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1,465 results for “resilience”
Data from: 42 years of no-tillage and cover cropping improved soil oxygen availability and resilience
<p>Healthy soil air-water balance is critical for crop growth. Conservation agricultural practices improve soil physical properties to influence soil oxygen availability. We evaluated the impact of 42 years of hairy vetch (HV) cover cropping (CC) and no-tillage (NT) on soil oxygen dynamics during a cotton growing season experiencing multiple intensive rain events in silt loam soil. HV and NT treatments exhibited higher growing season soil oxygen availability (<em>p </em>< 0.05), and experienced 3 to 4 times fewer hours of oxygen limitation (i.e., oxygen concentration <10%) as compared to no cover (NC) and conventional tillage (CT) treatments. After heavy rainfall, NT-HV treatment exhibited the highest soil oxygen availability, followed by NT-NC, CT-HV, and CT-NC treatments (<em>p </em>< 0.05). While CC and/or NT treatments quickly regained soil oxygen status within 24 hours after saturating rain events, CT-NC suffered from sub-optimal soil aeration until the third day post-rainfall. The combination of CC with NT practices enhanced soil oxygen availability and resilience to extreme precipitation events.</p>
Interactions between phenanthrene exposure and historical chemical stress: Implications for fitness and ecological resilience of the sentinel species Daphnia magna
<div> <p>Polycyclic aromatic hydrocarbons (PAHs) arise from incomplete combustion of oil, coal, and gasoline, with lipophilic properties facilitating their widespread distribution and persistence. Due to their biochemical attributes, PAHs can accumulate in animal tissues, potentially causing mutagenic and carcinogenic effects. Since the industrial revolution, PAH concentrations in the environment have risen, with some areas showing levels from 0.159 to 33,090 µg/kg sediment. Despite acute toxicity studies showing adverse effects on freshwater organisms, the long-term impacts and synergistic interactions with other pollutants remain largely unexplored. </p> </div> <div> <p>This study investigates the impact of phenanthrene (PHE), a prominent PAH found in aquatic environments, on <em>Daphnia magna</em>, a species of significant ecological importance in freshwater ecosystems globally, being both a sentinel species for chemical pollution and a keystone organism in freshwater aquatic ecosystems. Leveraging the dormancy of <em>D. magna</em>, which spans decades or even centuries, we exposed strains with diverse histories of chemical contaminant exposure to environmentally relevant concentrations of PHE. Initially, acute exposure experiments were conducted in accordance with OECD guidelines across 16 <em>Daphnia</em> strains, revealing substantial variation in acute toxic responses, with strains naïve to chemical pollutants showing the lowest toxicity. Utilizing the median effect concentration EC10 derived from acute exposures, we assessed the impacts of chronic PHE exposure on the life history traits and ecological endpoints of the 16 strains. To elucidate how historical exposure to other environmental stressors may modulate the toxicity of PHE, temporal populations of <em>D. magna</em> resurrected from a lake with a well-documented century-spanning history of environmental impact were utilized. Our findings demonstrate that PHE exposure induces developmental failure, delays sexual maturation, and reduces adult size in <em>Daphnia</em>. Populations of <em>Daphnia</em> historically exposed to chemical stress exhibited significantly greater fitness impacts compared to naïve populations. This study provides crucial insights into the augmented effects of PAHs interacting with other environmental stressors. </p> </div>
Open Data in German Forest Information Systems: Towards an EU Forest Resilience Monitor (Collection of Forest Resilience Indicators)
<p>This dataset represents the collection of forest indicators for my Master's Thesis in the pioneer master programme at the Universities of Münster, Tallinn (Taltech) and Leuven (KUL), titled "Open Data in German Forest Governance: Towards an EU Forest Resilience Monitor". </p> <p>It contains a classification of inicators sourced from:</p> <ul> <li> <p><span>Nikinmaa, L., Lindner, M., Cantarello, E., Jump, A. S., Seidl, R., Winkel, G., & Muys, B. (2020). Reviewing the use of resilience concepts in forest sciences. <em>Current Forestry Reports, 6</em>, 61-80.</span></p> </li> <li> <p><span>European Commission. (2023a). Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on a monitoring framework for resilient European forests. <em>Directorate-General for Environment</em>. Lastly retrieved on December 17, 2023 from </span><span><a href="https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2023)728&amp;lang=en&lang=en"><span>https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2023)728&amp;lang=en&lang=en</span></a></span><span>. </span></p> </li> <li> <p><span>European Parliament. (2024, February 27). European Parliament legislative resolution of 27 February 2024 on the proposal for a regulation of the European Parliament and of the Council on nature restoration (COM(2022)0304 – C9-0208/2022 – 2022/0195(COD)). Lastly retrieved on April 26, 2024 from </span><span><a href="https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html"><span>https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html</span></a></span><span>. </span></p> </li> </ul>
Climate Change Adaptation, Social Resilience, and Perceived Values Data from Turkana, Machakos and Narok County, Kenya
<p>This dataset provides socioeconomic and value perception interview data collected from 1,020 individuals living across three counties in Kenya: Turkana, Machakos and Narok. Socioeconomic data were collected on housing, healthcare, water sources, electricity access, experience of extreme weather events, community services and access to information. Value perception data were collected using the user-perceived value (UPV) method - a perception-based surveying approach which requires interviewees to select their most valued household items in different circumstances and explain their choice through 'why'-probing. This is done under different circumstances - here, either in daily life, or in the face of a climate shock. The data are made available with sub-county level geospatial attribution. Together, the socioeconomic and interview data can be used to better understand the views of different communities and demographic groups concerning climate change and extreme weather events across Kenya. They also provide insight as to the intrinsic, social, emotional, epistemic, functional, and indigenous values associated with everyday household items. The data can be used by policymakers to inform development planning and to identify gaps in available infrastructure. Additionally, researchers and development practitioners can use these data to design interventions which reflect the needs and values of communities in Kenya.</p>
Regional irrigation expansion can support climate resilient crop production in post-invasion Ukraine
<p>This dataset contains results used to plot figure 1, 2, 3, 4 of the manucript "Regional irrigation expansion can support climate resilient crop production in post-invasion Ukraine" published in Nature Food. </p>
E-storage driven sustainable and resilient city renaissance with lifecycle carbon footprints and levelized costs of carbon abatement
<p>The dataset contains: The simulation results of the energy balance data, battery capacity sizing data and battery degradation data. The data set also includes calculations and results based on the net present value, carbon emission, levelized cost of storage (LCOS), levelized cost of energy (LCOE) and levelized cost of carbon abatement (LCCA).</p>
Data and Code for "No general support of functional diversity enhancing resilience across terrestrial plant communities"
<p>The data and code provided here is to support the study "No general support of functional diversity enhancing resilience across terrestrial plant communities" </p> <p>This repository contains the following files:</p> <p> The code to reproduce main analysisi and graphs in R and HTML format</p> <ul> <li>RcodeNoGeneralSupport.R</li> <li>RcodeNoGeneralSupport.html</li> </ul> <p>The data to be used for the different analyses</p> <ul> <li>ResilienceFDIndices.csv</li> <li>ResilienceFDIndices-BiomassH.csv</li> <li>ResilienceFDIndices-BiomassW.csv</li> <li>ResilienceFDIndices-CompositionW.csv</li> <li>ResilienceFDIndices-Herbaceous.csv</li> <li>ResilienceFDIndices-woody.csv</li> <li>ResilienceSR.csv</li> <li>ResilienceSR-BiomassH.csv</li> <li>ResilienceSR-BiomassW.csv</li> <li>ResilienceSR-CompositionW.csv</li> <li>ResilienceSR-herbaceous.csv</li> <li>ResilienceSR-woody.csv</li> </ul> <p>The detailed information for each variable in each data set</p> <ul> <li>README.txt</li> </ul>
Data from: A genome-guided strategy for climate resilience in American chestnut restoration populations
<p>The American chestnut (<em>Castanea dentata</em>) is a functionally extinct tree species that was decimated by an invasive fungal pathogen in the early 20<sup>th</sup> century. An understanding of the genomic architecture of local adaptation in wild American chestnut was necessary in order to deploy locally adapted, disease-resistant American chestnut populations. Here, we characterize the genomic basis of climate adaptation in remnant wild American chestnut, develop new computational methods, and evaluate the adaptive genomic content captured within backcross breeding populations. Whole genome re-sequencing data of 356 trees from Sandercock et al. (2022) coupled with genotype-environment association methods identified 18483 climate associated loci.</p>
Datased for a publication: "Harmonizing Microstructures and Enhancing Mechanical Resilience: Novel Powder Metallurgy Approach for Zn-Mg Alloys"
<p>These data are published as a part of the paper: Harmonizing Microstructures and Enhancing Mechanical Resilience: Novel Powder Metallurgy Approach for Zn-Mg Alloys. The structure and organization of the data are outlined in the readme file. </p> <p> </p> <p><strong>Dataset versions:</strong></p> <p><strong>V1:</strong> Original version of the data.</p>
Investigating the resilience of termite communities to logging and climate change in Borneo
<b>Description: </b><p>This project set out to quantify the tolerance of termite communities to climate change, or more specifically, temperature and humidity change, two climatic variables that have been hypothesised to drive species distributions (particularly for small ectotherms such as termites). The data presented here are the tolerances of termites to increasing temperatures, and decreasing humidities. <br><br>The thermal data was recorded by inserting termites into individual glass vials, placing those sealed vials into a water bath, and increasing the temperature until they could no longer function. This temperature was recorded, and taken as CTmax (Critical Thermal Maximum), for each individual termite. These data can be found in the TemperatureData worksheet. <br><br>The humidity data was recorded slightly differently. Groups of termites (of the same genus) were weighed and placed in one of two types of glass vial. Dessicated vials also contained silica gel (and a barrier to prevent termite interaction with the gel) which reduced the humidity to an average of 30%. Control vials did not contain any silica gel and had an average humidity of 85%. These vials were removed at one of 5 time points, and the termites were weighed again, and weight change was recorded. This weight change was attributed to water loss. <br><br>The body water data was used to calculate the proportion of body mass that was water, for multiple termite genera. This was done so that percentage of body water lost could be calculated for the humidity experiment, rather than an absolute value of water loss (as termites vary in size, using absolute values would cause false conclusions). </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/31"><b>Investigating the resilience of termite communities to logging and climate change in Borneo</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=33">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Thermal tolerance data</b> (Worksheet TemperatureData)</p><p>Dimensions: 1256 rows by 17 columns</p><p>Description: Thermal tolerance data of termites</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that each termite was taken from (Field type: ID)</li><li><b>Day</b>: The day on which the experiment took place (Field type: ID)</li><li><b>Termite_no</b>: The unique termite number, missing numbers are due to non-experimental deaths (Field type: ID)</li><li><b>Experiment</b>: Whether it was the first or second experiment from the same colony (Field type: Replicate)</li><li><b>Family</b>: The family of the termite (Field type: ID)</li><li><b>Genus</b>: The genus of the termite (Field type: ID)</li><li><b>Species</b>: The species (where known) of the termite (Field type: ID)</li><li><b>Name</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>CTmax</b>: The critical thermal maximum of the termite, or the temperature that it died at (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Nest_type</b>: The type of nest that the termite builds (Field type: Categorical Trait)</li><li><b>Nest_Layer</b>: The layer within the forest that the nest is built (Field type: Categorical Trait)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Humidity tolerance data</b> (Worksheet HumidityData)</p><p>Dimensions: 169 rows by 16 columns</p><p>Description: Humidity tolerance data of 4 termite genera</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tube</b>: The unique tube number that the termites were placed in (Field type: ID)</li><li><b>Taxa</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Time</b>: The five time points that the tubes were removed at (Field type: Numeric)</li><li><b>Treatment</b>: Whether the termites were placed in a control or desiccated tube (Field type: Categorical)</li><li><b>Initial</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Finish</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage_lost</b>: Proportion of body mass change (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>No_termites</b>: Number of termites placed in the tube (Field type: Numeric)</li><li><b>No_dead</b>: Number of termites that were dead at the point of the second weighing (Field type: Numeric)</li><li><b>Percentage_Dead</b>: Percentage of termites that are dead at point of second weighing (Field type: Numeric)</li></ul><br></li><li><p><b>Termite total body water data</b> (Worksheet BodyWaterData)</p><p>Dimensions: 38 rows by 12 columns</p><p>Description: Data calculating the total body water of 4 termite genera, this data was used in the humidity data to calculate the percentage of body water lost during the experiment</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tray_no</b>: The unique tray number that the termites were placed in (Field type: ID)</li><li><b>Genus</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Weight_start</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Weight_end</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage</b>: Percentage of body mass that is water (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li></ol><p><b>Date range: </b>2016-02-01 to 2016-07-01</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Isoptera<br> -  -  -  -  - <i>Homallotermes</i><br> -  -  -  -  -  - [<i>Homallotermes foraminifer</i>]<br> -  -  -  - Kalotermitidae<br> -  -  -  -  - <i>Glyptotermes</i><br> -  -  -  -  -  - [Glyptotermes sp.]<br> -  -  -  - Rhinotermitidae<br> -  -  -  -  - <i>Coptotermes</i><br> -  -  -  -  -  - [Coptotermes sp.]<br> -  -  -  -  - <i>Parrhinotermes</i><br> -  -  -  -  -  - [<i>Parrhinotermes pygmaeus</i>]<br> -  -  -  -  - <i>Schedorhinotermes</i><br> -  -  -  -  -  - [<i>Schedorhinotermes sarawakensis</i>]<br> -  -  -  -  -  - [Schedorhinotermes sp.]<br> -  -  -  - Termitidae<br> -  -  -  -  - <i>Bulbitermes</i><br> -  -  -  -  -  - [Bulbitermes sp.]<br> -  -  -  -  - <i>Dicuspiditermes</i><br> -  -  -  -  -  - [Dicuspiditermes sp.]<br> -  -  -  -  - <i>Globitermes</i><br> -  -  -  -  -  - [<i>Globitermes globosus</i>]<br> -  -  -  -  - <i>Hospitalitermes</i><br> -  -  -  -  -  - [<i>Hospitalitermes hospitalis</i>]<br> -  -  -  -  -  - [<i>Hospitalitermes bicolour</i>]<br> -  -  -  -  - [Lacessitermes sp.]<br> -  -  -  -  - <i>Longipeditermes</i><br> -  -  -  -  -  - [Longipeditermes sp.]<br> -  -  -  -  - <i>Macrotermes</i><br> -  -  -  -  -  - [<i>Macrotermes gilvus</i>]<br> -  -  -  -  - <i>Microcerotermes</i><br> -  -  -  -  -  - [Microcerotermes sp.]<br> -  -  -  -  - <i>Nasutitermes</i><br> -  -  -  -  -  - [<i>Nasutitermes havilandi</i>]<br> -  -  -  -  -  - [Nasutitermes sp.]<br> -  -  -  -  - <i>Odontotermes</i><br> -  -  -  -  -  - [Odontotermes sp.]<br></div><p></p>
Development and Implementation of Sustainable Transportation Resilience Indicators
<p>Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system-wide capability to maintain desired performance levels, while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time.</p> <p>In an earlier work, the authors developed a classification scheme to aid in identification, selection and application of community sustainable resilience indicators that can be tailored to a community’s needs in operationalizing the assessment process. These indicators were characterized according to whether they aligned with social, economic or environmental systems that are necessary for a community to achieve a sustainable resilience domain of survival, well-being, or full preparedness.</p> <p>Of the critical infrastructure systems that support these systems and domains, transportation is arguably the most important. This is based on the premise that transportation is a means to an end, providing the mobility that enables a community to establish and maintain a social, economic and environmental fabric. Whether it involves an educational, medical, recreational, religious, work or other purpose, absent a safe and reliable transportation system, none of these activities can be satisfactorily pursued. Moreover, in times of crisis, transportation serves as a vital artery for enabling access to and egress from impacted areas.</p> <p>The objective of this project was to establish and demonstrate a method for evaluating a community’s transportation resilience, such that if deficiencies exist, attention can be focused on mitigating those concerns. This approach was designed around the scenario of a river valley community exposed to the threat of a significant flood event, with the expectation that the methodology has the potential to be extended to assess community resilience to other natural and manmade hazards.</p>
Constructing a Comprehensive Disaster Resilience Index: The case of Italy
<p>The file contains raw data used to construct disaster resilience index for Italian municipalities.</p>
Assessing the Resilience of Software Systems by Application of Chaos Engineering - A Case Study
<p>This repo contains all supplementary data sets that I have created and used throughout my bachelor's thesis. In particular, it contains<br> - JMeter configuration file<br> - CSV files of measured JMeter runs<br> - Chaos Experiments declared in JSON<br> - Created boxplots and ecdf plots based on the CSV files<br> - Kubernetes deployments<br> - Bash scripts <br> </p>
Identifying Hot Spots in Rural Community Resilience in Scotland - database
<p>Database of nearest resilience infrastructure from each Scottish postcode, commissioned and funded by the National Centre for Resilience.</p> <p>Data are in <a href="http://www.geopackage.org/">geopackage format</a>, which is curated by the open geospatial consortium. The data format can be read by GDAL, and hence all major analytical and spatial software (e.g. R, Python, QGIS). The database has two main tables:</p> <ul> <li>datazones - a spatial table of boundary polygons. Available from <a href="https://data.gov.uk/dataset/ab9f1f20-3b7f-4efa-9bd2-239acf63b540/data-zone-boundaries-2011">data.gov.uk</a> under the <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">open government licence</a>.</li> <li>postcode_to_POI - a table of postcode to POI type distances with lookups for a range of administrative boundaries.</li> </ul> <p>An example query (in R) is shown in 10.5281/zenodo.3386179 to access data in the geopackage.</p> <p>The database was built using the following datasets:</p> <ul> <li><a href="https://www.ordnancesurvey.co.uk/business-and-government/products/os-open-roads.html">Ordnance Survey Open Roads</a></li> <li><a href="https://www.ordnancesurvey.co.uk/business-and-government/products/code-point-open.html">Ordnance Survey Code-Point Open</a></li> <li><a href="https://digimap.edina.ac.uk/webhelp/os/data_information/os_products/points_of_interest.htm">Ordnance Survey Points of Interest</a></li> </ul> <p>The following copyright licences apply to this dataset:</p> <p>© Crown Copyright and Database Right 2019. Ordnance Survey (Digimap Licence).<br> This material includes data licensed from PointX Database Right/Copyright 2019.<br> Contains NRS data © Crown copyright and database right 2019.</p>
Modeling Sulfate Attack in Modern Concrete for Building Sustainable and Resilient Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17CTAM01. Abstract of the final report is stated below for reference:</p> <p>"External sulfate attack is a complex phenomenon and is manifested in the form of large expansion, cracking, and spalling depending on the exposure solution and material constituent properties. Several models were developed in the past to demonstrate sulfate attack mechanisms that account for the diffusion of sulfate ions into the porous concrete and the successive deformation triggered by the chemical reaction and precipitation of expansive agents. However, none of these models accounts for the effect of the migration of solvent water from the low solute concentration solution to high solute concentration solution driven by the osmotic pressure. Osmotic pressure is believed to cause spalling and cracking of concrete substrates coated with semipermeable membrane that prohibits diffusion of ions from the surroundings into the porous body. In order to determine the effect of osmotic pressure on the deformation of concrete exposed to sulfate solution, a coupled poromechanical model has been developed. Sensitivity analysis has been performed to investigate the effect of material constituent properties and exposure solution on the osmotic pressure induced damage propensity of concrete. It has been found that concrete surface can exhibit high instantaneous tensile stress developed by the gradient in the salt concentration between the pore solution and external surroundings."</p>
A Preliminary Evaluation of the Shame Resilience Theory
<p>Raw data set from study as well as scored data for each measure and scored data for ICIAI sub-scales. </p>
Disaster Resilient and Self-Assessing Multifunctional Transportation Structures
<p>Corresponding data set for Tran-SET Project No. 18STTAM02. Abstract of the final report is stated below for reference:</p> <p>"This research designs and characterizes multifunctional materials, in particular inexpensive shape memory alloys, for transportation structures that possess excellent mechanical properties and self-sensing capabilities for strengthening and health monitoring. The properties are the Fe-SMAs are sensitive to part size in that the grain size of the material, which can be grown to several inches, should exceed the smallest dimension of the part. In the current work, maximum part size of the large dimension Fe-SMA rods were determined through detailed microstructural investigations. Samples were subjected to abnormal grain growth heat treatments and found out that part size be increased up to 4.6 mm. Work to correlate computational and experimental work concerning the magnetic sensing of Fe-SMA transformation was conducted using via tensile loading of Fe-SMA wire. A model was developed to simulate a grain by grain transformation of a wire with large (4mm) grains along the wire, modeled as partitioned segments."</p>
Data from the article "Short-interval wildfire and drought overwhelm boreal forest resilience" Whitman et al., Scientific Reports, 2019
<p>Field data collected in the Northwest Territories and Wood Buffalo National Park, as well as plot locations, for the study "Short-interval wildfire and drought overwhelm boreal forest resilience" by Whitman et al. in Scientific Reports, 2019.</p> <p>For metadata or assistance please contact the authors. If you intend to publish research using these data, please contact and inform the authors, and cite the source article.</p>
Ecosystem_resilience_study_vegetation_images
<p>Processed and cropped satellite images of vegetation patterns in the Sahel.</p>
Figure 7 in Substratum stability and coral reef resilience: insights from 90 years of disturbances on a reef in American Samoa
Figure 7. Acropora muricata colony having stability by being wedged into the loose substratum.
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.