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Fig. 2 in Reassessment of the generic attribution of Numidotherium savagei and the homologies of lower incisors in proboscideans
Fig. 2. Mandibular elements of the proboscidean Arcanotherium savagei (Court, 1995), from the Evaporite Unit (late Eocene) of Dor El Talha, Libya. A. Symphysis (BMNH M. 82164) in occlusal (A1), lateral (A2), and anterior (A3) views; uncrushed incisor loci are outlined in white on A3. B. Part of right mandibular ramus (BMNH M. 82166) with erupting m2 and p4 in occlusal (B1) and lateral (B2) views.
Fig. 6 in Reassessment of the generic attribution of Numidotherium savagei and the homologies of lower incisors in proboscideans
Fig. 6. Phylogenetic relationships among early tethytheres. Most parsimonious tree (L = 381; CI = 0.64; RI = 0.70) obtained from 207 morphological characters. Nodes are identified by letters (A to L). Bremer support is indicated in black under each node.
Fig. 3 in Reassessment of the generic attribution of Numidotherium savagei and the homologies of lower incisors in proboscideans
Fig. 3. Dental elements of the proboscidean Arcanotherium savagei (Court, 1995), from the Evaporite (A and C, late Eocene) and Idam (B, early Oligocene) units of Dor El Talha, Libya. A. Erupting left i1 (BMNH M. 82183) in occlusal (A1), lateral (A2), and anterior (A3) views. B. Right M1 (BMNH M. 82172) in buccal (B1), occlusal (B2), and lingual (B3) views. C. Right M2 (BMNH M. 82398) in buccal (C1), occlusal (C2), and lingual (C3) views. D. Left M3 (MNHN LBE 20) in buccal (D1), occlusal (D2), and lingual (D3) views.
Fig. 6. A. Peraiocynodon major, attributed right upper molar BMNH J. 576 in Docodonts from the British Mesozoic
Fig. 6. A. Peraiocynodon major, attributed right upper molar BMNH J. 576, in labial (A1), lingual (A2), anterior (A3), posterior (A4), and two occlusal (A5, A6) views (one shaded). B. Peraiocynodon major sp. nov., attributed left upper canine BMNH J.212, in labial (B1), lingual (B2), and occlusal (B3) views. Scale bar 1 mm.
Fig. 4. A. Borealestes serendipitus, attributed right upper molar BMNH J. 580 in Docodonts from the British Mesozoic
Fig. 4. A. Borealestes serendipitus, attributed right upper molar BMNH J. 580, in labial (A1), lingual (A2), anterior (A3), posterior (A4), and occlusal (A5) views. B. Borealestes mussetti sp. nov., attributed left upper molar BMNH J.404, in lingual (B1), anterior (B2), posterior (B3), and occlusal (B4) views. C. Borealestes sp., left upper molar BMNH J.455, in labial (C1), lingual (C2), anterior (C3), posterior (C4), and occlusal (C5) views. Scale bar 1 mm.
Impact attribution of European floods, 1950-2020
<p>This collection contains impact attribution of 1729 riverine, flash, coastal and compound floods that affected Europe between 1950 and 2020. Each event was reconstructed with a modelling chain combining hazard, exposure, and vulnerability. A total of six drivers are included in two versions: factual (historical) being the best estimate of the drivers per each event, and counterfactual, being an estimate of the driver under static, 1950 conditions. All combinations of possible factual and counterfactual drivers results in 64 scenarios. A list of all scenarios is contained in 'Attribution_scenarios_list.csv', where the columns are as follows:</p> <ul> <li>Scenario - scenario number used in attribution results </li> <li>Climate_change - effect of changes in river discharges, storm surge heights, wave run-up, and long-term sea level rise</li> <li>Catchment_alteration - effect of changes in land use, reservoir capacity and water demand on the hydrological cycle</li> <li>Exposure_growth - effect of change in population stock and gross domestic product (GDP) at subnational region level (NUTS3)</li> <li>Exposure_local - effect of local-scale exposure changes: land use structure, GDP composition by sector, asset-to-GDP ratio, building of new infrastructure</li> <li>Flood_protection - effect of change in probability that a hydrological flood will cause significant socioeconomic impacts, primarily through the breach of structural defenses</li> <li>Vulnerability - effect of changing relative loss due to local factors, e.g. private precaution, building material, early warning, emergency measures, etc.</li> </ul> <p>'Event_data.zip' contains six files per each flood event identified by their HANZE ID number (https://zenodo.org/records/12635205):</p> <ul> <li>'Attribution_mean_ID.csv' - mean estimate of impact per scenario (fatalities, persons affected, economic loss in 2020 euros)</li> <li>'Attribution_params_ID.csv' - additional impact data per scenario: chance of flood protection failure (fraction), change of fatalities (fraction), relative fatalities (%), relative population affected (%), relative economic loss (%), number of impacted NUTS3 regions, potential absolute flood exposure (fatalities, population affected, economic loss)</li> <li>'Attribution_eco_ID.csv' - mean estimate of economic loss in 2020 euros per scenario per NUTS3 region</li> <li>'Attribution_fat_ID.csv' - mean estimate of fatalities in 2020 euros per scenario per NUTS3 region</li> <li>'Attribution_pop_ID.csv' - mean estimate of population affected in 2020 euros per scenario per NUTS3 region</li> <li>'Attribution_unc_ID.npy' - impact per scenario (fatalities, persons affected, economic loss in 2020 euros), 1000 samples of the uncertainty distribution of impact.</li> </ul> <p>'Aggregated_data.zip' contains six files of aggregated impacts (fatalities, persons affected, economic loss in 2020 euros) per scenario:</p> <ul> <li>'Attribution_aggregate_event_IMPACT.csv' - all events aggregated in one file per impact type, displaying: <ul> <li>HANZE_ID - unique ID of the event in HANZE database</li> <li>Year - year when the event started</li> <li>Code - country two-letter code</li> <li>Type - type of flood</li> <li>scen_X - scenario number X</li> </ul> </li> <li>'Attribution_aggregate_region_IMPACT.csv' - estimated NUTS3-level impacts of all events that affected a given region between 1950 and 2020 (see NUTS3 region map in https://zenodo.org/records/12635205).</li> </ul> <p>Graphs of attribution per event, and maps of aggregated attribution can be viewed online: <a href="https://naturalhazards.eu">https://naturalhazards.eu</a></p> <p>Results can be reproduced using code (HANZE model v2.4) and input data also available from Zenodo.</p>
Supporting dataset for the article 'Unique structural attributes of the PSI-NDH supercomplex in Physcomitrium patens'
<p><strong>Figure </strong><strong>1</strong><strong>:</strong> <strong>Separation and identification of pigment-protein complexes from </strong><strong>Physcomitrium patens</strong><strong> and structural characterization of PSI-NDH/NDH by single particle electron microscopy. a</strong><strong>) </strong>CN-PAGE separation of pigment-protein complexes from the TM from Pp solubilised by 0.5% β-DDM. The image shows the selected gel line scanned at room temperature in transmission mode (colour image) and in fluorescence mode (black and white image). <strong>b)</strong> Immunoblot identification of the NdhH subunit on a PVDF membrane with transferred proteins from the second-dimension denaturing PAGE. Two horizontal bands of the marker (blue colour) designate the molecular weight of proteins at these positions. <strong>c</strong><strong>–</strong><strong>e</strong>) Electron density maps of two forms of PSI-NDH sc and NDH monomer from Pp revealed by single particle electron microscopy (stromal view). The 2D class average of (<strong>c</strong>) the typical form of PSI-NDH sc consisting of 2566 particles, (<strong>d</strong>) the shifted form consisting of 1098 particles, and (<strong>e</strong>) the NDH monomer composed of 2725 particles. Abbreviations: PSII - photosystem II, PSI - photosystem I, LHCII - light-harvesting complex of photosystem II, mc - megacomplex, cc - core complex, mono - monomer, trim – trimer, B1 - C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> band, B2 – C<sub>2</sub>S<sub>2</sub>M band, B3 – C<sub>2</sub>S<sub>2</sub>/C<sub>2</sub>SM band, B4 – PSII cc + PSI band.</p> <p><strong>Figure </strong><strong>2</strong><strong>:</strong> <strong>Structural characterisation of PSI-NDH supercomplex and NDH monomer in </strong><strong>Physcomitrium patens</strong><strong>. a) </strong>Structural model of the PSI-NDH sc in Pp, which corresponds to the arrangements in the PSI-NDH sc from At. <strong>b) </strong>Structural model of the PSI-NDH sc, where PSI is rotated clockwise by 35° in comparison to its position in 2a). <strong>c) </strong>Superposition of the Pp PSI-NDH model with the rotated PSI from image 2b) (in colour) over its un-rotated form from 2a) (grey colour) with pictorial color-coded legend of individual LHCA antennae. <strong>d)</strong> Structural model of the NDH monomer fitted by the truncated At NDH. <strong>e)</strong> Comparison of the Pp NDH model (in colour) versus the complete At NDH model (grey) with highlighted subunits absent in Pp NDH (limon). Structural models of PSI-NDH scs viewed from the stromal side were obtained by a fit of the NDH monomer from At (PDB ID: 7WG5, Su et al., 2022) and PSI complex from Pp (PDB ID: 7KSQ, Gorski et al., 2022). Prior to fitting, the subunits of NDH unencoded in Pp genome were removed from the At NDH structure, namely the PnsB2, PnsB3, and the subunits of SubL. PSI subunits and NDH subcomplexes and selected subunits are color-coded. PSI: forest green - core; pink, white, chocolate, yellow and magenta – LHCA1, LHCA2a, LHCA2b, LHCA5 and LHCA3, respectively. NDH: orange – SubM, cyan – SubB (PnsB1, PnsB4, PnsB5), blue – SubA and NdhT, lime – SubB (PnsB2, PnsB3) and SubL.</p> <p><strong>Supporting Figure S1: Single particle electron microscopy analysis of PSI-NDH supercomplex in <em>Physcomitrium patens</em>. </strong>Representative transmission EM micrographs of the <strong>a)</strong> B1, <strong>b)</strong> B2 and <strong>c)</strong> B3 band containing PSII scs (un-circled particles), PSI-NDH scs (in blue circles) and NDH monomers (in yellow circles); <strong>d)</strong> 2D classification of the PSI-NDH and NDH particles extracted from all micrographs of B1 and B2 bands with the given numbers of particles in each class. The 2478 and 20 454 particles were extracted for B1 and B2 band, respectively, and were further classified into 15 classes.</p> <p><a name="_Hlk177648374"></a><strong>Supporting Figure S</strong><strong>2</strong><strong>: <a name="_Hlk174966851"></a>Relative content of selected proteins in analysed bands (B1–B3). </strong><strong>a) </strong>Relative protein abundance of individual LHCI proteins (including LHCB9) evaluated to the protein content of PSI core (represented by the sum of PsaA and PsaB) in three different bands: B1, B2, and B3.<strong> </strong>The relative protein content of all isoforms representing one LHCA protein (see Table S2) were summarised and subsequently evaluated as one for reducing results complicity. <strong>b)</strong> The relative protein content of PSI (represented by sum of PsaA and PsaB) and PSII (represented by sum of PsbA(D1) and PsbD (D2)) evaluated to the sum of all proteins in specific bands.<strong> c) </strong>The SubA/PSI ratio calculated as the sum of relative protein abundance of SubA NDH subunits divided by the sum of relative protein content of PsaA and PsaB in bands B1 and B2. The relative content (relative PG iBAQ values (riBAQ) in ppm format) of individual LHCA isoforms, LHCB9, SubA subunits, PsaA, PsaB, PsbA (D1) and PsbD (D2) was determined by mass spectrometry in samples prepared from gel bands excised from CN-PAGE (Fig 1a). The presented values are means ± SD from 3 replicates.<strong> </strong></p> <p><strong>Supporting Figure S2 (xlsx format): </strong>Selected mass spectrometry source data for Figure S2(a-c).</p> <p><strong><a name="_Hlk177648454"></a>Supporting Figure S3: Impact of PSI rotation within Pp PSI-NDH sc on the distance between the putative ferredoxin (Fd)-binding sites in NDH and PSI. </strong>Comparison of the distance between Fd-binding site in NDH and PSI in typical (a) and shifted (b) form of PSI-NDH sc. The subunits forming Fd-binding site in NDH according Laughlin et al. (2020): NdhO, NdhI, NdhH (excluding NdhS and NdhV absent in model) and in PSI according Caspy et al. (2020): stromal PsaC-E (excluding PsaA, PsaF) are labelled and colour coded as follows: NDH: limon, blue, orange – NdhO, NdhH, NdhI, respectively; PSI: yellow, violet, and firebrick –PsaC, PsaD, PsaE, respectively, with the rest of the subunits in PSI and NDH in grey colour. The binding positions of the Fd in NDH (O-site) and PSI are illustrated as brown circles. Structural models of PSI-NDH scs viewed from the stromal side were obtained by a fit of the NDH monomer from At (PDB ID: 7WG5, Su et al., 2022) and PSI complex from Pp (PDB ID: 7KSQ, Gorski et al., 2022). Prior to fitting, the subunits of NDH unencoded in Pp genome were removed from the At NDH structure, namely the PnsB2, PnsB3, and the subunits of SubL. Two headed arrows determine the distance between approx. centre of Fd-binding sites in NDH and PSI in two different structures with estimated values in Angstroms. <strong> </strong></p> <p><strong>Supporting Table S1:</strong> <strong>A summary of the identified PSI-NDH supercomplex subunits in </strong><strong>Physcomitrium patens</strong><strong>. </strong>MS analysis of the B2 band (in 3 technical replicates) from CN-PAGE used for transmission EM analysis. The subunits of the PSI and NDH complexes identified in the band after digestion with trypsin and assigned according to UniProt database records are listed. Proteins highlighted in green are not encoded in the Physcomitrium patens genome or do not have any record in the UniProt database, proteins highlighted in orange were not identified in our dataset.</p> <p><a name="_Hlk177048123"></a><strong>Supporting Table S2: The list of all LHCI proteins (including LHCB9) and their isoforms identified in four analysed bands B1, B2, B3 and B4 by mass spectrometry.</strong> Individual rows represent different isoforms of LHCI antennae detected in specific bands by MS, accession numbers according UniProt database in one row represent the same isoform of protein. Letters “Y” and “N” indicate whether the specific protein was detected or undetected in MS dataset of individual bands, respectively. The label “Low” was assigned to those protein that were detected, however, whose abundance was close to the detection limit or they had overall low and negligible abundance relatively to PSI core. For band B1–B3 and B4 three and four technical replicates were analysed, respectively. </p>
Data from: A tale of two studies: detection and attribution of the impacts of invasive plants in observational surveys
1.Short-term experiments cannot characterize how long-lived, invasive shrubs influence ecological properties that can be slow to change, including native diversity and soil fertility. Observational studies are thus necessary, but often suffer from methodological issues. 2.To highlight ways of improving the design and interpretation of observational studies that assess the impacts of invasive plants, we compare two studies of nutrient cycling and earthworms along two separate gradients of invasive shrub abundance. By considering the divergent sampling strategies and statistical analyses of these two studies, and interpreting their contradictory results in the context of other studies, we also aim to better describe the impacts of the focal invader, Rhamnus cathartica. 3.In a new study of a single site in Minnesota, we observed positive correlations between buckthorn abundance and soil pH, soil nutrient pools, nutrient fluxes through leaf litterfall, earthworm abundance, and root biomass. Multiple regression models showed these relationships persisted after accounting for variability in soil texture and tree species composition. For a separate, more expansive study in Illinois, other authors reported little to no correlation between buckthorn abundance and 10 soil properties, including earthworm abundance, pH, and nutrient concentrations. However, like many other studies, their regression models only assessed predictors related to invader abundance. R2 values for models of ecosystem properties ranged from 0-0.79 (adjusted-R2) for our study in Minnesota and from <0.05-0.16 (unadjusted) for the prior study in Illinois. 4.Differences in sampling error and use of predictor variables between the two studies likely explain the contrasting results. 5.Synthesis and applications. To reduce the uncertainty of conclusions from observational studies of invasive plants, future studies must ensure that heterogeneity of soils and vegetation is adequately accounted for in the sampling strategy and statistical analyses (e.g., analysis of covariance, multiple regression). Particular attention should be given to ecosystem properties with variability that likely predates the invader (e.g., geophysical features and tree community composition). In our study, effects of buckthorn on ecosystem properties were not only robust to the inclusion of potentially confounding predictors, but also consistent with expectations based on ecological stoichiometry and mass balance of element flow.
Data from ATTRICI 1.1 - counterfactual climate for impact attribution
<p>Data as produced and presented in the publication</p> <pre><strong>ATTRICI v1.1 - counterfactual climate for impact attribution</strong></pre> <p>in Geoscientific Model Development.</p> <p>Abstract:</p> <p>Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC WGII a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system’s behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause". Impact attribution following this definition remains a challenge because the counterfactual baseline cannot be observed. Process-based and empirical impact models can fill this gap as they allow to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution set-up of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.</p>
FIGURE 2 in Bite marks attributable to Tyrannosaurus rex: preliminary description and implications
FIGURE 2. Bite mark furrows from tyrannosaur "puncture and pull" biting on the dorsal surface of the anterior iliac crest of the Triceratops pelvis (MOR 799). The initial bite penetrated the bone at the wider end of the furrow and was then dragged ascendingly toward the narrower end.
Fig. 2 in Simple relationships to predict attributes of fish assemblages in patches of submerged macrophytes
Fig. 2. Correlation coefficients (Pearson's R) obtained from relationships between fish assemblage attributes (a: density; b: species richness) and habitat variables (macrophyte biomass, BIOM; volume, VOL; proportional volume, %VOL). We show correlation results from all patches (All), patches dominated by E. densa and patches dominated by E. najas. All correlations were statistically significant (p <0.05).
Site characterization, regeneration attributes, and juvenile conifer growth characteristics for 25 forest and woodland sites in the southwestern United States
<p>This dataset contains biotic and abiotic site characterization data, juvenile conifer regeneration and growth data, and detailed juvenile growth characteristic data for destructively sampled juvenile conifers. Data are included for 25 forest and woodland sites in the southwestern United States, and were collected in summer 2019. A detailed sampling and analysis methodology is available from the following publication (in press as of 10-2021):</p> <p>Pirtel NL, Bradford JB, Hubbard RM, Abella SR, Kolb TE, Litvak ME, Porter SL and Petrie MD. 2021. The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States, Ecosphere: in press.</p> <p>Please contact the corresponding author (MD Petrie) with any questions or requests.</p> <p> </p>
Local Optima Network Analysis of Multi-attribute Vehicle Routing Problem
<p>Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), there are other restrictions that model diverse real-life system attributes. Among the most common attributes studied in the literature are the vehicle capacity and the maximum route length constraints. The impact of these restrictions on the overall structure of the problem and on the performance of local search algorithms used to solve it is not well known. This paper aims to explain how constraints impact different variants of VRP by altering the structure of the underlying search space. We focus on the analysis of Local Optima Networks (LON) for multiple Traveling Salesman Problem (m-TSP), and VRP with capacity (CVRP), distance (DVRP), and both (DCVRP) constraints. We present results that indicate that metrics obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the constraints of the problem. <br> The dataset contains the data extracted from the local optima network for a set of variants belonging to the family of vehicle routing problems.</p>
Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes
<p><strong>To watch it in Youtube:</strong></p> <p><a href="https://youtu.be/Uq2qtb4_K2U">https://youtu.be/Uq2qtb4_K2U</a></p> <p><strong>This is a pre-print, please access and cite the published version:</strong></p> <p><a href="https://doi.org/10.1145/3503229.3547057">https://doi.org/10.1145/3503229.3547057</a></p> <p>Automatic analysis of variability is an important stage of <em>Software Product Line</em> (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model.</p> <p>In this paper, we make use of the <em>Quality Variability Model</em> (QVM), based on <em>Category Theory</em> (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE -- the state-of-the-art CT tool.</p>
Masting is shaped by tree-level attributes and stand structure, more than climate, in a Rocky Mountain conifer species
<p>Many tree species mast, meaning seed production is highly variable from year to year and synchronous within a stand, but this phenomenon remains poorly understood. To better understand how a changing climate, altered disturbance regimes, or novel management strategies might affect future seed production, we quantified the joint influence of both biotic (tree size, age, and neighborhood competition) and abiotic factors (climate and weather) on seed production in a widespread conifer species, Rocky Mountain ponderosa pine (<em>Pinus ponderosa</em> var. <em>scopulorum</em>). We reconstructed individual-level annual cone production across a large portion of this species' range using the cone abscission scar method, and mixed models were used to test hypotheses related to the causes and drivers of masting in this species. Our results suggest that masting in ponderosa pine is a process shaped at the individual-level, and this leads to high, local-scale variation in annual cone production. The effects of weather were strongest at climatically marginal sites, but overall, the joint effects of weather and climate only weakly described individual-level patterns of annual cone production in ponderosa pine (R<sup>2</sup><sub>m</sub> = 1.6%, R<sup>2</sup><sub>c</sub> = 30.1%). Rather, we found that masting was strongly influenced by tree- and stand-level factors such as diameter, age, and local neighborhood density, all of which were associated with the mean, interannual variability, and between-tree synchrony of cone production at the individual-level. Larger and older trees produced more cones, more frequently, and with less synchrony than smaller and younger trees. Open-grown trees experiencing lower levels of neighborhood competition also produced more cones with less interannual variability, but with higher between-tree synchrony. Because tree- and stand-level traits appear to regulate seed production more strongly than climate or weather in this species, management interventions targeting these factors could be powerful tools to manage future tree recruitment. Thus, current efforts to reduce stand density and conserve large trees in some ponderosa pine forests may enhance tree-level seed production and reduce variability in seed crops among years.</p>
Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information
<p><strong>Citation</strong></p> <p>Zhang, Alice Tianbo, and Vincent Xinyi Gu. 2023. “Global Dam Tracker: A Database of More than 35,000 Dams with Location, Catchment, and Attribute Information.” <em>Scientific Data</em> 10 (1): 111.</p> <p><a href="https://www.nature.com/articles/s41597-023-02008-2">https://www.nature.com/articles/s41597-023-02008-2</a></p> <p> </p> <p><strong>Description</strong></p> <p>We present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spanning three decades, we demonstrate how GDAT improves upon existing databases to enable the inter-temporal analysis of the costs and benefits of dam construction on a global scale. Our findings show that over the past three decades, dams have contributed to a dramatic increase in global surface water coverage, especially in developing countries in Asia and South America. This is an important step toward a more systematic understanding of the worldwide impact of dams on local communities. By filling in the data gap, GDAT would help inform a more sustainable and equitable approach to energy access and economic development.</p>
FGI on coopetition attributes and coopetition performance_UMO-2020/39/B/HS4/00935
<p>Database contains two sets of transcriptions from focus group interviews. All the details are as follows:</p> <p>- Timeframe: April till May 2021 (specific dates are provided in transriptions);</p> <p>- Informants: acacemics running research on coopetition (3FGI) and managers from firms adopting coopetition strategy (2FGI)</p> <p>- Number of participants: form 2 to 3 in case of academic FGI and 6-7 in case of FGI on managers</p> <p>- Technique: on site FGI in case of managerial FGIs and virtual FGI in case of academic FGI</p> <p>- Selection criteria: academic FGI - all scholars with publications with IF on coopetition; managerial FGI: high-tech and low-tech manufacturing companies using coopetition strategy. </p> <p> </p>
Synthetic Nodules for Evaluation of 3D Deep Learning Segmentation Attribution
<p>Both the algorithm used to generate this dataset as well as the comprehensive evaluation metric for visual explanations are detailed in the paper "Attribution of 3D Deep Learning Segmentation in Medical Imaging".</p> <p>This is a synthetic dataset developed to enable the comprehensive evaluation of visual explanation methods applied to deep learning segmentation predictions. The data provided here include 100 training and 100 testing volumes, binary segmentation labels for model training, and explanation segmentation labels for explanation evaluation.</p> <p>The intended workflow for this dataset is: 1) Train a deep learning segmentation model using the 100 training volumes and binary segmentation labels. 2) Use a method of visual explanation to explain the trained model's segmentation decisions on the 100 testing volumes. 3) Use the explanation labels to evaluate the generated explanations.</p> <p> </p> <p>The folders <em>imagesTr</em> and <em>imagesTs</em> contain the training and testing image volumes, respectively. A <em>dataset.json</em> file has also been generated for the images to enable training using the nnUNet pipeline.</p> <p>The folders <em>labelsTr</em> and <em>labelsTs</em> contain the training and testing binary segmentation labels. In both cases, 1 indicates foreground voxels (spiculated nodule) and 0 indicates background voxels.</p> <p>The folders <em>labelsTr_full</em> and <em>labelsTs_full</em> contain the training and testing explanation labels. In both cases, 1 indicates non-spiculated nodule, 2 indicates spiculation structure (discriminating background), 3 indicates spiculated nodule body (segmentation foreground), and 0 indicates background.</p>
Site characterization, water balance modeling, and regeneration attributes of managed and unmanaged ponderosa pine sites in the southwestern United States
<p>This dataset contains biotic and abiotic site characterization data and SOILWAT2 water balance model simulation outputs (two daily outputs: 1915-2011, 1980-2020) for 77 ponderosa pine forest sites in the southwestern United States. Data were collected in summer 2019 and summer 2021. Overviews of the sampling and modeling methodologies are detailed in the following publications:</p> <p>Pirtel NL, Bradford JB, Hubbard RM, Abella SR, Kolb TE, Litvak ME, Porter SL and Petrie MD. 2021. The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States, Ecosphere 12: e03839, doi:10.1002/ecs2.3839.</p> <p>Petrie MD, Hubbard RM, Bradford JB, Kolb TE, Moser WK, Noel A, Schlaepfer DR, Bowen MA, Fuller LR and Moser WK. 2023. Widespread regeneration failure in ponderosa pine forests of the southwestern United States, Forest Ecology and Management: in press.</p> <p> </p>
Fig. 3 in The Importance Of Artificial Wetlands In The Conservation Of Wetland Birds And The Impact Of Land Use Attributes Around The Wetlands: A Study From The Ajara Conservation Reserve, Western Ghats, India
Fig. 3. Classification of wetland birds based on feeding guild recorded at five artificial wetlands during 2011– 2015: A — Gavase wetland; B — Dhangarmola wetland; C — Khanapur wetland; D — Erandol wetland; E — Ningudage wetland.
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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.