Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

Differences in visual-field sensitivity in the inferior hemifield between the eyes affect three-dimensional shape perception in glaucoma

Raw data repository for the article:   Differences in visual-field sensitivity in the inferior hemifield between the eyes affect three-dimensional shape perception in glaucoma   Hiromasa Sawamura1, Ryo Asaoka1,2,3,4,5, Hiroshi Murata1, Eriko Ando1, Céline R. Gillebert6, James T. Todd7, Guy A. Orban8   1 Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan. 2 Department of Ophthalmology, Seirei Hamamatsu General Hospital, Hamamatsu, Shizuoka, Japan. <ol> <li>Seirei Christopher University, Hamamatsu, Shizuoka, Japan.</li> </ol> 4  Nanovision Research Division, Research Institute of Electronics, Shizuoka University, Shizuoka Japan. 5  The Graduate School for the Creation of New Photonics Industries, Shizuoka Japan. <ol> <li>Department of Brain and Cognition, KU Leuven, Leuven, Belgium.</li> <li>Department of Psychology, The Ohio State University, Columbus, OH, USA.</li> <li>Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy.</li> </ol>   Excel data: Binocular Visual-filed sensitivity for 20 subjects: BinocularHFAData.xlsx Visual filed sensitivity in the right eye: OD_HFA_data.xlsx  Visual filed sensitivity in the left eye: OS_HFA_data.xlsx Error-in-depth for 20 subjects: ErrorinDepth_20subj.xlsx Results of Simple feature discrimination: Result_SinpleFeatureDiscrimination.xlsx

opencc-by-4.0Dec 2022View details →

Shaping Responsibility in an Applied Research Center

A unit at TECNALIA (a research and technological development centre in Spain), called Shape Lab aims to systematically introduce and implement RRI principles and reflect on the relationship between science and society in TECANALIA. The Lab is led by POINT (Policies for Innovation and Technology), a cross-cutting policy team from Tecnalia, which enriches TECNALIA’s value proposition due to its capacity to incorporate diverse political, social, and economic dimensions based on a better understanding of innovation ecosystem dynamics. TECNALIA is the largest center for applied research and technological development in Spain, a benchmark in Europe, and a member of the Basque Research and Technology Alliance.

opencc-by-4.0Oct 2022View details →

Pulse, Shunt and Storage: Hydrological Contraction Shapes Processing and Export of Particulate Organic Matter in River Networks

This repository hosts the code to run the analysis presented in  "Pulse, Shunt and Storage: Hydrological Contraction Shapes Processing and Export of Particulate Organic Matter in River Networks"  by Catalan et al., Ecosystems, 2022. DOI: https://doi.org/10.1007/s10021-022-00802-4 The code has been tested on Matlab R2019b. Run the Main.m code to produce the results of Figure 3 and 4 of the paper. Each simulation takes about 8 hours on a standard Desktop computer.

opencc-zeroDec 2022View details →

Individual flowering phenology shapes plant-pollinator interactions across ecological scales affecting plant reproduction

1. The balance of pollination competition and facilitation amongst co-flowering plants and abiotic resource availability can modify plant species and individual reproduction. Floral resource succession and spatial heterogeneity modulate plant-pollinator interactions across ecological scales (individual plant, local assemblage, interaction network of agroecological infrastructure across the farm). Intraspecific variation in flowering phenology can modulate the precise level of spatio-temporal heterogeneity in floral resources, pollen donor density and pollinator interactions that a plant individual is exposed to, thereby affecting reproduction. 2. We tested how abiotic resources and multi-scale plant-pollinator interactions affected individual plant seed set, modulated by intraspecific variation in flowering phenology and spatio-temporal floral heterogeneity arising from agroecological infrastructure. We transplanted two focal insect-pollinated plant species (Cyanus segetum and Centaurea jacea, n = 288) into agroecological infrastructure (10 sown wildflower, 6 legume-grass strips) across a farm-scale experiment (125 ha). 3. We applied an individual-based phenologically explicit approach to match precisely the flowering period of plant individuals to the concomitant level of spatio-temporal heterogeneity in plant-pollinator interactions, potential pollen donors, floral resources and abiotic conditions (temperature, water, nitrogen). 4. Individual plant attractiveness, assemblage floral density and conspecific pollen donor density (C. jacea) improved seed set. Network linkage density increased focal species' seed set and modified the effect of local assemblage richness and abundance on C. segetum. Mutual dependence on pollinators in networks increased C. segetum seed set, while C. jacea seed set was greatest where both specialization on pollinators and mutual dependence was high. Abiotic conditions were of little or no importance to seed set. 5. Intra- and interspecific plant-pollinator interactions respond to spatio-temporal heterogeneity arising from agroecological management affecting wild plant species reproduction. The interplay of pollinator interactions within and between ecological scales affecting seed set implies a co-occurrence of pollinator-mediated facilitative and competitive interactions among plant species and individuals.

opencc-by-4.0Jan 2023View details →

CAD files of the spirally shaped chemiluminescence flow cells with distinct cross-sectional geometries and confluence types

CAD files of (i) 3D-printed chemiluminescence flow-through cells with triangular, circular, semicircular, square and irregular pentagonal cross-section geometries, (ii) serpentine mixing coil, and (iii) T, Y and Y'-junction formats (in STL format, ready to print) associated to Figures 1, 2 and S1 of the paper "3D-printed chemiluminescence flow cells with customized cross-section geometry for enhances analytical performance" published in Talanta 2023 (DOI:10.1016/j.talanta.2022.124211)

opencc-zeroJan 2023View details →

Data for: Space-for-time substitution reveals a hump-shaped distribution of dung beetles

Unravelling how climate change impacts the diversity and distribution patterns of organisms is a major concern in ecology, especially with climate-sensitive species, such as dung beetles. Often found in warmer weather conditions, beetles are used as bio-indicators of environmental conditions. By using an altitudinal gradient as a proxy for climate change (i.e., space-for-time substitution), we assessed how changes in climatic variables, such as temperature and precipitation, impact patterns of dung beetles diversity and distribution in the Peruvian Andes. We recorded dung beetles diversity using three different types of baits, feces, carrion, and fruits, distributed in 18 pitfall traps in five different altitudinal sites (from 900 to 2500 m, 400 m apart from each other) in the rainy and dry seasons. We found that (i) dung beetles richness and abundance were influenced by the climate gradient, (ii) seasonality influenced beetle richness, which was high in the wet season, but did not influence abundance, (iii) dung beetle richness and abundance fit to a hump-shaped distribution pattern along the altitudinal gradient, and (iv) species richness is the beta diversity component that best describes the composition of dung beetle species along the altitudinal gradient. Our data show that the distribution and diversity of dung beetles are different at larger scales, with different patterns resulting from the response of species to both abiotic and biotic factors.

opencc-zeroJan 2023View details →

Explosive Cenozoic origin and diversity-dependent diversification dynamics shaped the evolution of Australian skipper butterflies

Australia was predominantly tropical for most of the early Cenozoic, then transitioned to a cooler and drier climate in the Oligocene. In response to this increasing aridity, some lineages adapted to more xeric ecosystems, contracted, or became restricted to increasingly fragmented mesic refugia, or went extinct. Yet, the lack of macroevolutionary studies at a continental scale precludes a better understanding of Australian biodiversity patterns and processes during the Cenozoic. Here, we infer a robust dated phylogenomic tree for a radiation of Australian endemic butterflies, the Trapezitinae skippers, to test the impact of biotic and abiotic drivers on Cenozoic diversification dynamics in Australia. These butterflies originated during the Eocene (ca. 42 Ma) in the mesic biome of Australia. Trapezitinae exploded in diversity during a cool, dry period in the late Oligocene and early Miocene, then experienced a sharp deceleration in speciation. Xeric ecosystems appear to have been colonized more recently, supporting the hypothesis of arid and semi-arid biomes as evolutionary sinks. Temperature-dependent and phytophagy-dependent diversification models received little support. Instead, we find evidence for diversity-dependent processes with a declining diversification in Trapezitinae likely linked to limited ecological opportunities following a rapid initial burst of diversification.

Giant worm-shaped ESCRT-scaffolds surround actin-independent integrin clusters, data part 6

Part 6 of data for the article Giant worm-shaped ESCRT-scaffolds surround actin-independent integrin clusters.

Giant worm-shaped ESCRT-scaffolds surround actin-independent integrin clusters, data part 4

Part 4 of data for the article Giant worm-shaped ESCRT-scaffolds surround actin-independent integrin clusters.

Giant worm-shaped ESCRT-scaffolds surround actin-independent integrin clusters, data part 3

Part 3 of data for the article Giant worm-shaped ESCRT-scaffolds surround actin-independent integrin clusters.

Giant worm-shaped ESCRT-scaffolds surround actin-independent integrin clusters, data part 5

Part 5 of data for the article Giant worm-shaped ESCRT-scaffolds surround actin-independent integrin clusters.

opencc-by-4.0Nov 2022View details →

"Influencing Pedestrians' Route Choice Using Route Shape Simplification "Supplementary Materials

"Influencing Pedestrians’ Route Choice Using Route Shape Simplification "Supplementary Materials

opencc-zeroFeb 2023View details →

Data for: Perennial biomass cropping and use: Shaping the policy ecosystem in European countries

Demand for sustainably produced biomass is expected to increase with the need to provide renewable commodities, improve resource security, and reduce greenhouse gas emissions in line with COP26 commitments. Studies have demonstrated additional environmental benefits of using perennial biomass crops (PBCs), when produced appropriately, as a feedstock for the growing bioeconomy, including utilisation for bioenergy (with or without carbon capture and storage). PBCs can potentially contribute to Common Agricultural Policy (CAP) (2023–27) objectives provided they are carefully integrated into farming systems and landscapes. Despite significant R&D investment over decades in herbaceous and coppiced woody PBCs, deployment has largely stagnated due to social, economic and policy uncertainties. This paper identifies the challenges in creating policies that are acceptable to all actors. Development will need to be informed by measurement, reporting and verification (MRV) of greenhouse gas emissions reductions and other environmental, economic and social metrics. It discusses interlinked issues that must be considered in the expansion of PBC production: i) available land; ii) yield potential; iii) integration into farming systems; iv) research and development (R&D) requirements; v) utilisation options; and vi) market systems and the socioeconomic environment. It makes policy recommendations that would enable greater PBC deployment: 1) incentivise farmers and land managers through specific policy measures, including carbon pricing, to allocate their less productive and less profitable land for uses which deliver demonstrable greenhouse gas reductions; 2) enable GHG mitigation markets to develop and offer secure contracts for commercial developers of verifiable low carbon bioenergy and bio-products; 3) support innovation in biomass utilisation value chains; and 4) continue long-term, strategic R&D and education for positive environmental, economic and social sustainability impacts.

opencc-by-4.0Mar 2023View details →

Source Data for "Shape control in 2D molecular nanosheets by tuning anisotropic intermolecular interactions and assembly kinetics"

This folder contains source data for figures of the paper "Shape control in 2D molecular nanosheets by tuning intermolecular interactions and assembly kinetics" (https://doi.org/10.1038/s41467-023-37203-7).

Social structure and individuals' behavior shaped by group composition in long-term tracking of rats

Trajectory data and behavioral metrics from long-term tracking of groups of rats.  Experiments performed in 2012 at Eötvös Loránd University, Budapest, Hungary. The data here is used in the following publication. See the publication for information on experimental structure and notation. Nagy, Máté, Jacob Davidson, Gábor Vásárhelyi, Dániel Ábel, Enikő Kubinyi, Ahmed El Hady, and Tamás Vicsek. 2023. “Social Structure and Individuals’ Behavior Shaped by Group Composition in Long-Term Tracking of Rats.” bioRxiv. <a href="https://doi.org/10.1101/2023.03.18.533183">https://doi.org/10.1101/2023.03.18.533183</a>. Github repository to reproduce results in the paper: <a href="https://github.com/jacobdavidson/ratsocialgroups">github.com/jacobdavidson/ratsocialgroups</a> Github repositories used for tracking and computation of behavioral metrics: <a href="https://github.com/vasarhelyi">github.com/vasarhelyi</a> The behavioral metrics, approach-avoidance networks, individual labels and group membership information, and behavioral assay data needed to reproduce the results in the publication are included in the zip  file <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/metrics-networks-labels.zip">metrics-networks-labels.zip</a>.  This includes the following files in .csv format: Naming/labels: <ul> <li>uidlabels.csv: References the ranked-mass labels used in the paper to the 3-color-code labels that identify each rat from the tracking.  "Group" is the membership during the original groups (Period 1), and "GroupG" is membership during the remixed groups (Period 3). <table> <tbody> <tr> <td> </td> <td> masslabel </td> <td> GroupMassRank-Exp7 </td> <td> Group </td> <td> GroupG </td> </tr> <tr> <td> GOB </td> <td> a5 </td> <td> 5.0 </td> <td> A1 </td> <td> G3 </td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> </li> <li>groupnames.csv:  Group membership information for each rat, during each experimental period <table> <tbody> <tr> <td> Experiments </td> <td> GOB </td> <td> OBG </td> <td> ... </td> </tr> <tr> <td> 1-7 </td> <td> A1 </td> <td> A1 </td> <td> ... </td> </tr> <tr> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> </tr> </tbody> </table> </li> </ul> Behavioral metrics and approach-avoidance networks: <ul> <li>metrics.csv: Behavioral metrics for each rat, calculated for each experiment. <table> <tbody> <tr> <td> Experiment </td> <td> metric </td> <td> GOB </td> <td> OBG </td> <td> ... </td> </tr> <tr> <td> 1 </td> <td> AA </td> <td> 0 </td> <td> 0.690964 </td> <td> ... </td> </tr> <tr> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> </tr> </tbody> </table> </li> <li>Networks-Period1+Period3.csv: Approach-avoidance interaction networks for the groups of 7 rats in Period 1 and Period 3.   <table> <tbody> <tr> <td> Experiment </td> <td> DayNight </td> <td> Group </td> <td> index </td> <td> winner </td> <td> loser0 </td> <td> loser1 </td> <td> loser2 </td> <td> loser3 </td> <td> loser4 </td> <td> loser5 </td> <td> loser6 </td> </tr> <tr> <td> 1 </td> <td> Day </td> <td> A1 </td> <td> 0 </td> <td> GOB </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> </tr> </tbody> </table> </li> <li>Networks-Experiment8.csv and Networks-Experiment9.csv: Approach-avoidance interaction networks for the larger combined groups in the experiments during Period 2. </li> </ul>   Behavioral assays results: <ul> <li>IndividualTests.csv:  Individual behavioral assay test results for the black and white box, elevated plus-maze, and canopy tests. <table> <tbody> <tr> <td> ID </td> <td> BWB-All Frames </td> <td> BWB-Black </td> <td> ... </td> </tr> <tr> <td> BGP </td> <td> 7500 </td> <td> 6860 </td> <td> ... </td> </tr> <tr> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> </tr> </tbody> </table> </li> <li>PairedTests.csv:  Pairwise social interaction behavioral assay test results for tests with unfamiliar and familiar rats <table> <tbody> <tr> <td> rat_ID </td> <td> rat_group_N </td> <td> freq_standup_unfam1 </td> <td> ... </td> </tr> <tr> <td> BGP </td> <td> 2 </td> <td> 28 </td> <td> ... </td> </tr> <tr> <td> ... </td> <td> ... </td> <td> ... </td> <td> ... </td> </tr> </tbody> </table> </li> </ul>   Trajectory data is included as multiple text files for each experiment in TSV format.  #t is frame number, Color_code_name identifies each rat, Real_recognition(1)_or_virtual(0) = 1 is a detection of the rat's color codes while 0 is an interpolated value, x,y, and Orientation are the position and orientation of the rat. <table> <tbody> <tr> <td>#t</td> <td>Color_code_name</td> <td>Real_recognition(1)_or_virtual(0) </td> <td>x</td> <td>y</td> <td>Orientation</td> </tr> <tr> <td>0</td> <td>ORG</td> <td>0</td> <td>872.4000</td> <td>778.7000</td> <td>-79.5000</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table>   Time-lapse video of the entire experiment:  <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/ratlab_timelapse_entire_exp.avi?versionId=a58df78d-1291-4301-9763-86a97b9e146c">ratlab_timelapse_entire_exp.avi</a>

opennotspecifiedMar 2023View details →

FIGURE 2. F. Bean-shaped copulatory chamber occupying segment 18. G in Three new Glossoscolex (Annelida: Crassiclitellata: Glossoscolecidae) in the truncatus group from the Brazilian Atlantic Forest

FIGURE 2. F. Bean-shaped copulatory chamber occupying segment 18. G. Cocoons of different developmental stages, with long strand on one side and a smaller strand on the other (cocoon on right). H. Recently-hatched unpigmented juvenile (single individual). I. Adult individuals are generally over 20 cm long in the field (in hand of author A. Feijoo).

opennotspecifiedDec 2020View details →

Figure 3. a, linear discriminant function illustrating shape variation between iguanids. Kernel density ellipses for each species illustrate 90 in Morphological and performance modifications in the world's only marine lizard, the Galápagos marine iguana, Amblyrhynchus cristatus

Figure 3. a, linear discriminant function illustrating shape variation between iguanids. Kernel density ellipses for each species illustrate 90% and 70% of the data distribution. b, graph of morphometric trait loadings from LD analysis.

opennotspecifiedOct 2017View details →

Figure 3 in Phylogeography of Crocidura suaveolens (Mammalia: Soricidae) in Iberia has been shaped by competitive exclusion by C. russula

Figure 3. Median-joining network of cytochrome b clade IV haplotypes of Crocidura suaveolens, coloured according to lineage (A1, red; A2, yellow; B, orange; C1, light green; C2, dark green; C3, light blue; C4, dark blue). The diameter of the circles represents the number of sampled individuals with that haplotype. Black dots indicate unsampled intermediary haplotypes.

opennotspecifiedOct 2017View details →

Figure 1 in Phylogeography of Crocidura suaveolens (Mammalia: Soricidae) in Iberia has been shaped by competitive exclusion by C. russula

Figure 1. Distribution of clade IV of the Crocidura suaveolens group in western Europe (striped area), showing collection localities (1–18) and localities of downloaded sequences (19–25) (in colour). Localities in close proximity to each other are represented as a single location; see Supporting Information, Tables S1 and S3 for more details. The colours represent the different lineages/sublineages identified in the phylogenetic analyses, which are also separated by thick black lines/dashed lines on the map. Altitude is shown with a greyscale, lower areas with lighter tones and higher areas with darker tones. The distribution of C. russula is also shown with different striped fills. Note that the distribution range of C. suaveolens in western Europe is fragmented only where both species are sympatric.