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Supplementary material for "Surface frustration re-patterning underlies the structural landscape and evolvability of fungal orphan candidate effectors"
<p><strong>Tables</strong></p> <p>Table S1. List of fungal genomes analyzed in this work, associated references and properties.</p> <p>Table S2. List of all secreted proteins less than 300 amino-acids from the 20 fungal genomes. The table includes Signalp4.0 output, mature sequence, Espritz % disorder, pfam domains, AlphaFold top prediction pLDDT and the associated pdb file in Dataset S1.</p> <p>Table S3. Top Hits to pdb database for all OCE structures. 'network_node_name' corresponds to the portein identifier in the OCE structure similarity network provided in Dataset S3. 'Hidef_raw_community' corresponds to groups of structural OCE analogs identified by HiDEF community detection performed on the network provided in Dataset S3.</p> <p>Table S4. Table S4. List of the 62 major OCE folds with associated statistics. Columns I to AB provide the number of occurrences per species. Note that the actual number of members per species might be underestimated due to the stringent pipeline used for OCE identification (excluding proteins larger than 300 amino acids or containing PFAMs for instance).</p> <p> </p> <p>Table S5. Relative surface exposure, conformational flexibility and conservation data mapped on residues of members of the Alt-A1 and BoNT families. RMSD, root mean square deviation for all aligned atoms; Conservation, percentage conservation in multiple structure alignment.</p> <p>Table S6. Assignment of NCBI accessions to MMseqs clusters and assignment of MMseqs clusters to HMM matching-based super-clusters.</p> <p>Table S7. Co-mutation occurrences and associated p-values in two OCE clades from the Alt-A1 and KP6 families.</p> <p>Table S8. Amino acid properties inferred from mutation scans and frustration analyses in Alt-A1 cluster yellow1 and KP6 cluster 43. 'Number of aa variants' corresponds to the number of different amino acids found at each position (deletion counts as 1). 'Alanine scan ∆Z' and 'Deletion scan ∆Z' correspond to the difference between Z-score for the native protein agains itself and Z-score for the native protein against mutant at each position (either Alanine replacement or 5-aa deletion). 'Destabilization factor' is the average of column E and F. 'Stabilization factor' corresponds to the difference between expected structural variation due to destabilization factor and the observed structural variation in multiple mutants. 'netEffect' is difference between column G and H. 'Max co-mutation %' is the highest frequency of co-mutation observed with other residues in natural variants, with 'Min co-mutation p-value (Bonferroni corrected)' the associated p-value.Table S9. Sequence and delta Z of natural variants and mutants from AA1_cl25</p> <p>Table S9. List of natural variants and <em>in silico</em> mutants from the Alt-A1 cluster 25 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0).</p> <p>Table S10. List of natural variants and in silico mutants from the KP6 cluster 43 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0).</p> <p>Table S11. Summary statistics for the phylogenetic trees of 15 OCE clades analyzed for structure and frustration evolution.</p> <p>Table S12. Mapping of structural and frustration data onto phylogenetic trees for 15 OCE clades. The corresponding trees and protein structures are provided in Dataset S7.</p> <p><strong>Datasets</strong></p> <p>Dataset S1. AlphaFold rank1 models for 3 927 OCEs (.pdb format).</p> <p>Dataset S2. Pairwise structure comparison for 3 911 OCE. DALI matrix output containing pairwise Z-scores.</p> <p>Dataset S3. Network file including 2 561 OCEs with 3 or more vertices of Z-score weight 5.2 or more, in .sif and .xgmml formats.</p> <p>Dataset S4. Videos illustrating the mapping of relative surface exposure and structural variability in Alt-A1 and BoNT groups, amino-acids conservation, co-selected mutation patches and residue net stabilization effects on Alt-A1 clade 25 ancestor and KP6 cluster 43 ancestor. Color scales are as in Figure 2 and 3 respectively (.mp4 format).</p> <p>Dataset S5. Phylogenetic trees (.nwk), ancestral (.fasta) and modern variant (.faa) sequences, and AlphaFold best protein models (.pdb) for members of KP6 cluster 43 and Alt-A1 cluster 25. The archive includes 140 Alt-A1 protein structure and 128 KP6 protein structures.</p> <p>Dataset S6. Best predicted structures for 917 natural variants and mutants of AA1_cl25 and 801 natural variants and mutants of KP6_cl43 (.pdb format).</p> <p>Dataset S7. Phylogenetic trees (.nwk) and AlphaFold best protein models (.pdb) for 15 OCE clades. The file includes 2 598 protein structures distributed from clades AA1_s (139), AA1_t (135), AA1_y1 (140), AA1_y2 (90), AA1_y3 (128), BoNT_s (291), CIP_s (167), CIP_t (231), crystallin (233), GNK2 (189), KP6_cl3 (203), KP6_cl26 (111), KP6_cl43 (123), KP6_cl96 (231), KP6_cl242 (187).</p> <p><strong>Text and Figures</strong></p> <p>Text S1. Contains supplementary methods, results and figures S1 to S13.</p>
Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces (Figures)
<p>High resolution figures related to the below manuscript:</p> <p>Atul Deshpande, Melanie Loth, et al., <a href="https://doi.org/10.1101/2022.06.02.490672">Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces</a>. <em>bioRxiv</em> 2022. doi:10.1101/2022.06.02.490672</p>
The spatial landscape of gene expression isoforms in tissue sections
<p><strong>This upload provides raw in situ sequencing (ISS) data used to validate Spatial Isoform Transcriptomics (SiT), as well as R scripts required for SiT analysis.</strong></p> <p><strong>GenePlots.zip and Reads.zip are ISS data </strong><strong>generated and collected by the CARTANA ISS service</strong>. <strong>The following data description is cited from the report provided by CARTANA ISS service:</strong></p> <p><em>"Folder "Reads" contains coordinates and gene information of segmented spots.<br> The coordinates are in pixel unit. Scaling factor is 0.32 um/pixel. (0,0) is at northwest (top-left corner).<br> With Low/High Threshold, we refer to the quality thresholding. Our technology is fluorescence based, i.e. with the thresholding one can balance how certain the signals are.</em></p> <p><em>Files ending with _LowThreshold: reads not matching with any known barcode were already discarded.</em></p> <p><em>Files ending with _HighThreshold: has information only about spots that passed additional quality check.</em></p> <p><em>Folder "GenePlots" has plotted images in static .png format, fully zoomed out. LowThreshold and HighThreshold follow the same thresholding strategy as in reads files."</em></p> <p> </p> <p><strong>SiT-master.zip is a download of the GitHub repository </strong><a href="https://github.com/ucagenomix/SiT">https://github.com/ucagenomix/SiT</a>, <strong>providing figures and analysis scripts for SiT.</strong></p> <p> </p> <p><strong>Related SiT data are deposited through GEO, accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153859">GSE153859</a></strong></p>
NI4OS-Europe landscaping survey results
<p>The dataset includes 575 complete responses collected in the NI4OS-Europe landscaping survey between 21 October and 10 December 2019. The survey was run in the following countries: Albania, Armenia, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Georgia, Greece, Hungary, Moldova, Montenegro, North Macedonia, Romania, Serbia and Slovenia.<br> The activity was coordinated in collaboration with the four regional EOSC projects (EOSC- Pillar, EOSC-Nordic, EOSC Synergy, ExPaNDS) and its results were used by the EOSC Landscape Working Group.</p> <p>The starting point for developing the survey was the questionnaire designed by the EOSC-Pillar project team, but Policy Mapping Survey by Regional Cooperation Council, EUA’s Open Access Survey and the FAIRsFAIR Policy and Practice Survey 2019 were also used. Some of the questions that were included had to be rephrased to adapt to regional stakeholders’ needs. NI4OS-Europe work package leaders suggested additional questions, some of which were included in the survey. The file NI4OS-Europe_survey_questions.ods contains all the questions from the survey mapped according to topics. For each question it is indicated whether it was adopted from an existing survey or was suggested by a NI4OS work package.</p> <p>Five questionnaires were defined – one for each stakeholder group identified by the NI4OS-Europe partners based on their role in the research ecosystem: FUND (research funders and policymakers), CREATE (universities, research institutes, etc.), SUPPORT (libraries, repositories, research infrastructures, etc.), CONSUME (organizations using research results in their work, e.g. SMEs) and FACILITATE (individuals and organizations involved in promoting the principles of open science). There were 82 questions in all questionnaires.</p> <p>LimeSurvey was used to collect responses. As some questions appeared across multiple surveys, the five questionnaires were merged into one survey with forking paths: after answering the preliminary questions, a respondent was redirected to the appropriate questionnaire.</p> <p>The survey collected 575 complete responses from 482 distinct entities in 15 countries. The incomplete responses have been removed from the dataset.</p> <p>****Dataset contents****<br> NI4OS-Europe_survey-results_2019_completed.ods, data file, survey responses, OpenDocument Spreadsheet (.ods) file generated in Apache OpenOffice Calc<br> NI4OS-Europe_survey_questions.ods, survey questions, OpenDocument Spreadsheet (.ods) file generated in Apache OpenOffice Calc<br> NI4OS-Europe_survey-questions.pdf, survey questions exported as a PDF file from LimeSurvey<br> NI4OS-Europe_survey-README.txt<br> The dataset was used to create visualizations on the NI4OS website: https://ni4os.eu/survey-results/<br> ****Dataset license****<br> The dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0), <a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a></p>
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology
<p>This dataset contains direct measurements of biomass burning emission factors for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file. </p> <p> </p>
The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex - Minimal Dataset
<p>Minimal Dataset for "The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex", published at <a href="https://www.nature.com/nsmb/">NSMB</a>.</p>
Antibody landscape of C57BL/6 mice cured of B78 melanoma via immunotherapy
<p>Antibodies can play an important role in innate and adaptive immune responses against cancer, and in preventing infectious disease. Using a Nimble Therapeutics high-density peptide array, we assessed potential protein-targets for antibodies found in sera of immune mice, that were previously cured of their melanoma through a combined immunotherapy regimen with long-term memory. Using flow cytometry, immune sera showed strong antibody-binding against melanoma tumor cell lines. Sera from 6 of these cured mice were analyzed with this high-density, whole-proteome peptide-array to determine specific antibody-binding sites and their linear peptide sequence. We identified thousands of peptides that were targeted by 2 or more of these 6 mice and exhibited strong antibody binding only by immune, not naive sera. Confirmatory studies were done to validate these results using 2 separate ELISA-based systems. This technology may be helpful in studying the “immunome”. of protein-based epitopes that are recognized by immune sera from mice, or possibly patients, cured of cancer via immunotherapy.</p>
Dataset (81 forest parcels) supplementing the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)"
<p>The dataset about 81 small-scale private forest parcels contains the answer variables and predictors used in the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)".</p>
Epigenetic and transcriptional landscape of stress memory in woodland strawberry
<p>Bedfiles of differentially methylated regions (DMRs) that were detected in stressed mother plants (M) and their (themselves unstressed) clonal daughter plants that were formed <em>via</em> stolon formation (St1, St2, St3). The M plants were grown and sampled <em>in vitro</em> and the St1, St2, St3 plants in the green house. The DMRs were called using the the EpiDiverse/dmr bioinformatic analysis pipeline (Nunn et al., 2021).</p> <p><strong>Stress assays <em>in vitro</em></strong></p> <p>One-month-old seedlings were transferred to a fresh MS media and growth chambers at 24<sup>o</sup>C/21<sup>o</sup>C (day/night),16 h light/8 h dark, as control conditions<em>. </em>For heat-stress, plants were exposed to 30<sup>o</sup>C (day/night) for one week followed by 2 days of recovery (24<sup>o</sup>C/21<sup>o</sup>C) on fresh medium as well as the control plants. Then, the plates were transferred to 37<sup>o</sup>C (day/night) for 1 week with 2 recovery days (Figure 1A). We sampled aerial parts of plants for the molecular analyses. To reduce variability resulting from individual plants, three biological replicates of 5 pooled plants were collected per condition. Samples were harvested in 1.5 mL tubes between 9:00-11:00 a.m. and immediately frozen in liquid nitrogen and stored at -80<sup>o</sup>C until required.</p> <p><strong>Greenhouse propagation assays</strong></p> <p><em>In vitro</em> plants after heat and control treatment were transferred to soil (one plant per pot) in square plastic pots (size: 12x12x10 cm) and to a greenhouse with long day conditions (24<sup>o</sup>C/21<sup>o</sup>C day/night and 60%-70% humidity).</p> <p>Twelve mother plants (M) from control (CM; n=12) and heat-stress (HM; n=12) conditions were used for asexual propagation. From each mother plant, the two first stolons (St) were kept for producing the daughter plants of the first asexual propagation (St1) in individual pots. After two weeks, following root formation, the stolons were cut to get independent daughter plants from their mother plant (M). This process was continued until St3.</p> <p>The DMRs can also be visualized here: <a href="https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub">https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub</a></p> <p>And the raw bisulfite sequencing data can be found here: <a href="https://www.ebi.ac.uk/ena/browser/text-search?query=ERP135585">https://www.ebi.ac.uk/ena/browser/text-search?query=ERP135585</a></p> <p> </p> <p> How many daughter plants per stolon? One stolon produce a chain of daughter plants.</p>
Datasets for examining perceptions of fire resilient landscapes
<p>Datasets used to answer the question 'what is a fire resilient landscape?'. Included is the data used for a literature review from Scopus and Web of Science containing entries surrounding resilience to landscape fires. Also included is survey responses. Participants came from two groups, students of the Pyrogeography course at Wageningen University, and professionals working within the fire community (both academia and practice). The participants were asked about their perception of a fire resilient landscape, and the responses coded with thematic analysis.</p>
CS3MESH4EOSC Final Event Science Mesh - Unlocking Open Science and Collaborative Research Landscape
<p>The recap video of CS3MESH4EOSC final event. The CS3MESH4EOSC final event, took place on 22 June 2023, at the EGI Conference in Poznan (Poland), showcased how the Science Mesh is contributing to an easier and more robust open science across Europe, thanks to novel approaches for data sharing and synchronisation.</p> <p><strong>The first half of the event</strong> will count with live demonstrations, where each data service from the Science Mesh will be presented from a user-perspective point of view. Event attendees will get practical information on how they can join the Science Mesh as a researcher, a software developer or a service provider. The event will also bring together representatives of Science Mesh use cases, who will explain how Science Mesh is making a difference in their lives, thanks to easier data sharing and synchronisation. A panel discussion with representatives of different sectors, from research to industry and education, will discuss the most urgent trends & priorities for cross-border science collaboration between different sciences.</p> <p><strong>The second half of the event</strong> will be focused on the technical novelties within the Science technical foundation. A series of demonstrations will be presented, followed by a panel discussion, with representatives of CS3MESH4EOSC members that are part of the EOSC Task Forces, on how the Science Mesh contributes to EOSC’s success and the overall EOSC Strategic Research and Innovation Agenda (SRIA).</p>
Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach
<p>Abstract:</p> <p>A novel method of estimating the silica (SiO2) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to calculate first the minimum SiO2, water (H2O) and carbon dioxide (CO2) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO2. In a recursive step, a ‘consensus’ SiO2 is then established as the average between the two aforementioned estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Ti elemental concentrations + ‘consensus’ SiO2 + reported trace element concentrations converted to wt% + ‘normative’ H2O + ‘normative’ CO2) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H2O and CO2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO2 is linear, strong (R2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO2 concentrations with another publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO2 values measured on the ground over the same study area. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to validate results.</p> <p>Datasets:</p> <p>The original geochemical and mineralogical data for soils of the conterminous United States (A and C horizon datasets) were downloaded from <a href="https://mrdata.usgs.gov/ds-801/">https://mrdata.usgs.gov/ds-801/</a>.</p> <p>The ‘Shacklette and Boerngen’ dataset was downloaded from <a href="https://mrdata.usgs.gov/ussoils/">https://mrdata.usgs.gov/ussoils/</a>.</p> <p>A worked example for the five selected samples of Figure 5 is available as a Microsoft Excel spreadsheet (NALG_Ch_oxides_with_estimated_SiO2_LOI_worked example.xlsx) on Zenodo.org.</p> <p>The new datasets including sample identification, coordinates, converted major oxide concentrations, and the concentration estimates for SiO<sub>2</sub> and LOI in wt% for the A and C horizon datasets from the North American Soil Geochemical Landscapes (NASGL) project are available as comma separated value files (NALG_Ah_oxides_with_estimated_SiO2_LOI.csv and NALG_Ch_oxides_with_estimated_SiO2_LOI.csv) on Zenodo.org.</p>
ScRAPv20230731: Telomere-to-telomere assemblies of 142 strains characterize the genome structural landscape in Saccharomyces cerevisiae
<p><strong><em>Saccharomyces cerevisiae </em>Reference Assembly Panel (ScRAP) v20230731 </strong>></p> <p>The haplotype-resolved and/or collapsed T2T genome assemblies for 142 <em>S. cerevisiae</em> strains isolated from diverse geographical and ecological niches.</p>
UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network
<p>The UBGG dataset provides easily access and leverage to researchers and analysts, which is stored in the following Zenodo repository (<a href="https://doi.org/10.5281/zenodo.8053333">https://doi.org/10.5281/zenodo.8352777</a>). The UBGG dataset consists of two main components:</p> <ul> <li><strong>UBGG-3m: the fine-grained UBGG map product of 36 metropolises in China.</strong> The UBGG-3m dataset captures the intricate urban landscape features with remarkable precision, providing a detailed representation at an impressive 3-meter resolution. Fig. 1 in User Guides shows the classification results for 36 Chinese metropolises. Researchers can delve into the nuances of the UBGG continuum, gaining invaluable insights into the interplay between the blue, green, and gray elements of urban environments in each metropolis.</li> </ul> <ul> <li><strong>UBGGset:</strong> <strong>the large-volume sample dataset to support the UBGG deep learning research.</strong> Complementing the UBGG-3m dataset, UBGGset serves as a large-volume sample dataset specifically tailored to support and foster UBGG research endeavors (Fig. 2). The UBGGset consists of 14,627 sample images (without data augmentation), with dimensions of 256 pixels in length and width, covering an urban area of approximately 2,272 km<sup>2</sup>. The UBGGset was constructed with co-registered pairs of 3 m Planet images and fine-annotated urban landscapes labeled on 1 m Google Earth image. This dataset encompasses 15 typical cities, offering researchers a rich and diverse resource to drive exploration, analysis, and innovation in the field of urban landscape studies.</li> </ul> <p> </p> <p><strong>Citation format for paper and dataset:</strong></p> <p>[1] Zhiyu Xu, Shuqing Zhao. Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network. <em>Sci Data</em> 11, 266 (2024). https://doi.org/10.1038/s41597-023-02844-2</p> <p>[2] Zhiyu Xu, Shuqing Zhao, Fine-grained urban landscape mapping reveals broad-scale homogeneity in urban environments,<br>Science Bulletin, (2024). https://doi.org/10.1016/j.scib.2024.03.060</p> <p>[3] Zhiyu Xu, Shuqing Zhao. UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network (v1.0) [Data set]. (2023). Zenodo. https://doi.org/10.5281/zenodo.8352777</p>
Contrasting effects of landscape composition on crop yield mediated by specialist herbivores
Open the record for dataset details and reuse information.
Virgin Islands National Park: Coral Reef: Population Dynamics: Landscape-scale Variation in Scleractinian Corals
This study provides a landscape-scale context to a decadal-scale analysis of community structure on shallow reefs along 4 km of the south shore of St. John, US Virgin Islands. By focusing on 12-14 sites along ~100 km of the shores of St. John and St. Thomas, surveys conducted in 2011 were used to contrast: (1) a local-scale with a landscape-scale analysis on two islands, (2) reefs around St. John and St. Thomas, and (3) reefs on north and south shores. Reefs were censused using photoquadrats that were analyzed for percentage cover first by functional groups (coral, macraolagae and CTB), and then by coral genus. In general, among-site variation for the coarse-resolution analysis eclipsed shore and island effects, but the fine-resolution analysis revealed strong site-specific differences for multiple coral genera that could be the product of priority effects in community succession. Over the next decade these differences probably will create unique community trajectories at each site.
Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15.
Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15. Spatial beta-diversity may increase landscape productivity if there are positive spatial selection effects. Alternatively, dominant species in mixtures might not be the most productive species in monoculture leading to negative or neutral spatial selection effects. However, these hypotheses remain untested experimentally. Seedling survival can determine species establishment, influencing productivity later. To address this knowledge gap, we experimentally tested whether transplanted seedlings of dominant species optimally sort among habitat types (grassland dominated by Andropogon gerardii, savanna by Quercus macrocarpa, deciduous forest by Acer rubrum, coniferous forest by Pinus strobus, bog by Larix laricina), creating positive effects of landscape diversity on seedling survival and net biodiversity effects at Cedar Creek Ecosystem Science Reserve (CCESR) in Minnesota, USA. The study is named BetaDIV and consists of 100 plots (20 plots per habitat × 5 habitats). Each of the five habitats includes two true replicate monocultures for each of the five species and two true replicates for each of the five possible mixture compositions of four species (leaving each one out in turn to eventually explore the effect of species identity). Each plot is 1.5 by 1.5 m, with 12 seedlings planted 0.5 m apart in a 4 × 4 square grid, except in the plot corners. In the early June 2022, we tagged and planted all seedlings (i.e., bareroot seedlings for trees and plugs for the grass A. gerardii). Two weeks after the initial transplanting, we started tracking seedling survival (presented here) to investigate how seedlings responded to local habitat conditions. We conducted a seedling census for each of the 1200 tagged seedlings (12 seedlings per plot×100 plots), in early September 2022, which was two months at the end
Soil moisture data across a Florida scrub and sandhill landscape collected from 1998-2018 at Archbold Biological Station
This project was initiated 1998 to examine the variation in percent soil moisture in relation to rainfall, vegetation type, gaps, and time-since-fire in upland habitats at Archbold Biological Station, in south-central Florida. Data were collected from 78 sampling points across four vegetation types (rosemary scrub, scrubby flatwoods, oak-hickory scrub and sandy roadsides) with different time-since-fires (2-3 years or >20 years post-fire). In January 2006, 30 additional sampling points were added to include a fourth vegetation type (southern ridge sandhill). Data were collected at three depths below the soil surface (10, 50 and 90 cm) weekly (1 October 1998 – 9 June 1999), then bi-weekly (23 June 1999 – 3 October 2001), monthly (17 October 2001 – 15 June 2011), and every other month thereafter until the project ended on 16 July 2018.
Disturbance legacies and resilience simulation using an individual-based forest landscape model on the Andrews Experimental Forest
Disturbances are key drivers of forest ecosystem dynamics, and forests are well adapted to their natural disturbance regimes. However, as a result of climate change, disturbance frequency is expected to increase in the future in many regions. It is not yet clear how such changes might affect forest ecosystems, and which mechanisms contribute to (current and future) disturbance resilience. We here studied the 6364-ha HJ Andrews Experimental Forest landscape to investigate how patches of remnant old-growth trees (as one important class of biological legacies) affect the resilience of forest ecosystems to disturbance. Using the spatially explicit, individual-based forest landscape model iLand we analyzed the effect of three different levels of remnant patches (0%, 12%, and 24% of the landscape) on 500-year recovery trajectories after a large, high severity wildfire. In addition, we evaluated how three different levels of fire frequency (no fire, a historic fire return interval of 262 years, and a reduced fire return interval of 131 years) modulate the effects of initial legacies. The study investigated effects of legacies on the resilience of forest ecosystem structure (represented by canopy complexity as described by the rumple index), composition (proportion of late-seral species), and functioning (total ecosystem carbon storage). For each scenario of initial legacy and fire return interval 25 replicates were simulated. More information on the simulation methodology as well as the code and executable used for this study can be obtained at http://iLand.boku.ac.at. The dataset is completed and no further analyses are planned at this point. The results are published in Ecological Applications http://dx.doi.org/10.1890/14-0255.1.
Hubbard Brook Experimental Forest: Landscape scale (valley-wide) soil carbon and nitrogen cycling data
The valley-wide plots are a grid of 431 sites along fifteen N–S transects established at 500-m intervals spanning the entire Hubbard Brook Valley. This dataset includes total soil carbon, nitrogen and organic matter content, potential net nitrogen mineralization and nitrification rates, microbial respiration rates, soil water content and holding capacity, soil ammonium and nitrate concentrations, soil pH, and tree composition in a subset of 100 randomly selected plots in 2000. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. An analysis of these data can be found in: Venterea, R. T., Lovett, G. M., Groffman, P. M., & Schwarz, P. A. (2003). Landscape patterns of net nitrification in a northern hardwood-conifer forest. Soil Science Soc. Amer. J., 67, 527–539. https://doi.org/10.2136/sssaj2003.5270
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.
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.
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.
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.