma</th><td>62 (57–65), SD</td><td>65 (60–69), SD</td><td>68b</td><td>69b</td></tr><tr><th>length to body</th><td>2</td><td>2</td><td></td><td></td></tr><tr><th>length ratio (%)</th><td></td><td></td><td></td><td></td></tr><tr><th>Aedeagus length</th><td>21 (18–29), SD</td><td>–</td><td>33, SD 7</td><td>–</td></tr><tr><th>3</th></tr><tr><th>Vulva length</th><td>–</td><td>12 (10–17), SD</td><td>–</td><td>22, SD 2</td></tr><tr><th></th><td></td><td>2</td><td></td><td></td></tr></tbody></table>a Measurements were rounded to the nearest micrometer with respect to the original results (Kadulski and Izdebska, 1996).b Calculated from measurements of Kadulski and Izdebska (1996).

Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Public and Stakeholder Engagement

This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project. Aim: Alignment of RRI to advance the UN SDGs. Objectives: <ul> <li>Mapping the RSSR to the SDGs </li> </ul> Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable. The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders. Two documents were used for this task: <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>

Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Ethics and Ethical Governance

opencc-zeroJun 2021View details →

dryad40/100

Intra-specific variation in metal tolerance modulate competition between two marine diatoms

This project contains data for the manuscript Intra-specific variation in metal tolerance modulate competition between two marine diatoms. The experiments tests how different toxic concentrations of heavy metals modulates competition between an artificially assembled community of Baltic Sea diatoms (four clonal strains each of the two diatom species Skeletonema marinoi (SM) and Thalassiosira baltica (TB). Briefly we use toxic Dose Response curves (DRC) to model how inter and intra specific selection between strains is expected to be modulated under Cadmium, Copper and Silver stress. The stress level was set at the average 50% inhibition concentration (EC50) between all eight strains. Strains were mixed at even biomass based on Relative Chl a Fluorescence Units (RFU) and cultivated semi-continuously to maintain exponential growth. The growth of individual species was measured through microscopy observations and the photosynthetic capacity was monitored using Pulse Amplitude Modulation (PAM) fluorometry. A experimental design flow figure is included as a supplement. The complete functional set of scripts, statistical analysis, and graphical illustrations in R can be found on github: https://github.com/Bearstar85/Selection_Competition_2021

Source-specific light absorption by carbonaceous components in the complex aerosol matrix from yearly filter-based measurements

Data presented in the main figures of the peer-reviewed journal article "Source-specific light absorption by carbonaceous components in the complex aerosol matrix from yearly filter-based measurements" by Moschos et al. (2021) in Atmospheric Chemistry and Physics (https://doi.org/10.5194/acp-2020-1293).

Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach

Database used for the analysis of the manuscript entitled: Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach. The aim of the study was to investigate the cluster areas of asphyxia-associated neonatal mortality and to explore the per capita gross domestic product (GDP) as an associated risk factor in São Paulo State (SP), Brazil.

opencc-by-4.0Aug 2021View details →

Sanger sequencing traces of specific exons of the Sm.TRPM_PZQ gene from schistosome field samples

Praziquantel (PZQ) is the only drug available to treat schistosomiasis, which is caused by schistosome blood flukes. In Schistosoma mansononi, the transient receptor potential (TRP) channel Sm.TRPMPZQ is strongly suspected to be the target of PZQ. Our <a href="https://doi.org/10.1101/2021.06.09.447779">genetic analysis of S. mansoni response to PZQ</a> revealed a QTL on its chromosome 3 which contains the Sm.TRPMPZQ gene, strongly suggesting that Sm.TRPMPZQ could be responsible for PZQ resistance. Therefore, understanding the natural variation in this gene and identifying potential resistance alleles will be a valuable tool for monitoring mass treatment programs aimed at schistosomiasis elimination. We investigated our schistosome collection to examine mutations present in Sm.TRPMPZQ in natural schistosome populations. We analyzed exome sequencing data from 259 miracidia, cercariae or adult parasites from 3 African countries (Senegal, Niger, Tanzania), the Middle East (Oman) and South America (Brazil). We were able to sequence 36/41 exons of Sm.TRPMPZQ from 122/259 parasites on average (s.e. = 18.65). We identified several mutations in critical areas of the channel. However, these mutations were supported by a limited number of reads only and required confirmation by Sanger sequencing. The present dataset corresponds to the sequencing effort done on specific exons which carried the mutations to be confirmed. We generated PCR products which were sequenced on on ABI sequencer using Eurofins Genomics services. The SCF files were then analyzed using PolyPhred (see manuscript for details about PCR conditions and data analysis). The trace files are available in the traces folder. Each filename carries a barcode which corresponds to a combination of sample, exon, and primer. All the combinations and corresponding barcodes are listed in the barcode_list.tsv file. Table header details of the barcode list: <ul> <li>Sample: the name of sample. The sample coding is as follows: species.country_patientID. BR: Brazil, SN: Senegal, NE: Niger, TZ: Tanzania, OM: Oman.</li> <li>Exon: the exon targeted. The exon number corresponds to the exon number of isoform 5 and not the exon number of the gene.</li> <li>Barcode: the barcode provided by Eurofins Genomics.</li> <li>Primer: the primer used for sequencing. F: forward, R: reverse.</li> </ul>

opencc-by-4.0Oct 2020View details →

Figure 4 in Specifics of life cycle and damage of Oligonychus ununguis (Acari: Tetranychidae) on introduced species of coniferous plants in conditions of megalopolis

Figure 4. Relationship between the start of mass hatching of O. ununguis larvae and HC values at air temperature higher than 10 ℃.