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17,039 results for “Natural”
Urbanus and Cosgrove et al. Nature Communications (2023) - 12 months scRNAseq fastq files (mouse 2) for Figures 5 and 6
<p>This dataset contains .fastq files for the 12 month timepoint (mouse 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic
<p>De novo assembly of ancient metagenomes to reconstruct biosynthetic machinery and access Pleistocene bacterial metabolites. This Zenodo archive belongs to the following publication (DOI: <a href="https://doi.org/10.1126/science.adf5300" target="_blank" rel="noopener">10.1126/science.adf5300</a>):</p> <p>Klapper, M., Hübner, A., Ibrahim A. et al. Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic. <em>Science</em> <strong>380</strong>, 619–624 (2023).</p> <p>This Zenodo archive contains the GitHub repository <a href="https://github.com/paleobiotechnology/EMN001_Paleofuran" target="_blank" rel="noopener">Paleobiotechnology/EMN001_Paleofuran</a> with all code and and data used for the publication mentioned above, as well as additional figures, huge NMR and GCMS data, which are not stored in the GitHub repository but have been deposited in this long-term Zenodo archive (see directory tree below).</p> <p>All datasets and scripts required for the project analysis can be found in this directory: <br>├── GCMS.tar.gz<br>├── github.tar.gz<br>├── licenses.tar.gz<br>├── MS_figures.tar.gz<br>└── NMR.tar.gz</p>
Dataset and stimuli: Perception of saturation in natural objects
<p><strong>This dataset contains observer data and stimulus information for the below publication. Refer to this manuscript for more details.</strong></p> <p>Laysa Hedjar, Matteo Toscani, and Karl R. Gegenfurtner, "Perception of saturation in natural objects," Journal of the Optical Society of American A <strong>40</strong>(3), A190-A198 (2023), doi:10.1364/JOSAA.476874.</p> <p> </p> <p>Participant data is available in two files: <em>fruit_pt_data.csv </em>and <em>blob_pt_data.csv</em></p> <ul> <li><em>fruit_pt_data.csv</em>: <ul> <li>fruit name: name of fruit pair</li> <li>object or swatch: whether the stimulus pair were whole objects or 8x8 swatches</li> <li>participant ID: given participant identification number</li> <li>proportion positive: proportion of trials in which participant chose the positive stimulus as more saturated (out of 10 total trials per stimulus pair)</li> </ul> </li> <li><em>blob_pt_data.csv</em>: <ul> <li>blob hue (radians - LAB): hue in radians of the blob pair, as defined in LAB-LCH color space</li> <li>object or swatch: whether the stimulus pair were whole objects or 8x8 swatches</li> <li>matched or unmatched: whether the stimulus pair were matched in terms of blob ID (refers to spatial configuration)</li> <li>positive stimulus ID: identification number of the positive LC-slope stimulus (refers to spatial configuration)</li> <li>negative stimulus ID: identification number of the negative LC-slope stimulus (refers to spatial configuration) <ul> <li>note that the above two IDs should be identical if the stimulus is a 'matched' pair</li> </ul> </li> <li>participant ID: given participant identification number</li> <li>proportion positive: proportion of trials in which participant chose the positive stimulus as more saturated (out of 5 total trials per stimulus pair)</li> </ul> </li> </ul> <p> </p> <p>Stimuli pngs are in the zip file s<em>timuli.zip</em>. Pngs are not gamma-corrected. Blob and fruit stimulus sets are separated by folder; object and swatch stimulus sets are also separated by folder.</p> <p>Fruit pngs are labeled:</p> <p> fruit_[object/swatch]_[fruitName]-[negative/positive].png</p> <p>For blob pngs, six possible spatial configurations for each hue were used. An ID was given for each configuration. Blob pngs are labeled:</p> <p> blob_[object/swatch]_hue[hueInRadians]_ID[1-6]-[negative/positive].png</p> <p> </p> <p>Statistics of the stimulus images are presented in the files <em>fruit_stats_objects.csv</em>, <em>fruit_stats_swatches.csv</em>, <em>blob_stats_objects.csv</em>, and <em>blob_stats_swatches.csv</em>. Each column represents a different stimulus image. Each row represents a different statistic taken across the distribution of pixels. Calculations were made in CIELAB-LCH color space ('white point' defined as white of monitor: CIE1931 xyY 0.3328, 0.3343, 142.35).</p>
Penta-coordinated or -valent: the nature of the chemical bond of some Ti-C-Al compounds
<p>These folders contain the raw date which were used to prepare the above publication.<br> The zipfiles entitles compound-1.zip, compound-2.zip, and compound-3.zip contain the data for the computed compounds of the main publication. Please refer to that for further details. The zip-file xray.zip contains the original cif-file for convenience.<br> </p>
Sustaining Cities, Naturally Webinar: Recording Day 1
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This recording of Day 1 was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Mangrove Crab Sampling Data in Dongzhaigang National Nature Reserve, Haikou, Hainan Province, China
<p>This dataset contains the results of a study on mangrove crabs conducted in four seasons (Summer, SU; Autumn, AU; Winter, WI; Spring, SP) of 2020 and 2021. The samples were collected in the Dongzhaigang National Nature Reserve, Haikou, Hainan Province, China, at five sites: Sanjiang (SJ), Tashi (TS), Shanweitou (SWT), Luodou (LD), and Puqian (PQ). The primary focus is on crab species belonging to the superfamilies Ocypodoidea (ghost crabs), Grapsoidea (square crabs), and Portunoidea (swimming crabs).</p> <p>Sampling was conducted using net trapping, with three replicate plots set up for each habitat type at each site. Each plot was sampled continuously for three days. Vegetation information was recorded using dominant species as representatives, and water environmental information was collected using a WTW instrument. The parameters measured include total dissolved solids (TDS) (mg/L), dissolved oxygen (DO) (mg/L), salinity (SAL) (‰), water temperature (T) (℃), and pH. Finally, the longitude and latitude in the WGS84 coordinate system and Cartesian coordinates for each plot were recorded.</p> <p>The dataset fields are as follows:</p> <ul> <li>date: Date of sampling</li> <li>year: Year of sampling</li> <li>month: Month of sampling</li> <li>day: Day of sampling</li> <li>site: Sampling location, including TS, SJ, SWT, LD, PQ</li> <li>habitat: Habitat type, including tidal channels, tidal flats, and several vegetation types represented by mangrove trees such as Avicennia marina, Rhizophora stylosa, Bruguiera sexangular, Sonneratia apetala, and Ceriops tagal.</li> <li>plotname: Plot name</li> <li>species: Species name, as per the World Register of Marine Species (<a href="https://www.marinespecies.org/">https://www.marinespecies.org</a>)</li> <li>superfamily: Superfamily, as per the World Register of Marine Species (<a href="https://www.marinespecies.org/">https://www.marinespecies.org</a>)</li> <li>season: Season, including Summer (SU), Autumn (AU), Winter (WI), and Spring (SP)</li> <li>cname: Plot division by season, site, and habitat</li> <li>fullname: Plot division by season, site, habitat, and plot sequence number</li> <li>pname: Plot division by site, habitat, and plot sequence number</li> <li>TDS: Water total dissolved solids (mg/L)</li> <li>pH: Water pH</li> <li>DO: Water dissolved oxygen (mg/L)</li> <li>T: Water temperature (℃)</li> <li>SAL: Water salinity (‰)</li> <li>longitude: Longitude in WGS84 coordinate system</li> <li>latitude: Latitude in WGS84 coordinate system</li> <li>x: Cartesian coordinate x</li> <li>y: Cartesian coordinate y</li> </ul> <p>We thank Chengpu Jiang, Liangjun Wei and other colleagues for their assistance during the field samplings. Thanks also for the experimental conditions and sampling support provided by Hainan Dongzhaigang National Nature Reserve Authority.</p>
Implementation of GR hydrological models in 95 near-natural catchments across Chile
<p>All the files included here contain the data and calibration results produced for the paper "Exploring parameter (dis)agreement due to calibration metric selection in conceptual rainfall-runoff models" accepted for publication in Hydrological Sciences Journal (HSJ). This database summarizes the calibrated parameter sets for the GR4J, GR5J and GR6J conceptual rainfall-runoff models, all coupled to the CemaNeige snow module (i.e., GRXJ + CemaNeige = GRXJCN), using 12 objective functions. The models are configured for 95 near-natural catchments located in Continental Chile. Each basin is identified by a unique code registered in the National Water Bank (BNA by its acronym in Spanish) by the Chilean Water Bureau (DGA; https://dga.mop.gob.cl/). Meteorological forcings and hypsometric curves for each basin studied are also included.</p> <p>The information is organized as follows:</p> <p>- "01 Forcings" : It includes a "Comma-separated value" file (".csv") per basin (according to the notation "BNA code.csv") which contains daily time series of precipitation (P; mm/d), temperature (°C) and potential evapotranspiration (E; mm/d) for the period 1980-01-01 to 2017-12-31. The daily runoff observations (Q; mm/d) and snow water equivalent (SWE; mm) from Cortés et al. (2017) are included. P and T are estimated from the basin-scale average of the CR2Met v2.0 gridded product, while E was calculated using Oudin's formula.<br> - "02 Hypsometry" : It includes a "Comma-separated value" file (".csv") per basin with elevation (in m a.s.l.) vs. area below elevation (in percentage) in the format required by GR models ("BNA code.csv" notation), and the full hypsometric curve ("BNA code_original.csv" notation) retrieved from the SRTM DEM clipped to the basin of interest.<br> - "03 Calibrated parameters": It includes one sub-directory per basin, containing a summary of the calibrated parameters for each combination of model structure (GR4JCN, GR5JCN and GR6JCN) and objective function.</p> <p>Additionally, we include the following "Comma-separated value" (i.e., ".csv") files:</p> <p>- "BNA_select.csv": list of case study basins (BNA code and name).<br> - "Catchment_attributes_CAMELS-CL.csv": catchments attributes directly obtained from CAMELS-CL.<br> - "Catchment_attributes.csv": catchments attributes used for this study. Note that this file contains re-calculated values for climatic attributes. </p>
Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal'
<p>Title: Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal'</p> <p>Recommended Citation: Adhikari, B., Prescott, G., Urbach, D., Chettri, N., & Fischer, M. (2022). Nature’s contributions to people and the sustainable development goals in Nepal. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac8e1e</p> <p>Principal Investigator: Markus Fischer (markus.fischer@ips.unibe.ch)</p> <p>Authors:<br> Biraj Adhikari (biraj.adhikari@ips.unibe.ch, ORCID: 0000-0002-4260-8706)<br> Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X)<br> Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834)<br> Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879)<br> Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)</p> <p>Date of data collection: November 2020 - August 2021<br> Location of data collection: Kathmandu, Nepal<br> Article title: 'Nature's contributions to people and the Sustainable Development Goals in Nepal'</p> <p>R code available at: https://github.com/biraj-ad/r4dLiteratureReview_GithubRep</p> <p>Data Overview:</p> <p>1. 'Literature_References.csv'<br> List of 119 peer-reviewed journals and 21 grey literature documents used in the review. Each is assigned a unique identifier ("SN Ref") to link it to the other files.</p> <p><br> 2. 'Drivers_and_Trends.csv'<br> The "Quote" column is the text from papers which has information on (i) trends in ecosystems or NCPs, and if available (ii) direct and/or indirect drivers causing the trends.<br> The "Nature" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others, and Directly to NCP), the "NCP" column indicates which NCP (categorized into 18 categories based on the IPBES classification) the text refers to. The "NCP Category" column indicates whether the said NCP is a regulating, material or non-material NCP. <br> We also categorized direct and indirect drivers based on the IPBES classification (Direct Drivers: Land-use Change, Climate Change, Direct Exploitation, Invasive Alien Species, and Pollution; Indirect Drivers: Institutions and Governance, Demographic and Sociocultural, Economic and Technological). <br> If there were more than one drivers of change for a particular NCP, we have included them in additional columns. In order to avoid multiple counts for trends of a particular NCP, we introduced the "TrendCount" column. For example, columns 3, 4 and 5 refers to the same trend of decreasing WQN, but has 3 Direct Drivers. Therefore, each TrendCount is given a weight of 0.33 so that the total trend adds upto 1.</p> <p>3. 'NCP_to_SDG.csv'<br> The "Quote" Column is the text from papers which has information on which NCP is contributing towards which SDG. "Remarks from text" are the authors' own remarks based on the text and the overall context of the article.<br> The contribution of NCPs are classified as positive or negative, and indicated in the column "Effect (pos/neg)"<br> The "NCP" column indicates which NCP (categorized into 18 categories according to IPBES) the text refers to, while the "Contribution to SDG" column indicates which SDG the NCP is contributing towards.<br> The "Ecosystem" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others) the NCP is being supplied from.</p> <p>Codes for NCPS:<br> HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOI (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), ORG (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), INS (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), and OPT (Maintenance of Options).</p> <p>4. 'Data_consolidated.xlsx'<br> The above three data files combined into one xlsx document<br> </p> <p> </p> <p> </p>
Worldwide CO2 emissions and natural disasters from 1960 to 2021
<p>A CSV file containing worldwide CO2 emissions as well as the number of natural disasters per year.</p> <p>Sources:</p> <ul> <li>Global Carbon Atlas <ul> <li>DOI: <a href="http://doi.org/10.17616/R3434K">http://doi.org/10.17616/R3434K</a></li> <li>URL: <a href="http://www.globalcarbonatlas.org/en/CO2-emissions">http://www.globalcarbonatlas.org/en/CO2-emissions</a></li> <li>Last accessed: 2023-05-09</li> </ul> </li> <li>EM-DAT <ul> <li>DOI: <a href="http://doi.org/10.17616/R3QQ1X">http://doi.org/10.17616/R3QQ1X</a></li> <li>URL: <a href="https://public.emdat.be/data">https://public.emdat.be/data</a> (registration necessary)</li> <li>Last accessed: 2023-05-14</li> </ul> </li> <li>GitHub Project <ul> <li>DOI: <a href="http://doi.org/10.5281/zenodo.7934702">http://doi.org/10.5281/zenodo.7934702</a> </li> <li>URL: <a href="https://github.com/jkopec/global-emission-and-disaster-analysis">https://github.com/jkopec/global-emission-and-disaster-analysis</a></li> </ul> </li> </ul>
Copernicus High Resolution Vegetation Phenology and Productivity for Doñana Natural Space
<p>GeoTIFF rasters with the following phenometrics obtained from Sentinel 2 Data:.</p> <p> </p> <p>* Start of the season Day of the Year (SOSD)</p> <p>* Maximun of the Season Day of the Year (MAXD)</p> <p>* End of the Season Day of the Year (EOSD)</p> <p>* Start of the season Value (SOSV)</p> <p>* Maximun of the Season Value (MAXV)</p> <p>* End of the Season Value (EOSV)</p> <p> </p> <p>These rasters have been downloaded, mosaicked and croped with Doñana Natural Space DEIMS.ID through Pyvpp python package. </p>
lilGym: Natural Language Visual Reasoning with Reinforcement Learning, model files
<p>Baselines models for the paper <a href="https://lil.nlp.cornell.edu/lilgym"><em>lil</em>Gym: Natural Language Visual Reasoning with Reinforcement Learning</a>.</p>
An assessment of acd_lotus for the structural dereplication of natural products using 13C NMR spectroscopy data.
<p> A method that relies on carbon-13 nuclear magnetic resonance (NMR) spectroscopy, elaborated in earlier works of the author's research group, requires the availability of a dedicated database that establishes relationships between chemical structures, biological and chemical taxonomy, and spectroscopy. The construction of such a database, called <a href="https://doi.org/10.5281/zenodo.6621129">acd_lotus</a>, was reported earlier and its usefulness was only illustrated by <a href="https://doi.org/10.1002/cmtd.202200054">three examples</a>. This dataset provides the results of structure searches carried out starting from 58 carbon-13 NMR data sets recorded on compounds selected in the metabolomics section of BMRB, the biological magnetic resonance bank.</p> <p>Correction in the JSON file of ascochitine.</p> <p>Addition of a CSV file according to <a href="https://doi.org/10.1186/s13321-021-00520-4">Schymanski and Bolton</a> for the description of the selected compounds.</p> <p>Better PNG drawings for compounds 28, 38, 46, and 57.</p> <p>Better PNG drawing for compounds 7 and 26.</p>
Public perceptions of an avian reintroduction aiming to connect people with nature
<p>Full raw dataset (quantitative and qualitative) and R code for quantitative analyses, associated with the publication "Public perceptions of an avian reintroduction aiming to connect people with nature" <em>People and Nature</em>. Survey responses are presented here anonymised. Please note that where the term 'Proactive' sample is used this is referred to as the 'Self selecting' sample in the associated journal article.</p>
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
<p>This dataset is related to the paper "<strong>Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial <em>Pseudomonas </em>in the wheat rhizosphere</strong>" (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.</p> <p>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>.</p> <p>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>.</p>
Report on Transformers interpretability for Natural Language Processing: A case study on Technical Debt classification
<p>Transformer models have significantly advanced the field of natural language processing (NLP), achieving exceptional results in various tasks. However, these models are often seen as "black boxes", providing limited insight into the factors influencing their predictions. It has become crucial to develop and utilise methods for interpreting and explaining these models to uncover their complex inner workings. This report discusses the latest techniques and tools that aid in a more profound understanding of transformer models within NLP. Additionally, it explores a vital industrial use case: Technical Debt (TD) classification. In this context, the report leverages transformer model interpretability tools and Retrieval Augmented Generation (RAG) to analyse and understand the characteristics of text in Github issues, distinguishing between TD and non-TD.</p> <p>This report thoroughly outlines an approach to improve the transparency and reproducibility of machine learning models, with a special emphasis on TD classification. It integrates the RAG approach and exploits feature attribution techniques, presenting a route to create AI systems that are not only high-performing but also demonstrably trustworthy and comprehensible. Through a detailed examination of word patterns in TD classification and the innovative use of the RAG approach, the research highlights a strong dedication to promoting transparency and responsibility in AI systems, potentially ushering in a new phase in machine learning research that focuses on clarity and dependability.</p>
Watkins natural accessions yellow rust disease resistant scores
<p>The file contains the phenotypic information from field trails conducted in Kenya at the Kenya Agriculture and Livestock Research Organisation (KALRO) and the Ethiopian Institute of Agricultural Research (EIAR). Data are separated into the three rusts (yellow rust (Yr), stem rust (Sr) and leaf rust (Lr)), although data is not complete at all locations. When possible both seedling and adult plant data is provided. Adult scores include several observation across the growing season. For scoring rust severity, the modified Cobb scale (Peterson et al. 1948) was used to determine the percentage of tissue infected (0-100%) with rust and infection response (S, MS, MR and R, corresponding to susceptible, moderately susceptible, moderately resistant and resistant). </p> <p>Accession codes relate to Watkins landraces and their country of origin and accession names are indicated. Locations, dates and disease scores are indicated. Missing data is indicated as "-". </p> <p>For more detailed passport data and access to germplasm visit the John Innes Centre Germplasm Resources Unit (<a href="https://www.seedstor.ac.uk/search-browseaccessions.php?idCollection=39">SeedStor</a>). Additional germplasm resources and populations developed from the Watkins accessions can be found here: <a href="https://wisplandracepillar.jic.ac.uk/">https://wisplandracepillar.jic.ac.uk/</a> </p> <p> </p>
Evaluation dataset for Relation Extraction of relationships between Organisms and Natural-Products
<p>A curated evaluation dataset for end-to-end Relation Extraction of relationships between organisms and natural-products.</p> <p>Details about the manual annotation:</p> <ul> <li>For Chemicals: <ul> <li>The chemical labels are annotated as they appear in the abstract.</li> <li>In abstracts, singular chemicals and classes of chemicals produced by a specific organism were distinguished.</li> <li>The "type" attribute {“chemical”, “class”} is used to indicate the nature of the mentioned name.</li> <li>A "class" attribute for chemical entities has also been included if class information is present in the abstract.</li> <li>A Wikidata and PubChem identifiers were assigned to chemicals and classes when available.</li> </ul> </li> <li>For Organisms: <ul> <li>The organism labels are annotated as they appear in the abstract.</li> <li>If in an abstract, the genus name was mention first, e.g. "Plakinastrella sp." and then the specie name e.g "Plakinastrella clathrata" is precise, then only the specie name is used.</li> <li>A Wikidata identifier was assigned to all organisms.</li> <li>In some abstracts, only the genus name is mentioned.</li> </ul> </li> <li>For Relations: <ul> <li>Only the relations explicitly mentioned in the abstract are reported in the output labels.</li> <li>Relations are reported in their order of appearance in the abstract.</li> </ul> </li> </ul>
Dataset associated with the manuscript "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia's carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2).
<p>Dataset associated with the manuscript "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia’s carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2). </p>
Decomposition of Microstegium vimineum litter, plants grew through the Big Oaks National Wildlife Refuge in 2019. Litter used in this experiment naturally senesced in the fall 2019, decomposition data collected through 2020. Plants were infected or not-infected with the foliar fungal pathogen Bipolaris gigantea during the 2019 growing season.
Decomposition of plant litter, facilitated primarily by microbial decomposers, plays a critical role in biogeochemical cycling and ecosystem function. Emerging pathogens have the potential to impact litter decomposition by altering the chemical composition and associated microbial community of host tissue. Here, we compared litter decomposition of the invasive grass Microstegium vimineum collected from sites with Bipolaris leaf spot symptoms and sites with no apparent disease symptoms in a common garden experiment. Our results revealed that leaf tissue from litter from non-infected sites decomposed more rapidly through the spring than litter from infected sites. Differences in fungal composition between infected and non-infected litter at the start of the experiment largely persisted through the summer. Our work demonstrates that pathogen colonization may facilitate the persistence of infected host litter, potentially slowing the return of nutrients to the environmental pool while also promoting the survival and dispersal of primary inoculum the following season.
Adirondack Public Good Events Database: Natural Resource, Environmental, Economic and Recreation Policy and Common Pool Resources for a Social-Ecological System in Adirondack Park, New York, USA, 1760-2020.
I assembled this dataset from various published sources to evaluate how the social-ecological system (SES) in Adirondack Park, New York changed through time and the interplay of public goods (Common Pool Resources, CPRs), public land rules and private land rights, and related concepts over 260 years (1760-2020). The database was the basis for a doctoral dissertation titled "Blue Lining: Assessing the Resilience of Adirondack Park, New York Using Polycentricity and Panarchy Frameworks." The goal of the dissertation was to assess patterns and changes in institutional rules, actors and arrangements before and after establishment of the public Adirondack Forest Preserve in 1885 and Adirondack Park in 1892 as those actors and rules were modified and as both internal and external events influenced the SES as it moved through different phases of the adaptive cycle through space and time (see panarchy). Using the database, I identified which organizations and events contributed to natural resource and CPR policy. The dissertation can be downloaded here: https://experts.esf.edu/esploro/outputs/99917370604826.
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.