Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

4,624

datasets available to search

ShareScore release 0.9.0

Dataset results

4,624 results for “richness”

Learn how ShareScore rates datasets ↗

zenodo44/100

Rich subgroups in groups of order up to 2000

This file contains information about nontrivial rich subgroups in groups G of order up to 2000, i. e., subgroups H with the property that the restriction of each character of G to H has a trivial constituent. Rich subgroups are studied in the paper <a href="https://arxiv.org/abs/2407.08040" target="_blank" rel="noopener">On Frobenius graphs of diameter 3 for finite groups</a>. The DOI for the data file is <a href="../records/12731413" target="_blank" rel="noopener">10.5281/zenodo.12731413</a>. The contents of the file is an array of length 3. <ul> <li>The first entry is this description, an array of strings.</li> <li>The second entry is an array that lists the isomorphism types of the groups G; each entry has the format `[[n,i],[m1,m2,...,mk]]` meaning that G can be obtained as i-th group of order n in the <a href="https://gap-packages.github.io/smallgrp/" target="_blank" rel="noopener">small groups library</a>, and the representatives of the k conjugacy classes of rich subgroups in G have orders |m1|, |m2|, ..., |mk|; mj is positive if the j-th class of rich subgroups consists of diameter three subgroups of G, and negative otherwise.</li> <li>The third entry is an array that lists the isomorphism types of those groups G that are minimal w.r.t. inclusion with the property that they contain a rich subgroup; each entry has the format `[[n1,i1],[n2,i2],...,[nk,ik]]` meaning that the i1-th group of order n1, the i2-th group of order n2, ..., the ik-th group of order nk are minimal and that they are isoclinic.</li> </ul>

opencc-by-4.0Jul 2024View details →

zenodo44/100

Herbarium specimen image of Liparis loeselii (L.) Rich., part of the collection of Natural History Museum London

opencc-zeroNov 2018View details →

zenodo44/100

Herbarium specimen image of Anacamptis pyramidalis (L.) Rich., part of the collection of Natural History Museum London

opencc-zeroNov 2018View details →

zenodo44/100

Herbarium specimen image of Cephalanthera rubra (L.) Rich., part of the collection of Natural History Museum London

Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published. Content of this deposition: - A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values. - A JPEG image file of the scanned herbarium sheet. - A lossless TIFF image from which the JPEG image has been derived. - Two PNG files containing segmented image overlays of the scanned herbarium sheet. The _all extension indicates that all labels, color charts and pieces of text have received a different color against a black background color. The _sel extension indicates that these elements are white if they're barcode labels, yellow if they're color charts and red if they're anything else.

opencc-zeroNov 2018View details →

zenodo44/100

Herbarium specimen image of Gymnadenia odoratissima (L.) Rich., part of the collection of Natural History Museum, University of Tartu

opencc-zeroNov 2018View details →

zenodo44/100

Herbarium specimen image of Asplenium radicans var. cirrhatum (Rich. ex Willd.) Rosenst., part of the collection of National Museum of Natural History, Paris

opencc-zeroNov 2018View details →

zenodo44/100

Annotation of Phytozome V12 protein plant sequences using the ragp pipeline for hydroxyproline-rich glycoprotein mining

Hydroxyproline aware annotation of hydroxyproline-rich glycoprotein (HRGP) sequences was performed on sequence data from 62 plant proteomes obtained from Phytozome database (<a href="https://phytozome.jgi.doe.gov/pz/portal.html">https://phytozome.jgi.doe.gov/pz/portal.html</a>, version 12) using the ragp R package (<a href="https://github.com/missuse/ragp">https://github.com/missuse/ragp</a>, version 0.3.0.0001).  In each archive a single comma separated value table (.csv) is present along with a README.txt file describing the contents of the corresponding .csv file. The archives are: - phytozome_V12.tar.gz - sequences from 62 plant proteomes (phytozome V12) with a total of 2797062 protein sequences. - phytozome_V12_phobius.tar.gz -  Signal peptide prediction using Phobius (<a href="http://phobius.sbc.su.se/">http://phobius.sbc.su.se/</a>) on sequences present in phytozome_V12.tar.gz. - phytozome_V12_signalp.tar.gz -  Signal peptide prediction using SignalP 4.1 (<a href="http://www.cbs.dtu.dk/services/SignalP-4.1/">http://www.cbs.dtu.dk/services/SignalP-4.1/</a>) on sequences present in phytozome_V12.tar.gz. - phytozome_V12_targetp.tar.gz - Signal peptide prediction using TargetP 1.1 (<a href="http://www.cbs.dtu.dk/services/TargetP/">http://www.cbs.dtu.dk/services/TargetP/</a>) on sequences present in phytozome_V12.tar.gz. - phytozome_V12_predict_hyp.tar.gz - Probability of proline hydroxylation for each proline from 266135 protein sequences which were predicted to be secreted by a majority vote  (using Phobius, SignalP 4.1 and TargetP 1.1). - phytozome_V12_maab.tar.gz - Motif and amino acid bias (MAAB) classification of hydroxyproline-rich glycoproteins performed on 266135 protein sequences which were predicted to be secreted by a majority vote  (using Phobius, SignalP 4.1 and TargetP 1.1). The number of predicted hydroxyprolines in each sequence is also indicated (based on predictions provided in phytozome_V12_predict_hyp.tar.gz). - phytozome_V12_scan_ag.tar.gz. - Hydroxyproline aware arabinogalactan motif scan performed on 266135 protein sequences which were predicted to be secreted by a majority vote  (using Phobius, SignalP 4.1 and TargetP 1.1). Hydroxyproline predictions are provided in phytozome_V12_predict_hyp.tar.gz.  - phytozome_V12_scan_ag_hmmscan.tar.gz - Detection of domains in a subset of protein sequences which were found to contain arabinogalactan motifs (a subset of phytozome_V12_scan_ag.tar.gz). The list of the 62 plant species is provided in phytozome_V12.tar.gz README.txt. For questions contact mdragicevic@ibiss.bg.ac.rs.

opencc-by-4.0May 2019View details →

zenodo44/100

Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"

These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a> (Gagliano+2024, submitted).  The contents of the data files are detailed in the file ReadmeMESA.txt. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication.

opencc-by-4.0Nov 2024View details →

zenodo44/100

Data from: Moth species richness and diversity decline in a 30-year time series in Norway, irrespective of species' latitudinal range extent and habitat

Data from: Burner, R., V. Selås, S. Kobro, R. Jacobsen, A. Sverdrup-Thygeson. 2021. Moth species richness and abundance decline in a 30-year time series, irrespective of species’ latitudinal range extent and habitat. Journal of Insect Conservation   Current contact info for corresponding author: Ryan C. Burner, rburner[at]usgs.gov   These data consist of a 30-year time series (1984 to 2013) of moth captures from a single site in southeast Norway, along with trait data for many of the species and climate data for the site. The moths were collected and identified by Sverre Kobro for the entire 30-year period and we are grateful for his efforts.    Abstract from manuscript: Introduction Insects are reported to be in decline around the globe, but long-term datasets are rare. The causes of these trends are elusive, with land use change and climate change among the top candidates. Yet if species traits can predict rates of population change, this can help identify underlying mechanisms. If climate change is important, for example, northern species may decline as southern species expand. Land use changes, however, may impact species that rely on certain habitats. Aims and Methods We present 30 years of moth captures (comprising 85,149 individuals of 885 species) from a site in southeastern Norway to test for population trends that are correlated with species traits. We use time series analyses and joint species distribution models combined with local climate and habitat data. Results and Discussion Species richness and abundance declined by 10.1% and 13.8% per decade, respectively. Capture rates declined for 19% of species during this time as well, though 6% have increased. Annual summer weather is correlated with annual rates of abundance change for many species. But, opposite to a general expectation, many species in our study responded negatively to increasing summer temperatures. Surprisingly, neither species’ northern range limits nor the habitat in which their primary food plants grow are strong predictors of their rates of change, or their responses to climatic factors. However, species with more southerly distributions are less likely to be declining. Complex and indirect effects of both land use and climate change may play a role in these declines. Implications for insect conservation Our results provide additional evidence for long-term declines in insect abundance. The multifaceted causes of population changes may limit the ability of species traits to reveal which species are most at risk.     ACKNOWLEDGEMENTS Thanks to J. Fjelddalen, who helped with geometrid moth identifications. This project was supported by internal funding from the Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences.

opencc-by-4.0Dec 2020View details →

zenodo44/100

Dataset from the paper: "RR Lyrae From Binary Evolution: Abundant, Young and Metal-Rich"

The two files contain the Tables presented in Appendix B of Bobrick & Iorio et al. (2024, MNRAS, 527, 12196–12218) (https://ui.adsabs.harvard.edu/abs/2024MNRAS.52712196B/abstract) in CSV format. # V3 updates - New columns added: GRRL, Gcomp, RRRL, Rcomp, and Teffcomp. These columns are not included in the published paper tables. - The columns G and BP_RP were previously described as the Gaia G magnitude of the RRL, but they actually represent the G magnitude and BP–RP color of the entire system. - The previous description of the columns was missing the PorbRRL entry. - A new file, TableB2_SingleMadeBinaryRRL_V3.csv, has been added to replace the previous version, which had mismatched columns and some empty fields. - The README now reports the format for both tables. # Tables There are two tables included: ## TableB1_BinaryMadeRRL_V3.csv This table contains the systems reported in Table B1 of the paper, with additional columns (marked with a +). ### Columns: - **Age**: Age of the system since the zero-age main sequence [Myr] - **GBin**: Galactic bin from the Galactic model (Table 1 in the paper)     - TD1: Thin Disc – Bin 1       - TD2: Thin Disc – Bin 2       - TD3: Thin Disc – Bin 3       - TD4: Thin Disc – Bin 4       - TD5: Thin Disc – Bin 5       - TD6: Thin Disc – Bin 6       - TD7: Thin Disc – Bin 7       - B: Bulge       - TKD: Thick Disc       - H: Halo   - **Mproj**: Progenitor ZAMS mass of the RRL [Msun] - **Mcomp**: Progenitor ZAMS mass of the RRL companion [Msun] - **Porb_init**: Initial orbital period [days] - **feh**: [Fe/H] metallicity - **MRRL**: RRL mass [Msun] - **McompRRL**: Mass of the RRL companion [Msun] - **PorbRRL**: Current orbital period [days] - **McRRL**: Core mass of the RRL [Msun] - **LRRL**: Bolometric luminosity of the RRL [Lsun] - **Teff**: Effective temperature of the RRL [K] - **G**: Gaia G-band magnitude of the system as a whole [mag] - **BP_RP**: Gaia BP–RP color of the system as a whole [mag] - **GRRL**: Gaia G-band magnitude of the RRL [mag] + - **Gcomp**: Gaia G-band magnitude of the companion [mag] + - **RRRL**: Radius of the RRL [Rsun] + - **Rcomp**: Radius of the companion [Rsun] + - **Teffcomp**: Effective temperature of the companion [K] + --- ## TableB2_SingleMadeBinaryRRL_V3.csv This table contains the systems reported in Table B2 of the paper, with additional columns (marked with a +). ### Columns: - **Age**: Age of the system since the zero-age main sequence [Myr] - **GBin**: Galactic bin from the Galactic model (Table 1 in the paper)     - TD1: Thin Disc – Bin 1       - TD2: Thin Disc – Bin 2       - TD3: Thin Disc – Bin 3       - TD4: Thin Disc – Bin 4       - TD5: Thin Disc – Bin 5       - TD6: Thin Disc – Bin 6       - TD7: Thin Disc – Bin 7       - B: Bulge       - TKD: Thick Disc       - H: Halo   - **Mproj**: Progenitor ZAMS mass of the RRL [Msun] - **MRRL**: RRL mass [Msun] - **McompRRL**: Mass of the RRL companion [Msun] - **PorbRRL**: Current orbital period [days] - **feh**: [Fe/H] metallicity - **McRRL**: Core mass of the RRL [Msun] - **LRRL**: Bolometric luminosity of the RRL [Lsun] - **Teff**: Effective temperature of the RRL [K]

opencc-by-4.0Mar 2023View details →

zenodo44/100

"Hydration of a clay-rich unit on Mars, comparison of orbital data to rover data" supplementary data

MCMC results from individual DAN measurements from sols 1814 to 3069 used in this work with material classifications from Figure 11.

opencc-by-4.0Jun 2022View details →

zenodo44/100

Recovery of Lithium Carbonate from Dilute Li-Rich Brine via Homogenous and Heterogeneous Precipitation

An extensive experimental campaign on Li recovery from relatively dilute LiCl solutions (i.e., Li+ &sim; 4000 ppm) is presented to identify the best operating conditions for a Li2CO3 crystallization unit. Lithium is currently mainly produced via solar evaporation, purification, and precipitation from highly concentrated Li brines located in a few world areas. The process requires large surfaces and long times (18−24 months) to concentrate Li+ up to 20,000 ppm. The present work investigates two separation routes to extract Li+ from synthetic solutions, mimicking those obtained from low-content Li+ sources through selective Li+ separation and further concentration steps: (i) addition of Na2CO3 solution and (ii) addition of NaOH solution + CO2 insufflation. A Li recovery up to 80% and purities up to 99% at 80 °C and with high-ionic strength solutions was achieved employing NaOH solution + CO2 insufflation and an ethanol washing step.

opencc-by-4.0Aug 2022View details →

zenodo44/100

The mycobiome of the gut of willow wood borer, Xiphydria prolongata (Hymenoptera: Xiphydriidae): a rich source of rare yeasts

A high-throughput amplicon sequencing as a culture-independent approach was used to identify the gut mycobiome of the willow wood borer Xiphydria prolongata. The findings of this study are significance in terms of the insect-fungal interactions and indicate the unexpected richness of the mycobiome and the presence of many rare yeast species in the wood borer gut. A total of 40 fungal genera were found, and among them, only one endophytic fungi, Daldinia (Hypoxylaceae), has been previously reported in Xiphydria. Zygosaccharomyces siamensis is the most prevalent ascomycete species, while Rhodosporidiobolus colostri is the most abundant basidiomycetous yeast in X. prolongata. Some of the species identified in here was known as very rare fungus such as Skoua fertilis, Chaetomium nepalense, R. colostri and Vustinia terrae. This study is also the first report to S. fertilis and V. terrae in the insect gut flora. These funguses most likely aid in the digestion of lignocellulose in the gut of wood borer. Therefore, further researches are required to know the source of acquisition and functional role of these yeast and their industrial potential.

opencc-by-4.0Apr 2023View details →

zenodo44/100

Data from: Davison et al. (2023) Vegetation structure from LiDAR explains the local richness of birds across Denmark

Environmental and biodiversity data associated with the article: Davison et al. (2023) Vegetation structure from LiDAR explains the local richness of birds across Denmark, Journal of Animal Ecology. Bird richness and abundance at points across Denmark, with matched land cover and LiDAR structural data. Bird observations are a subset of the Common Bird Monitoring programme (DOF – Birdlife Denmark) and pooled from summer counts of 2014, 15, and 16. Bird functional group assignments and environmental data are from open access data sets (see below). <table> <tbody> <tr> <td>Data source</td> <td>Reference</td> </tr> <tr> <td>Danish Common Bird Monitoring programme</td> <td>Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</td> </tr> <tr> <td>EcoDes-DK15 LiDAR data set of Denmark</td> <td>Assmann, J. J., Moeslund, J. E., Treier, U. A., & Normand, S. (2022). EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark’s national airborne laser scanning data set. Earth System Science Data, 14(2), 823–844. https://doi.org/10.5194/essd-14-823-2022</td> </tr> <tr> <td>Pan-European land cover map of the year 2015 </td> <td>Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221, 583–595. https://doi.org/10.1016/j.rse.2018.12.001</td> </tr> <tr> <td>AVONET bird traits data</td> <td>Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</td> </tr> <tr> <td>Birds of the Palearctic - original source of trait data </td> <td>Cramp, S. (2006). The birds of the western Palearctic interactive. Oxford University Press and BirdGuides.</td> </tr> <tr> <td>Life-history characteristics of European birds - trait database</td> <td>Storchová, L., & Hořák, D. (2018). Life-history characteristics of European birds. Global Ecology and Biogeography, 27(4), 400–406. https://doi.org/10.1111/geb.12709</td> </tr> </tbody> </table>

opencc-by-4.0Dec 2022View details →

zenodo44/100

Three-component modelling of O-rich AGB star winds I. Effects of drift using forsterite – dataset

The data provided here include all parameter files, log files, and a set of the binary output files that are the basis for the publication in A&A. The file 'file_listing.txt' contains a complete list of files and directories in all gzipped tar files. Each individual gzipped tar file is formatted as follows:  Mm.m_Ll.ll_Ttttt.tar.gz where  m.m  :: the assumed mass of the model, in solar masses  l.ll :: The assumed luminosity, in log10(solar luminosities)  tttt :: The effective temperature of the star, in Kelvin. The contents of the tar files vary according to the model, but here is the general directory structure:  nodr/  :: non-drift / PC models  drift/ :: drift models  nodr/init  drift/init :: Initial model files created using John Connor. File suffixes are the following:  .par :: Plain-text parameter file that contains all parameters that are          different from the respective default value in the model.      Consequently, to see what parameters were actually used, it is      necessary to look in the log file (see below).  .bin :: Binary file that contains output of converged models. Each model is          stored in two versions, first the previous time step and then the      current time step (having access to the model code T-800, data of both      time steps are needed to restart model calculations at that time      step).          The initial model file only contains one model; where the previous      time step data are the same as the current time step data.          We provide a tool to read this file, see below.          Note! These files can get pretty large and are therefore only        available for a smaller number of the models in the Zenodo dataset.        Please ask the corresponding author for the missing files is the        need should appear.  .log :: Plain-text log file that shows the used model parameters and a number          of key properties for each converged model.      The encoding of this file is UTF-8.  .inf :: Plain-text secondary log file that contains the header of the          [primary] log file as well as timing information.      The encoding of this file is UTF-8.  .tpb :: Secondary binary file that contains a number of properties specified          at the outer boundary, typically for each consecutive time step.          We provide a tool to read this file, see below.  .lis :: Plain-text file with the iteration history. Unavailable here.  .liv :: Plain-text file with values specified for a number of properties at          each gridpoint. Unavailable here.  .inp :: Plain-text file that is used to launch a model; some are present.          This file is automatically generated by the tool that launches T-800      and is typically removed when T-800 launches. Unavailable here.  .eps :: Encapsulated PostScript file created by John Connor when calculating          the initial model. Model evolution structure - file endings before the suffix:  _rlx :: Files related to relaxing the T-800 calculations on the initial model          created by John Connor.  _exp :: Files related to expanding the initially compact model to using the          full radial domain.  _fix :: Files related to the intermediate stage where calculations are changed          from expansion to outflow.            _out :: Files related to the outflow stage of the calculations; this is what          you want to look at to see the wind evolution. Results in the paper      are calculated using these data.  Note! Some outflow stage calculations continue the evolution of the previous    set of files. The underlying reason for continued calculations is typically    that the calculated time interval is too short. Such files are typically    given the extension '_cont.lin_out', '_cont2.lin_out', etc. Stored data in the binary files:  The binary files (suffix '.bin') contain the full radial structure in the  following 10 (PC models) or 11 (drift models) primary variables:    mr: radius    mm: integrated [gas] mass    md: gas density    mu: gas velocity    me: internal energy    mj: radiative energy    mh: radiative flux    n0: dust moment, forsterite (Fo)    nm: number density of magnesium atoms    ns: number density of silicon atoms    v0: dust velocity, forsterite (only drift models)  Other properties are derived from these primary variables using auxiliary code  that isn't part of this dataset. Load files:   Two tools are provided here that can load the binary data files using the   Interactive Data Language (IDL):   sc_load_bin (for files with the suffix '.bin'):     Loads the full content of a T-800 binary file and returns a structure     with the data.   sc_load_tpb (for files with the suffix '.tpb'):     Loads the full content of a T-800 'tpb' binary file and returns a     structure with the data.     Note! Due to the way models run on clusters, this file is sometimes       incomplete; this happens when the model code T-800 is stopped as the       cluster-specific walltime is reached. If this is the case, it is       necessary to use the binary file instead, where data are saved       typically every 20:th time step.   Alternative tools for use with Python and Julia could be considered for   writing, but where not yet available when this dataset was made public.   Please contact the corresponding author for a current status on this issue.

opencc-by-4.0Jun 2023View details →

zenodo44/100

Dataset Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial Pseudomonas in the wheat rhizosphere

This dataset is related to the paper "Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial Pseudomonas in the wheat rhizosphere" (Garrido-Sanz et al., 2023, doi: 10.1186/s40168-023-01660-5) and contains the data obtained from bacterial competition asays and plant-growth measurements. Sequencing data used in this study has been deposited in the NCBI Sequence Read Archive (RSA) under the BioProject accession number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA948847">PRJNA948847</a>. The R script used to analyze the data generated in the paper is available at <a href="https://github.com/dgarrs/Pprotegens_proliferation_NatComs">GitHub </a>and <a href="https://doi.org/10.5281/zenodo.8322086">Zenodo</a>.

opencc-by-4.0Sep 2023View details →

zenodo44/100

Data and code for "Climate change shapes richness-evenness relationships in a subalpine grassland experiment"

Aggregated Biodiversity metrics derived from the biodiversity data collected from the ClimGrass experiment at the Agricultural Research and Education Centre (AREC) in Raumberg-Gumpenstein. Data and code needed to reproduce the analyses in "Climate change shapes richness-evenness relationships in a subalpine grassland experiment" are included.

opencc-by-4.0Nov 2024View details →

edi44/100

Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness, Berchtesgaden National Park, Bavaria, Germany

Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall AUC = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 2

openCC (other)Dec 2023View details →

edi44/100

Community composition, richness, and density of endobionts from two sponge species in Crete, Greece, June 2021

These data was collected as part of a study titled "The “Single Hotel” hypothesis – Does sponge abundance affect endobionts’ diversity?" that was conducted in the island of Crete, Greece in June 2021. It includes collection of 30 sponge specimens of the common species (Agelas oroides and Sarcotragus foetidus) via SCUBA diving, their dissection and removal and identification of all endobionts living withing them (macroinvertebrates). The diversity of endobionts was then calculated and correlated with sponge properites (such as volume), and the sponges area and site of collection.

openCC (other)Feb 2024View details →

edi44/100

Data for study Conventional land-use intensification reduces species richness and increases production: A global meta-analysis

Most current research on land‐use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, this dataset was created and a global meta‐analysis on it was carried out, thus synthesizing 115 studies. The dataset consists of 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. It was found that across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (−8.9%). However, analysis of sub‐groups revealed inconsistent results. Within high‐intensity systems species losses were non‐significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (−22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (−21.2%). In addition, this meta‐analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land‐use intensity. These findings suggest that, in many cases, conventional land‐use intensification drives a trade‐off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results, which were published in a paper titled Conventional land‐use intensification reduces species richness and increa

openCC (other)Jun 2020View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record