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.
376
datasets available to search
ShareScore release 0.9.0
Dataset results
376 results for “local scales”
CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments require local-scale climate scenarios. The climate change projections from <span>Global Climate Models (GCMs) </span>are difficult to use at local scale due to their <span>coarse spatial and temporal resolution. </span><span>It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of </span>realisations <span>to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A</span><span> <span>dataset of future climate change scenarios was therefore generated at </span></span><span>26 representative sites across the UK</span><span> based on the latest </span><span>CMIP6 multi-model ensemble </span><span>downscaled to local-scale by using a </span><span>stochastic weather generator LARS-WG 7.0. The data set provides </span><span>1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (</span><span>Shared Socioeconomic Pathways - SSPs <em>viz</em>. </span>SSP2-4.5 and <span>SSP5-8.5)</span><span>.</span><span> </span><span>A total of </span>15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. <span>LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. </span>Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, <em>viz</em>.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs.<span> <span>The 1000 years of </span></span>realisations <span>of daily weather for the baseline as well as future climate change scenarios are helpful for estimating </span>seasonality and<span> inter-annual variation, and for detecting short, </span>low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset <span>can be used as an input to climate change impact models in various fields, including, </span><span>land and water resources, agriculture and food production, </span>ecology and epidemiology, and <span>human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</span></p>
Local scale carbon and nitrogen cycling in temperate forests, eastern U.S., 2017-2018
Data collected in 2017-2018 from individual mature canopy trees (and their surrounding soil) and from monospecific common garden plots to assess how aboveground and belowground carbon and nitrogen cycling are related. Data include foliar, litter, root, and soil carbon and nitrogen pools and fluxes. Field sites span the eastern United States, including south-central Indiana (Moores Creek), Maryland (Smithsonian Environmental Research Center), Pennsylvania (Pennsylvania State University common garden), and Massachusetts (Harvard Forest).
Seasonal hindcast of temperature and precipitation at a local scale by using TeWA approach
<p><strong>Methodology</strong></p> <p>Data set of simulated time-series of temperature and precipitation for the 1982-2020 period. Our statistical seasonal prediction model have two main components: a) the ocean-atmosphere coupling represented by correlations between surface variables with delayed teleconnections and b) the self-predictability of the residual anomalies by trends or cycles (quasi-oscillations).</p> <p>The approach has three stages approach with two main predictor components, as mentioned above. The first two stages consist of separate predictions, one per each component, and the third stage is a combination of both predictions (Fig. 2): Teleconnection-based approach (Redolat et al. 2019, 2020) and a self-predictability by using Wavelet-ARIMA models (Conejo et al. 2005; Joo and Kim 2015). Therefore, the total method is a Teleconnection+Wavelet+ ARIMA (TeWA) approach.</p> <p><strong>References</strong></p> <p>Conejo, A.J., M.A. Plazas, R. Espinola, A.B. Molina, 2005: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst., 20, 1035-1042, https://doi.org/10.1109/TPWRS.2005.846054.</p> <p>Joo, T., S. Kim, 2015: Time series forecasting based on wavelet filtering. Expert Syst. Appl. 42, 3868-3874. https://doi.org/10.1016/j.eswa.2015.01.026</p> <p>Redolat, D., R. Monjo, C. Paradinas, J. Pórtoles, E. Gaitán, C. Prado-López, and J. Ribalaygua, 2020: Local decadal prediction according to statistical/dynamical approaches. Int. J. Climatol., 40: 5671–5687. https://doi.org/10.1002/joc.6543.</p> <p>Redolat, D.; R. Monjo, J.A. Lopez-Bustins, and J. Martin-Vide, 2019: Upper-Level Mediterranean Oscillation index and seasonal variability of rainfall and temperature. Theor. Appl. Climatol., 135: 1059–1077. https://doi.org/10.1007/s00704-018-2424-6.</p>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
TBPos: Dataset for Large-Scale Precision Visual Localization (database files)
<p>Large-scale dataset for visual localization, provided in the format of the well-known InLoc dataset (Taira et al, 2018). Contains co-registered RGB point clouds and a script for generating the rest of the 'database' files for visual localization by the InLoc algorithm. Note: query images are provided in a separate repository.</p>
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania
<h3><strong>Reference</strong></h3><p>Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. <a href="https://doi.org/10.1073/pnas.2207398120">https://doi.org/10.1073/pnas.2207398120</a> </p><h3><strong>Abstract</strong></h3><p>Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (<i>P</i> = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.</p><h3><strong>Replication Data</strong></h3><p>We include anonymized household survey data from our analysis to support open and reproducible science. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the <i>hh_data</i> folder and includes:</p><ul><li><i>hh_imputed10_2018:</i> anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times</li><li><i>hh_compensation_2018:</i> anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs</li><li><i>hh_migration_2018:</i> anonymized household extract for 2018 representing household migration behavior following LSLAs</li><li><i>hh_rsdata_2018:</i> extracted remote sensing data at the household geo-location for 2018</li><li><i>hh_land_<strong>2019</strong>:</i><strong> </strong> anonymized household extract for <strong>2019 </strong>of land variables</li></ul><p>Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in <i>lsms_data</i> folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). </p><p>Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the <i>gis_data</i> folder.</p><p>Finally, our data replication includes several model outputs (found in <i>mod_outputs)</i>, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our <i>main_analysis.Rmd</i> script. </p><h3><strong>Replication Code</strong></h3><p>We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:</p><ul><li><i>main_analysis.Rmd:</i> main analysis supporting the finding, graphs, and tables reported in our main manuscript</li><li><i>compensation.R:</i> analysis of benefits and compensation received directly by households from LSLAs</li><li><i>landvalue.R:</i> analysis of household land values as a function of distance from LSLAs</li><li><i>migration.R:</i> analysis of migration behavior following LSLAs</li><li><i>selection_bias.R:</i> analysis of LSLA selection bias between control and treatment enumeration areas</li></ul>
Dataset for Local Communication in Small-Scale PV Systems: Study on Inverter - Smart Meter PLC Communication
<p>This study investigates communication technologies and protocols for small-scale photovoltaic (PV) systems, focusing on the interaction between inverters and smart meters. The research evaluates the performance of Power Line Communication (PLC) technologies, comparing both narrowband (NB-PLC) and broadband (BB-PLC) options. The analysis identifies MODBUS protocol limitations and highlights the benefits of advanced protocols like DLMS/COSEM and DNP3 for enhanced efficiency and reliability. Field tests demonstrate the viability of PLC for residential PV systems, with narrowband PLC showing better performance over longer distances. Future work aims to optimize PLC communication, digitize ripple control signals, and develop a Multi-Radio and Cable Access Technology (Multi-RCAT) module. This module will integrate various communication technologies, enabling flexible and redundant local communication behind utility sub-meters. These advancements will support real-time production and consumption control, contributing to the efficient and sustainable operation of decentralized energy systems.</p>
Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"
<p>As the 3rd generation partnership project (3GPP) organization pushes out new releases,<br>positioning in heterogeneous mobile networks enables the achievement of the accuracy required<br>in the majority of industrial applications without dependence on global navigation<br>satellite systems (GNSS). This study presents the results gathered during an extensive measurement<br>campaign related to the practical applicability of localization in next-generation<br>heterogeneous networks. We present an accuracy comparison of basic timing advance (TA)<br>localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF),<br>extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural<br>network. Our results demonstrate that TA cannot be considered an optimal solution<br>from the perspective of localization accuracy because the error roughly corresponds to the<br>average separation distance from the base station (BS) to the end device (ED). In addition,<br>we found that the LSTM approach is not optimal for the outdoor localization of moving<br>ED because of the combination of multiple factors, with sparse deployment being the most<br>important. The median value of the location error of the LSTM was more than 200m higher<br>than that of the TA for the self-validation dataset. However, a simple KNN regression shows<br>solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN<br>provided the most accurate results of all methods, with median error values of approximately<br>12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.</p>
Raw data: Local-scale feedbacks influencing cold-water coral growth and subsequent reef formation
<p>Spreadsheets with the raw data of ADV-measured current velocity, coral growth derived from buoyant weight measurements and stress-related protein activities and concentrations.</p>
Animations: Turbulence sets the length scale for planetesimal formation:Local 2D simulations of streaming instability and planetesimal formation
<p>This repository includes three animations discussed in the accepted version of ''Turbulence sets the length scale for planetesimal formation:Local 2D simulations of streaming instability and planetesimal formation" to appear in the Astrophysical Journal. </p> <p>Short descriptions of the animations are as follows:</p> <p>Movie 1: Simulations Ae3L0005 and Ae3L0005. Both use $\St= 0.1$ particles, but only the larger box shows collapse and planetesimal formation. See also: <a href="https://youtu.be/gkHiluqH8HY">https://youtu.be/gkHiluqH8HY</a> </p> <p>Movie 2: The evolution of $\St= 0.1$ pebbles for all 6 different box sizes in Table 1. See also: <a href="https://youtu.be/nA87-9_trUc">https://youtu.be/nA87-9_trUc</a></p> <p>Movie 3: The evolution of $\St= 0.01$ pebbles. See also: <a href="https://youtu.be/CCywDPKVU8w">https://youtu.be/CCywDPKVU8w</a></p>
Rapid in situ diversification rates in Rhamnaceae explain the parallel evolution of high diversity in temperate biomes from global to local scales
<p>The macroevolutionary processes that have shaped biodiversity across the temperate realm remain poorly understood and may have resulted from evolutionary dynamics related to diversification rates, dispersal rates, and colonization times, closely coupled with Cenozoic climate change.</p> <p>We integrated phylogenomic, environmental ordination, and macroevolutionary analyses for the cosmopolitan angiosperm family Rhamnaceae to disentangle the evolutionary processes that have contributed to high species diversity within and across temperate biomes.</p> <p>Our results show independent colonization of environmentally similar but geographically separated temperate regions mainly during the Oligocene, consistent with the global expansion of temperate biomes. High global, regional, and local temperate diversity was the result of high <em>in</em> <em>situ</em> diversification rates, rather than high immigration rates or accumulation time, except for Southern China, which was colonized much earlier than other regions. The relatively common lineage dispersals out of temperate hotspots highlights strong source-sink dynamics across the cosmopolitan distribution of Rhamnaceae.</p> <p>The proliferation of temperate environments since the Oligocene may have provided the ecological opportunity for rapid <em>in</em> <em>situ</em> diversification of Rhamnaceae across the temperate realm. Our study illustrates the importance of high <em>in</em> <em>situ</em><strong> </strong>diversification rates for the establishment of modern temperate biomes and biodiversity hotspots across spatial scales.</p>
Data from: Seasonal bee communities vary in their responses to resources at local and landscape scales: Implication for land managers
<p><strong>Context</strong>:<em> </em>There is great interest in land management practices for pollinators; however, a quantitative comparison of landscape and local effects on bee communities is necessary to determine if adding small habitat patches can increase bee abundance or species richness. The value of increasing floral abundance at a site is undoubtedly influenced by the phenology and magnitude of floral resources in the landscape, but due to the complexity of measuring landscape-scale resources, these factors have been understudied.</p> <p><strong>Objectives</strong>: To address this knowledge gap, we quantified the relative importance of local versus landscape scale resources for bee communities, identified the most important metrics of local and landscape quality, and evaluated how these relationships vary with season.</p> <p><strong>Methods</strong>: We studied season-specific relationships between local and landscape quality and wild-bee communities at 33 sites in the Finger Lakes region of New York, USA. We paired site surveys of wild bees, plants, and soil characteristics with a multi-dimensional assessment of landscape composition, configuration, insecticide toxic load, and a spatio-temporal evaluation of floral resources at local and landscape scales.</p> <p><strong>Results</strong>:<em> </em>We found that the most relevant spatial scale and landscape factor varied by season. Early-season bee communities responded primarily to landscape resources, including the presence of flowering trees and wetland habitats. In contrast, mid to late-season bee communities were more influenced by local conditions, though bee diversity was negatively impacted when sites were embedded in highly agricultural landscapes. Soil composition had complex impacts on bee communities, and likely reflects effects on plant community flowering. </p> <p><strong>Conclusions</strong>:<em> </em>Early-season bees can be supported by adding flowering trees and wetlands, while mid to late-season bees can be supported by local addition of summer and fall flowering plants. Sites embedded in landscapes with a greater proportion of natural areas will host a greater bee species diversity.</p>
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F).
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D).
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D).
nal; E', F', internal, G'-J', Pal. 1341 (peripheral 8); G', H', external; I', J', internal; K'-N', Pal. 1343 (peripheral 9); K', L', external; M', N', internal; O'-R', Pal. 1345 (peripheral 10); O', P', external; Q', R', internal; S'-V', Pal. 1348 (peripheral 11); S', T', external; U', V', internal views; W', reconstruction of carapace Thick lines correspond to scute sulci, dotted lines denote plate sutures, and oblique lines indicate missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Spr, supracaudal; Ve, vertebral. Scale bars: A-V', 1 cm; W', 2.5 cm. in Fossil turtles from the early Miocene localities of Mokrá-Quarry (Burdigalian, MN4), South Moravian Region, Czech Republic
nal; E', F', internal, G'-J', Pal. 1341 (peripheral 8); G', H', external; I', J', internal; K'-N', Pal. 1343 (peripheral 9); K', L', external; M', N', internal; O'-R', Pal. 1345 (peripheral 10); O', P', external; Q', R', internal; S'-V', Pal. 1348 (peripheral 11); S', T', external; U', V', internal views; W', reconstruction of carapace Thick lines correspond to scute sulci, dotted lines denote plate sutures, and oblique lines indicate missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Spr, supracaudal; Ve, vertebral. Scale bars: A-V', 1 cm; W', 2.5 cm.
sal; W, visceral; X-Z, Pal. 1308 (costal 5); X, Y, dorsal; Z, visceral; A'-C', Pal. 1309 (costal 6); A', B', dorsal; C', visceral; D'-F', Pal. 1310 (costal 8); D', E', dorsal; F', visceral; G'-J', Pal. 1312 (peripheral 1); G', H', dorsal; I', J', visceral; K'-N', Pal. 1313 (peripheral 7); K', L', dorsal; M', N', visceral; O'-R', Pal. 1314 (peripheral 8); O', P', dorsal; Q', R', visceral views; S', reconstruction of carapace. Thick lines indicate to scute sulci, dotted lines sutures and oblique lines denote missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Ve, vertebral. Scale bars: 1 cm. in Fossil turtles from the early Miocene localities of Mokrá-Quarry (Burdigalian, MN4), South Moravian Region, Czech Republic
sal; W, visceral; X-Z, Pal. 1308 (costal 5); X, Y, dorsal; Z, visceral; A'-C', Pal. 1309 (costal 6); A', B', dorsal; C', visceral; D'-F', Pal. 1310 (costal 8); D', E', dorsal; F', visceral; G'-J', Pal. 1312 (peripheral 1); G', H', dorsal; I', J', visceral; K'-N', Pal. 1313 (peripheral 7); K', L', dorsal; M', N', visceral; O'-R', Pal. 1314 (peripheral 8); O', P', dorsal; Q', R', visceral views; S', reconstruction of carapace. Thick lines indicate to scute sulci, dotted lines sutures and oblique lines denote missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Ve, vertebral. Scale bars: 1 cm.
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
Text-fig. 8. SEM (a, b) and SRXTM (c, d) images of fruit associated with Miranthus elegans and Miranthus kvacekii; Mira locality, Portugal. a: Apical view of capsular fruit with five, partly open valves revealing the enclosed reticulate seeds (arrows). b: Detail of fruit wall showing an enclosed seed (arrow). c: Transverse section (orthoslice xy1200) of fruit showing central column (cc) of placenta and numerous angular and bitegmic seeds; note that the outer integument (black arrow) is thicker than inner integument (white arrow). d: Longitudinal section (orthoslice yz1239) of fruit showing perigynous attachment of calyx, central column (cc) of the placenta and sections through seeds. Specimen, Mira 99-S156331 (a–d). Scale bars = 600 µm (a, c, d), 200 µm (b). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 8. SEM (a, b) and SRXTM (c, d) images of fruit associated with Miranthus elegans and Miranthus kvacekii; Mira locality, Portugal. a: Apical view of capsular fruit with five, partly open valves revealing the enclosed reticulate seeds (arrows). b: Detail of fruit wall showing an enclosed seed (arrow). c: Transverse section (orthoslice xy1200) of fruit showing central column (cc) of placenta and numerous angular and bitegmic seeds; note that the outer integument (black arrow) is thicker than inner integument (white arrow). d: Longitudinal section (orthoslice yz1239) of fruit showing perigynous attachment of calyx, central column (cc) of the placenta and sections through seeds. Specimen, Mira 99-S156331 (a–d). Scale bars = 600 µm (a, c, d), 200 µm (b).
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.