Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13,276
datasets available to search
ShareScore release 0.9.0
Dataset results
13,276 results for “IT management”
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 1. Examples for Today Well Manageable and not yet Well Manageable Processes in Automation
<p>Despite the many progresses that have been achieved in automation and AI over the last decades, applications have mainly been successful for circumscribed, well-defined tasks in environments that are relatively specific, well-structured, and characterized by a limited number of possible occurring processes, states, and ways how to react to them [59]. There, technical solutions can even exceed certain human capabilities. The situation changes however if we switch to systems that should perform a broader range of tasks in less well-structured environments. Here, the limits of technical feasibility are being stretched to the utmost [7]. This fact is probably best illustrated by<br> the following two concrete examples in Figure 1.</p>
Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The complete agents “environment” is shown in figure 6. Each agent receives the necessary<br> environmental or plant condition measurements. While each agent keeps its autonomy and has its<br> own “personal” goals, it interacts with the other agents through some specific communication<br> language, in the “Agent discussion area”, where the overall goal is taken into account. This<br> interaction takes place under the presence of information from the existing models and the target<br> goals. At that point, each agent has decided on its best strategy.</p>
Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p> The value of the final product incorporates not only quantity issues but also<br> quality issues, which are difficult to be measured or even estimated. The environment of each agent<br> is defined by the same parameters that define the physical environment in addition to internal states<br> reported by each agent.</p>
Data set on soil physicochemical parameters, biomass accumulation and carbon credit generation in different management systems in Rio Verde, GO, Brazil
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on soil organic carbon and its role in mitigating climate change through carbon sequestration on agricultural lands in Rio Verde, GO, Brazil. With the global imperative to reduce anthropogenic CO2 emissions, our data highlights the effectiveness of no-till agricultural practices in both improving soil quality and enhancing carbon storage. This collection represents extensive soil and biomass sampling from five distinct areas within the Cerrado region, utilizing three priority management systems:</p> <p>No-till with soybean and maize in sequence under rainfed conditions. No-till with soybean and maize in sequence with central pivot irrigation. First and second cuts of sugarcane. The samples were meticulously collected post-harvest and used to estimate both soil biomass accumulation and carbon stock indices. A thorough analysis of the soil's physicochemical parameters was conducted for the 0-20 cm soil profile in each area. This dataset not only provides a valuable resource for studying the impact of different no-till practices on carbon sequestration but also serves as a critical input for modeling future contributions of conservation management systems to carbon trading markets.</p> <div> <div> </div> <div> <h2>Data Contents</h2> </div> <p>Soil organic carbon measurements for various no-till systems. Biomass accumulation data post-harvest. Carbon stock indices derived from biomass samples. Detailed physicochemical profiles of soil samples.</p> <div> <h2>Significance</h2> </div> <p>This dataset is pivotal for researchers and policymakers focusing on the potentials of agricultural carbon sequestration and its implications for carbon trading schemes. It offers insights into the current contributions of no-till conservation management systems and aids in the development of future strategies to enhance carbon</p> <h1>Metadata Description and Script</h1> </div> <p>This repository contains two key data files that encapsulate diverse aspects of soil physicochemical parameters, biomass accumulation, and carbon credit generation across different management systems in Rio Verde, GO, Brazil. Below are descriptions of each file's contents and structure.</p> <div> <h2>all.txt</h2> </div> <p>This text file presents aggregated data from various sites under different agricultural management systems. Each row in the dataset represents measurements from distinct sample plots, with the following fields:</p> <ul> <li><code>Sites</code> - Identifier for the plot location.</li> <li><code>SB</code> - Soil bulk density (g/cm³).</li> <li><code>SOC</code> - Soil organic carbon (%).</li> <li><code>Stock</code> - Carbon stock (ton/ha).</li> <li><code>Biomass</code> - Biomass accumulation (ton/ha).</li> <li><code>Credits</code> - Estimated carbon credits (ton CO2 equivalent/ha).</li> </ul> <div> <h2>Quimica.xlsx</h2> </div> <p>This Excel file provides detailed physicochemical analyses of soil samples from different management zones in the study area. The data is structured to support in-depth analysis of soil characteristics influencing carbon sequestration capabilities. Each sheet in the workbook corresponds to a specific area, with columns typically representing:</p> <ul> <li><code>pH</code> - Soil pH, indicating the acidity or alkalinity.</li> <li><code>EC</code> - Electrical conductivity (dS/m).</li> <li><code>Cation Exchange Capacity (CEC):</code> - (meq/100g).</li> <li><code>Organipont c Matter:</code> - (%).</li> <li><code>NPK levels</code> - Concentrations of Nitrogen (N), Phosphorus (P), and Potassium (K).</li> </ul>
Fig. 1 in Description of an integrated management system for invasive mosquitoes at entry-exit ports in Zhejiang, China
Fig. 1 Framework of integrated management system for invasive mosquitoes at Zhejiang ports, China. Abbreviation: HC, Hangzhou Customs
Fig. 4 in Description of an integrated management system for invasive mosquitoes at entry-exit ports in Zhejiang, China
Fig. 4 Tools for online classification and taxonomy of mosquito identification keys using Lucid on the front-page internet (http://www.livefarm.cn/ lucid/mosquito)
Fig. 5 in Description of an integrated management system for invasive mosquitoes at entry-exit ports in Zhejiang, China
Fig. 5 Related supplemental information on exotic mosquitoes obtained from the identification keys on the front-page internet
Fig. 3 in Description of an integrated management system for invasive mosquitoes at entry-exit ports in Zhejiang, China
Fig. 3 Annual trends and species abundance of captured mosquitoes from 2011 to 2017 in the Zhejiang Province, China. a Dynamic variation of captured invasive mosquitoes. b Pie chart of mosquito species composition
Fig. 2 in Description of an integrated management system for invasive mosquitoes at entry-exit ports in Zhejiang, China
Fig. 2 Geographical distribution of surveillance points in Zhejiang Province, China (obtained from the National Platform for Common Geospatial Information Services)
State of Practice and Challenges of Issue Management for Component-based and Service-Oriented Systems: An Empirical Study
<p>Modern software systems often consist of independently developed components, e.g., libraries or (micro-)services, that can be combined to form a larger architecture. Many of these components are developed externally, i.e., in other projects by other teams, which is particularly the case in service-oriented architectures. Even though the components are independently developed, bugs may arise that propagate along the call chains, which sometimes negatively affect the calling components. However, as such components are typically managed in separate issue management systems (IMS), identifying that a downstream component is affected by an upstream component's issues is complex and time-consuming. Even though some scientific works and IMS forums discuss how to manage such cross-component issues, there is no systematic study on the state of practice and the challenges of issue management when different components are affected. Therefore, this study aims to determine which types of components are usually included in modern software projects, how developers identify and manage issue propagation between components, and how often they face such propagations. To get these insights, we conducted an empirical study through a questionnaire with 64 industry experts and open-source contributors and interviewed seven of them. Our key takeaways help researchers and software engineers to understand possible impacts and to improve cross-component issue management. We identified, for example, that related issues currently are non-semantically linked via URLs in issue comments.</p>
Maize management and yield of smallholder farmers in Sub-Saharan Africa between 2016 and 2022
<p>Yield and management practices data were collected from smallholders’ maize fields from 2016 to 2022. All fields corresponded to maize grown in pure stands (no intercropping). Data were collected from five maize producing regions in Sub-Saharan Africa: (i) north-central Nigeria (<em>n</em> = 115), (ii) Rwanda and Burundi (<em>n</em> = 2720), (iii) central Zambia (<em>n</em> = 861)<strong>,</strong> (iv) southwest Tanzania (<em>n</em> = 3710), and (v) eastern Uganda and western Kenya (<em>n</em> = 7367). Data were collected by One Acre Fund (https://oneacrefund.org/), an NGO that provides smallholder farmers access to agricultural training, credit, crop insurance services, and farming supplies. About half of the fields in the database comprised farmers who subscribed to the One Acre Fund program and the other half farmers who did not. </p> <p>Maize grain yield, plant density, and row spacing were measured in two randomly placed boxes of 36 square meters at harvest, avoiding field edges. Field geolocation was recorded in 70% of the observations. When missing, the field geolocation was defined based on the nearby town (21%) or associated district (9%) location for the purpose of retrieving climate data. Management practices associated with each field were reported by farmers, including sowing and harvest dates, cultivar name, fertilizer inputs (types and total quantities for both organic and inorganic), fertilization method, liming, weeding, and pesticides (mainly insecticides to control fall armyworms). Farmers also reported the incidence of adversities (such as pests, diseases, Striga witchweed, hail, and excess water). Field size was reported by farmers and, in those cases in which farmers could not provide an accurate measure of their field size, or there was a strong indication of mistakes (e.g., nutrient fertilizer rates out of range), One Acre Fund personnel took in-situ measurements to determine field size. Input rates per hectare were calculated as the ratio of the farmer-reported input amount and field size. Data were subjected to quality control to remove unlikely values. Maize yield outliers were detected with a Bonferroni Outlier Test. Observations with plant densities and fertilizer rates higher than four standard deviations from the mean were excluded as well as those without geolocation, no N or P data, and atypical sowing dates. After quality control, the database contains a total of 14,773 field observations.</p> <p>Inorganic fertilizer rates were converted to nutrient rates (in elemental nutrients) following typical fertilizer nutrient contents. Organic fertilizers were encoded separately in two binary variables and one continuous variable, indicating whether compost was used, if that compost contained manure, and compost application rate. Likewise, cultivars were classified into hybrids or open pollination varieties (OPVs), which included local varieties, retained seed, and improved OPVs. For hybrids, we retrieved the associated crop cycle maturity (short, medium, and long), disease tolerance traits, and year of release from companies’ seed catalogs. Reported incidence of diseases and insect pests (e.g., anthracnose, aphids, blight, cutworms, drought, fall armyworm, stemborer, termites, and stalk or kernel rot) were simplified to two binary variables indicating whether the crop was affected by pests and/or diseases. Infestation by parasitic witchweeds (Striga hermonthica and S. asiatica) was considered as a separate variable. Fertilization methods were also simplified to whether the fertilizer was applied inside a hole or broadcasted in the surface. Number of weeding operations was simplified to zero, one or two or more weeding per season. Sowing dates were expressed as a deviation from the estimated average sowing date for each climate zone-season combination. Fields were grouped based on their location using the climate zone scheme developed by the Global Yield Gap Atlas Project (www.yieldgap.org). Isolated observations (more than three standard deviations from the median distance across sites within the climate zone) were excluded from their group. In the case of climate zones with two maize seasons, each crop season was considered as a separate group. Field elevation was retrieved from the Amazon Web Services Terrain Tiles. Total precipitation during the growing season, as well as for early, flowering, and grain filling phases, was retrieved from CHIRP. For observations with field-level coordinates data, root-zone plant-available water-holding capacity was retrieved from the World Soil Information database, and soil clay content, pH, organic carbon, and effective cation exchange capacity from iSDA. Lastly, the topography wetness index (TWI) was calculated from the elevation data. </p> <p>Table 1. List of survey-derived variables.</p> <div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>plant_date_dev</td> <td>discrete</td> <td>days</td> <td>sowing date deviation from cluster average</td> </tr> <tr> <td>pl_m2</td> <td>continuous</td> <td># m2</td> <td>plant density (plants per area)</td> </tr> <tr> <td>row_spacing</td> <td>continuous</td> <td>cm</td> <td>distance between rows</td> </tr> <tr> <td>hybrid</td> <td>binary</td> <td>-</td> <td>Was a commercial hybrid seed used?</td> </tr> <tr> <td>hyb_mat</td> <td>ordinal</td> <td>-</td> <td>hybrid maturity (early, medium, late)</td> </tr> <tr> <td>hyb_yor</td> <td>continuous</td> <td>-</td> <td>Year of release of the cultivar</td> </tr> <tr> <td>hyb_tol_mln</td> <td>binary</td> <td>-</td> <td>Tolerance to maize lethal necrosis</td> </tr> <tr> <td>hyb_tol_msv</td> <td>binary</td> <td>-</td> <td>Tolerance to maize streak virus</td> </tr> <tr> <td>hyb_tol_gls</td> <td>binary</td> <td>-</td> <td>Tolerance to gray leaf spot</td> </tr> <tr> <td>hyb_tol_nclb</td> <td>binary</td> <td>-</td> <td>Tolerance to northern corn leaf blight</td> </tr> <tr> <td>hyb_tol_rust</td> <td>binary</td> <td>-</td> <td>Tolerance to rust</td> </tr> <tr> <td>hyb_tol_ear_rot</td> <td>binary</td> <td>-</td> <td>Tolerance to ear rot</td> </tr> <tr> <td>N_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>N fertilization rate</td> </tr> <tr> <td>P_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>P fertilization rate</td> </tr> <tr> <td>K_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>K fertilization rate</td> </tr> <tr> <td>compost</td> <td>binary</td> <td>-</td> <td>Was compost applied?</td> </tr> <tr> <td>comp_t_ha</td> <td>continuous</td> <td>t/ha</td> <td>compost rate</td> </tr> <tr> <td>manure</td> <td>binary</td> <td>-</td> <td>Did the compost contain manure?</td> </tr> <tr> <td>fert_in_hole</td> <td>binary</td> <td>-</td> <td>Was the fertilizer applied in a hole?</td> </tr> <tr> <td>lime_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>lime rate</td> </tr> <tr> <td>weeding</td> <td>discrete</td> <td>#</td> <td>number of times the plot was weeded</td> </tr> <tr> <td>pesticide</td> <td>binary</td> <td>-</td> <td>Was any pesticide applied?</td> </tr> <tr> <td>disease</td> <td>binary</td> <td>-</td> <td>Was yield affected by diseases?</td> </tr> <tr> <td>pest</td> <td>binary</td> <td>-</td> <td>Was yield affected by pests?</td> </tr> <tr> <td>striga</td> <td>binary</td> <td>-</td> <td>Was yield affected by the Striga weed?</td> </tr> <tr> <td>water_excess</td> <td>binary</td> <td>-</td> <td>Was yield affected by water excess (heavy rain or flooding)?</td> </tr> </tbody> </table> <p> </p> <p><strong>Table 2. List of environmental variables. </strong></p> <div> </div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Spatial resolution</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>GDD</td> <td>°C days</td> <td>30 arc-sec (1km)</td> <td>Growing degree days</td> <td>www.worldclim.org</td> </tr> <tr> <td>AI</td> <td>unitless</td> <td>30 arc-sec (1km)</td> <td>Aridity Index (annual precipitation over potential evapotranspiration)</td> <td>www.worldclim.org</td> </tr> <tr> <td>TS</td> <td>°C</td> <td>30 arc-sec (1km)</td> <td>Temperature seasonality</td> <td>www.worldclim.org</td> </tr> <tr> <td>season_prec</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Total rainfall during the maize season (10% of planting to 50% of the harvest)</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_1</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the first third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_2</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the second third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_3</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the last third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>elev</td> <td>m.a.s.l.</td> <td>75 meters</td> <td>Elevation (altitude) above sea level</td> <td>registry.opend26ata.aws/terrain-tiles</td> </tr> <tr> <td>soil_rzpawhc</td> <td>mm</td> <td>1 km</td> <td>Root zone plant-available water holding capacity</td> <td>www.isric.org</td> </tr> <tr> <td>soil_clay</td> <td>%</td> <td>30 meters</td> <td>Clay content at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_pH</td> <td>-</td> <td>30 meters</td> <td>pH (H2O) at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_orgC</td> <td>g/kg</td> <td>30 meters</td> <td>Organic carbon at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_ECEC</td> <td>cmolc/kg</td> <td>30 meters</td> <td>Effective cation exchange capacity at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>twi</td> <td>unitless</td> <td>75 meters</td> <td>Topographic Wetness Index</td> <td>calculated from elevation</td> </tr> </tbody> </table> </div> </div> </div>
Land management controls on soil carbon fluxes in Asia's largest tropical grassland
<p>Data files for 'Land management controls on soil carbon fluxes in Asia’s largest tropical grassland', submitted to Ecological Indicators on 15 May 2024.</p>
Datasets for input and output of INFORM Severity-based SMAA study of resource allocation in humanitarian aid and disaster management under climatic losses and damages
<p>The landscape of climate change and extreme events will remain a wicked problem for equitable and forward-looking resource prioritisation. The question of how to couple climate and multi-risk information remains. IPCC has considered that multi-criteria decision analysis (MCDA) can help.</p> <p>We use stochastic multi-attribute analysis (SMAA), a variant of MCDA, to compute prioritisations of climatic losses & damages (l&d) for fragile countries with a humanitarian response plan. SMAA is combined with the INFORM Severity index, measuring the status of crises and disasters, and preferences gathered from stakeholders (e.g., United Nations, European Union, World Bank, the research and public sector, civil society).</p> <ul> <li><strong>Dataset S1. </strong>XLS-file with all the input data compiled from sources, concurrent data manipulation, and descriptions of steps taken until ready for the SMAA.</li> <li><strong>Dataset S2.</strong> XLS-file with results of the SMAA for all weight schemes and concurrent analysis, such as sensitivity heat mapping, correlations, regressions, and Tukey mean-difference plot.</li> </ul>
Data from: Growth and longevity of the endangered freshwater pearl mussel (Margaritifera margaritifera): Implications for conservation and management
<p>Key life-history data, such as growth and age, are necessary to effectively manage and conserve threatened freshwater mussel species. Traditionally growth and age studies require large yet destructive sample sizes covering all age classes. Such methods pose a risk to populations of conservation concern, and therefore alternative methods that need only limited sample sizes are necessitated to prevent further threats to such populations. We applied retrospective shell growth at age reconstructions to 98 critically endangered freshwater pearl mussel (FPM) individuals from 34 populations across Finland and Sweden, enabling the use of extremely small sample sizes (n = 1–6 per population). We compared the performance of six different growth models with the reconstructed size-at-age data across FPM juvenile (<20 years old) and adult life stages. The growth reconstruction model showed reasonable skill in reconstructing FPM growth patterns. The von Bertalanffy model was shown to be a good general descriptor of growth for FPM, but it systematically underestimated the asymptotic size. The power law model was the most accurate in estimating juvenile growth (lowest deviances from the size-at-age data). FPM showed great variability in longevity (A<sub>max</sub> = 54–254 years) and growth constant k (0.018– 0.057 year<sup>-1</sup>). Our results show that reasonable estimates of growth can be attained even when sample sizes are extremely limited. The results can be further applied to gain knowledge on the population's age structure, size at maturation, and recovery potential. The methodology is applicable to other freshwater mussel species of conservation concern.</p>
Challenges faced by adolescent girls on Menstrual Hygiene Management: School-based study, Siha, Kilimanjaro, Tanzania
<p><span>Menstrual hygiene management (MHM) has received increasing attention as a public health issue globally. Governments and stakeholders have started to engage communities to address barriers and challenges faced by adolescents in and out of school. </span><span>This study, conducted in Siha District, northern Tanzania, responds to the call for evidence sensitive to local barriers and challenges to inform successful strategies in MHM. </span></p> <p><strong><span>Methods:</span></strong></p> <p><span>Institutional-based cross-sectional study which involved 400 school girls aged 10 to 19 years old who attained menarche were randomly selected in four secondary and advanced level government schools from September 2019 to January 2020. Bivariate and multivariable logistic regression analysis were employed. A P-value less than 0.05 was used to declare statistical significance.</span></p> <p><strong><span>Results:</span></strong></p> <p><span>Among all the girls who participated in the study 30% reported missing school due to menstruation while 56% of the girls reported using toilets as changing places at school. The use of reusable sanitary material was 52% compared with non-reusable materials which was 48%. In urban areas, 34.5% of students reported missing school due to menstruation compared to 25% who reported in rural areas. The findings show that school absenteeism among adolescent girls during menstruation is significantly associated with a headache (Adjusted odds ratio (AOR) = 3.3 (95% CI:1.32 – 8.23)) and abdominal waist pain (AOR = 8.50 (95% CI: 6.27 – 15.56)),</span> <span>lack of changing rooms in school (AOR = 5.85 (95% CI: 4.82 – 7.93)). In addition, the high cost of sanitary pads was mentioned as one of the main reasons for students not using sanitary pads.</span></p> <p><strong><span>Conclusion:</span></strong></p> <p><span>This study calls for promoting MHM-friendly practices in schools to create a supportive and conducive learning environment for adolescent girls. Ongoing school infrastructure improvements such as the construction of classrooms and toilets in schools should include the construction of proper changing places to reduce the number of adolescent girls who miss school due to menstruation. </span></p> <p><span> </span></p>
Fig. 5 in Captive management, reproduction, and comparative larval development of Klappenbach's Red-bellied Frog, Melanophryniscus klappenbachi Prigioni and Langone, 2000
Fig. 5. Body size of different test groups. (A) Single tadpole, O‒1, and (B) five tadpoles per box, O‒5, in osmosis water. (C) Single tadpole, P‒1, and (D) five tadpoles per box, P‒5, in pond water.
Fig. 2. Keeping and rearing M in Captive management, reproduction, and comparative larval development of Klappenbach's Red-bellied Frog, Melanophryniscus klappenbachi Prigioni and Langone, 2000
Fig. 2. Keeping and rearing M. klappenbachi. (A) Terrarium of the adult group housing eight specimens. (B) Rearing of the tadpole test groups in a climate chamber.(C) Rearing containers for the young toadlets.
Fig. 1 in Captive management, reproduction, and comparative larval development of Klappenbach's Red-bellied Frog, Melanophryniscus klappenbachi Prigioni and Langone, 2000
Fig. 1. Melanophryniscus klappenbachi. (A) Dorsal and (B) ventral view of an adult female. (C) Amplexus.(D) Egg clump attached to moss. (E) Contrasting photo of a tadpole, used for evaluating the growth.
Fig. 4 in Captive management, reproduction, and comparative larval development of Klappenbach's Red-bellied Frog, Melanophryniscus klappenbachi Prigioni and Langone, 2000
Fig. 4. (A) Mortality rate of different test groups until metamorphosis. (B) Average growth rate of the different test groups. (C) Number of tadpoles metamorphosed per day after hatching (O = osmosis water, P = pond water, number indicates individuals per container).
Fig. 3 in Captive management, reproduction, and comparative larval development of Klappenbach's Red-bellied Frog, Melanophryniscus klappenbachi Prigioni and Langone, 2000
Fig. 3. Developing coloration in young toadlets of different ages. (A) Recently metamorphosed toadlet. (B) Ten days after metamorphosis. (C) Twenty-three days after metamorphosis. (D) Two months after metamorphosis.
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.