Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Variant, Metabolite and Source Data for: Population genomics uncover loci for trait improvement in the indigenous African cereal tef (Eragrostis tef)

These files contain the variant and metabolome for a collection of 220 tef (Eragrsotis tef) accessions from an ethiopian diversity panel. The accessions were assembled and managed by the Ethiopian Institute of Agricultural Research (EIAR, Ethiopia). The variant data was produced at the John Innes Centre (UK). The metabolome data was produced at Aberystwyth University (UK). These dataset are described in Jones et al. (2024), bioRxiv, https://doi.org/10.1101/2024.09.30.615331. The source data for main figures in the publication are also included. The submission contains <ol> <li>EIAR_filtered.vcf.gz: This is the variant data obtained from alignment of Illumina reads from all 220 teff accessions to the reference assembly of tef (Dabbi).  Low quality variants were filtered out. This variant data was used for constructing the phylogenetic relationship between the accessions. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li>pooled_EIAR_filtered.vcf.gz: After the phylogentic analysis described above, reads from accessions that were found to be genetically redundant were pooled before variant calling. This file was used for the SNP GWAS analysis. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li> Metabolite_Profile.xlxs (source data for Figure 5): This file contains m/z feature intensities from untargeted metabolite fingerprinting using Flow Infusion Electrospray High-resolution Mass Spectrometry (FIE-HRMS). The sample names contains a combination of Location code and Plot number in Supplementary Table S10 e.g AT plot 1, CD plot 1, DZ plot 1, where AT, CD and DZ represent Alem Tena, Chefe Donsa and Debre Zeit, respectively. The data was used for the partial least squares discriminant analysis and differentially accumulated metabolites analysis presented in Figure 5.</li> <li>Source data: Numerical source data for graphs and charts in Figures 3 - 7.</li> <li>Tsedey TT2 Sequence from Improved Assembly: The 4A and 4B sequences around the TT2 orthologue in tef from the improved PacBio-based chromosome-scale assembly of tef. These sequences were used for plotting the LTR Copia alignments presented in Supplementary Figure 9. We thank Corteva for pre-publication access to this improved Tsedey genome assembly.</li> </ol>

opencc-by-4.0Nov 2024View details →

Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states - raw count data set - RNAseq

Raw count data of the RNAseq analysis of a project and manuscript under the title "Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states". The header of the table includes the subject identifiers except the first column "genes". The latter column includes all assessed gene identifiers.

opencc-by-4.0Dec 2022View details →

ShareTrait: a data portal for making trait data interoperable and reusable

Scope This dataset provides the SQL version of the ShareTrait database, corresponding to ShareTrait version 1.2.0. It represents the structured database equivalent of the previously published dataset, ShareTrait: a data portal for making trait data interoperable and reusable, version 1.0.0 (Zenodo, July 12, 2023, <a href="https://doi.org/10.5281/zenodo.8138904" target="_new" rel="noopener">DOI: 10.5281/zenodo.8138904</a>). This version includes the database file, an SQL query, and the resulting output file generated from the SQL query.  Dataset content The repository provides: <ul> <li>ShareTrait-database-v1.2.0.db : the database file.</li> <li>master-query-all.sql : SQL query used for reconstructing the dataset.</li> <li>master-query-output.csv : SQL query output file .</li> <li>ShareTrait-dataset-database-mapping.csv : mapping of attribute fields from the SQL query output to the corresponding attributes in <a href="/records/8138904/files/ShareTrait_MetaData_v1.0.0.csv?download=1">ShareTrait_MetaData_v1.0.0.csv</a>, with relevant column name = "Column_name", version 1.0.0 (<a href="https://doi.org/10.5281/zenodo.8138904" target="_new" rel="noopener">DOI: 10.5281/zenodo.8138904</a>)</li> </ul> Access and usage Files and documentation are available on GitHub: <a href="https://github.com/ShareTraitProject" target="_new" rel="noopener">ShareTrait Project</a>. Users can execute the SQL query by using either SQLiteStudio or SQLite3. Detailed instructions are provided in this repository README. Citation When using this dataset, please provide the following citation: Leiva, F., Ellers, J., Berg, M. P., Cuxart-Erruz, R., Barneche, D., Blackburn, T., Castañeda, L. E., Chown, S., Gaitán-Espitia, J. D., Gebauer Mery, P. H., Gomez Isaza, D., Hardy, I., Hermaniuk, A., Hirst, A., Jorissen, S., Keasar, T., Koene, J. M., Le Lann, C., Martorelli, I., Molinet C., Niklitschek E.J., Oliveira B., Olivier B., Orizaola G., Pilakouta N., Shameer K.S., Shokri M., Stoks R., Tougeron K., Tuni C., van de Pol I.L.E., van Dis N.E., Visser B., Vogels J., White C.R., Wu N., and Verberk W. (2025). ShareTrait: a data portal for making trait data interoperable and reusable (version 1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14826294" target="_new" rel="noopener">https://doi.org/10.5281/zenodo.14826294</a> For further information, please contact: <a rel="noopener">i.martorelli@vu.nl</a>

opencc-by-4.0Jun 2018View details →

Data from: Padfield et al. (2016) Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters.

This repository provides the data from the TPC and logistic growth curves from the paper: Padfield, D., Yvon‐Durocher, G., Buckling, A., Jennings, S., & Yvon‐Durocher, G. (2016). Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters, 19(2), 133-142. metadata.pdf gives a more detailed explanation of the data.

opencc-by-4.0Jun 2023View details →

Supplementary data: Agro-morphological and molecular characterization reveal deep insights in promising genetic diversity and marker-trait associations in Fagopyrum esculentum and F. tataricum

Our study focuses on the global/European buckwheat germplasm collected as part of the ECOBREDD project. The potential of this highly diverse collection for organic buckwheat breeding was evaluated at two complementary levels: phenotypic and genetic. Here, we characterized the phenotypic and genetic diversity of a global collection of the two cultivated buckwheat species Fagopyrum esculentum and F. tataricum (190 and 51 accessions, respectively) using 37 agro-morphological traits and 24 SSR markers (Simple Sequence Repeats) (see publication and info sheet of the data).

openCC BYMay 2024View details →

CoRRE Trait Data: A collection of 17 categorical and continuous traits for more than 4000 grassland species worldwide

In our changing world, it is critical to understand and predict plant community responses to global change drivers. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands, however their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is incredibly sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Database, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The database contains complete categorical trait records for all 4,079 plant species, obtained from a comprehensive literature search. Additionally, the database contains nearly complete coverage (99.97%) of species mean values for continuous traits for a subset of 2,927 plant species, predicted from observed trait data drawn from TRY and a variety of other plant trait databases using Bayesian Probabilistic Matrix Factorization (BHPMF) and multivariate imputation using chained equations (MICE). These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide.

openCC (other)Nov 2024View details →

Understory percent cover, plant traits, canopy LAI, PAR, temperature, and soil moisture data at multiple time points for sites in the burn chronosequence and Indian Point forest at the University of Michigan Biological Station, Pellston, MI (2022-2023)

Community ecology has sought to understand the mechanisms by which plant communities are assembled through time and space. One prominent way to address how communities are assembled is by quantifying functional traits. While there is a tremendous body of literature on functional traits, debate persists about how to account for variation in measured traits. For example, intraspecific trait variation (ITV) can be equal to or greater than interspecific trait variation and ITV has also been found to vary greatly across years. Therefore, there is a need to account for variability in functional trait measures among and within species and through time to improve our understanding of community assembly. Chronosequences are a powerful tool to address temporal changes in community dynamics, however, the inclusion of understory plants in forest chronosequence studies is still relatively uncommon. Previous chronosequence studies have been primarily performed in grasslands or in a limited subset of forest types, so further work is needed in understory plant traits across other ecosystems and climates to improve trait-based understanding of understory plant communities through time. Additionally, because plant traits change as ecosystems age, community interactions are likely to change with ecosystem age. Interactions of particular interest are herbivory, arthropod predation, and the influence of plant traits on arthropod diversity.

openCC (other)Dec 2024View details →

Environmental, community and trait data of small water bodies in Zijin Mountain, Nanjing, Jiangsu, China, 2022

Small water bodies (SWBs) are vulnerable to drought and play a vital role in the conservation of aquatic biodiversity. Currently climate change is intensifying the seasonal drought of SWBs in monsoonal east Asia. However, little is known about the response of benthic macroinvertebrates of small ponds and streams that simultaneously suffer from climate-induced extreme drought. This study aimed to explore the taxonomic and functional response of macroinvertebrates in ponds and streams, either respectively or jointly, to extreme summer drought. We calculated taxonomic and functional diversity indices of communities in 11 streams and 12 ponds across three seasons: spring, summer and winter in 2022. We performed a permutational multivariate analysis of variance, Moran’s eigenvector maps, Moran Spectral Randomization based variation partitioning and convex hull analysis of trait space to examine temporal compositional and functional, as well as trait changes, and the contributions of environmental and spatial factors in shaping communities. The responses of taxonomic and functional diversity in ponds and streams were contrasting during the summer drought. Ponds showed increased taxonomic richness (TR), functional richness (FRic), functional richness (FRed) and trait space volume, while streams experienced decreased TR, FRic and trait space volume but increased FRed. The taxonomic and functional increases of ponds were driven by an influx of generalist taxa from streams, while the increase of FRed in streams resulted from the loss of species with strong dispersal and lentic adaptation traits. Dispersal played a more significant role than environmental filtering in shaping community structure during the drought, especially for streams lacking hydrological connectivity. This study provides the first insights into the complex response of macroinvertebrates to summer drought of SWBs in east Asia monsoonal region. Our results underscore the refuge effect of ponds during summer

openCC (other)Feb 2020View details →

American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013

"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."

openCC (other)Sep 2025View details →

Periphyton Abundance and Structural Traits, Diatom Taxa Relative Abundance, and Associated Environmental Data from Samples Collected from the Greater Everglades, Florida, USA from September 2005 - ongoing

This data package contains benthic algae (periphyton) and environmental data collected annually during the wet season between 2005 and 2021 from sites distributed throughout the greater Everglades ecosystem. This project is part of the Comprehensive Everglades Restoration Program's Monitoring and Assessment Plan (CERP MAP) intended to document baseline variability in periphyton attributes for assessing the effectiveness of restoration projects. A total of 200 primary sampling units (PSU) of 800 m x 800 m are nested in 32 landscape units (LSU) and each year, random coordinates are 'drawn' within each PSU and one draw is visited in each sampleable PSU. Sampled periphyton is processed for aggregate structural traits (i.e., biomass, chlorophyll-a, organic content, and phosphorus concentration) and for diatom taxa. For diatoms, slides are prepared, and at least 500 frustules are enumerated and identified to the lowest possible taxonomic resolution per slide. Taxon abundances are then relativized to the total count. These data accompany environmental and spatial data for each sampled draw. In addition to the CERP MAP data, this dataset also includes data on the same variables collected from up to 21 primary sampling units in the Broward County Water Preserve Area beginning in 2020. The data in this package replace and supersede those in package knb-lter-fce.1210 (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1210).

opencc-by-4.0Nov 2020View details →

Supporting data for "The methylome of Biomphalaria glabrata and other mollusks: enduring modification of epigenetic landscape and phenotypic traits by a new DNA methylation inhibitor"

Methylome of the fresh water snail Biomphalaria glabrata. DNA was extracted from the feet of 10 individuals of B. glabrata originally isolated from Brazil. These snails have been cultivated in the laboratory since 1960. Tissue were grinded at 4°C and incubated in 1 ml volume of lysis buffer (20 mM TRIS pH 8; 1 mM EDTA; 100 mM NaCl; 0.5% SDS), with 0.3 mg of proteinase K at 55°C for 1 night. Afterwards, lysate was purified with phenol-chloroform and DNA was isopropanol precipitated. The extracted DNA (around 138ng/µL) was poled in equivalent amounts and Whole Genome Bisulfite Sequencing was done by GATC-biotech (www.gatc-biotech.com). The principle of this treatment is to convert non-methylated cytosines of gDNA into deoxy-uracil, whereas methylated cytosines remain intact. WGBS was done according to the Lister protocol  (sequence 2 forward strands only). The reference genome (Biomphalaria-glabrata-BB02_SCAFFOLDS_BglaB1.fa) and annotation (Biomphalaria-glabrata-BB02_BASEFEATURES_BglaB1.3.gff3) used in this project are available on VectorBase (https://www.vectorbase.org/). To align our short reads, we chose to use two specific bisulfite mapping tools, BSMAP 1.0.0 (https://code.google.com/p/bsmap/) and Bismark 0.10.2 (www.bioinformatics.babraham.ac.uk /projects/bismark/), to compare their efficiency and convenience to finally work with the more suitable one on our datasets. IGV (Interactive Genomics Viewer, https://www.broadinstitute.org/igv/) was used to visualized final alignments. BSMAP performed better than Bismark and was used for downstream analyses. Without default parameters alignement efficiency for BSMAP is 47.1%, allowing for 2 mismatches increases it to 55.6%. Methylation occurs predominantly in CpGs. (C methylated in CpG context: 12.4%, C methylated in CHG context: 0.5%, C methylated in CHH context: 0.5%) The major part of CpG sites, 95.7% were unmethylated, of the remaining 4.3% of CpG sites around 3.8% had low methylation, and 0.5% were completely methylated. Methylation is of the mosaic type. Methylation is relatively low with 1.2% of total cytosines. Our analyses suggested that conserved genes and genes with stable expression are localized in high methylated regions of the genome. Finally, we see that repetitive sequences were predominantly situated in low methylated regions of B. glabrata.  Wiggle files were generated for CpG pairs only. Produced at IHPE (http://ihpe.univ-perp.fr/)

opencc-by-4.0Dec 2020View details →

Data from: Consistent trait-environment relationships within and across tundra plant communities

A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait-environment relationships are generalisable across plant communities and spatial scales. We collected data from 6720 plots and 217 species across four distinct tundra regions from both hemispheres. We combine this data with over 76000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH, and potential solar radiation. Results revealed strong, consistent trait-environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems. Kemppinen, Niittynen, le Roux, Momberg, Happonen, Aalto, Rautakoski, Enquist, Vandvik, Halbritter, Maitner & Luoto (2021). Consistent trait-environment relationships within and across tundra plant communities. Nature Ecology and Evolution These are the data and codes from Kemppinen et al. (2021).

opencc-by-4.0Oct 2024View details →

Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"

<div> <div> <div> <div> Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a> Suppl. File 1: Protein Family Clusters. Suppl. File 2: Cluster GO enrichments/depletions. Suppl. File 3: The raw ID-CBR data with annotations. Suppl. File 4: ID-CBR Cluster membership. Each file has an explanatory header.    </div> </div> </div> </div>

opencc-by-4.0Aug 2024View details →

Data for manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry

Support manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry.  Dataset includes 1)  model and analysis, and 2)  supplemental data.  In the "model_analysis" file, we include the ecosys model source code, the modeling runs, and the modeling results. The detailed introduction is in README.md file.    Acknowledgments We thank the EMERGE Biology Integration Institute Coordinators (members listed in Supplementary Information) for project guidance and management. This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070 (V.I.R., R.K.V., S.R.S., M.B.S., E.L.B., and the EMERGE Coordinators). Additional support for individual contributors included the following. Z.L. was additionally supported by Lawrence Livermore National Laboratory under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344. W.J.R. was supported by the Belowground Biogeochemistry Scientific Focus Area and U.K. was supported by the Watershed Function Science Area, both funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. G.L.M. was supported by the LLNL "Microbes Persist" Soil Microbiome Scientific Focus Area SCW1632 and an associated KBase award SCW1746. N.J.B. was supported by the US Department of Energy, Office of Science (BER), Early Career Research Program (#FP00005182). B.J.W. was supported by an Australian Research Council Future Fellowship (#FT210100521). J.T. was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory.  We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231).  Full list of the EMERGE Biology Integration Institute Coordinators and Affiliations Eoin L. Brodie1,2, Sarah C. Bagby3, Jeffrey P. Chanton4, Jessica G. Ernakovich5, Regis Ferriere6,7, Suzanne B. Hodgkins8, William J. Riley1, Virginia I. Rich8,9, Scott R. Saleska6, Matthew B. Sullivan8,9,10, Ruth K. Varner11, Gene W. Tyson12, Malak M. Tfaily13, Ahmed A. Zayed8,9  1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory; Berkeley, CA 94720, USA. 2Department of Environmental Science, Policy and Management, University of California; Berkeley, CA 94720, USA. 3Department of Biology, Case Western Reserve University; Cleveland, OH, USA, 44106 4Earth Ocean and Atmospheric Sciences, Florida State University; Tallahassee, FL, USA 5Department of Natural Resources and the Environment, University of New Hampshire; Durham, NH, USA 03824 6Department of Ecology and Evolutionary Biology, University of Arizona; Tucson, AZ, 85721, USA 7Institut de Biologie de l’ENS, Université Paris Sciences & Lettres; Paris, 75005, France 8Department of Microbiology, The Ohio State University; Columbus, OH, USA, 43210 9Center of Microbiome Science, The Ohio State University; Columbus, Ohio 43210, USA. 10Department of Civil, Environmental and Geodetic engineering, The Ohio State University; Columbus, Ohio 43210, USA. 11Department of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New Hampshire; Durham, NH 03824, USA. 12Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology (QUT), Translational Research Institute; Woolloongabba, QLD, Australia 13Department of Environmental Science, University of Arizona; Tucson, AZ, 85721, USA

opencc-by-4.0Dec 2021View details →

Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective

Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. Functional Ecology, 2022. We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.