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Supplementary datasets, data analysis code, and R tutorials for: Phylogenetic analysis of adaptation in comparative physiology and biomechanics: overview and a case study of thermal physiology in treefrogs
<p>Comparative phylogenetic studies of adaptation are uncommon in biomechanics and physiology. Such studies require collecting data from many species, a challenge when data collection is experimentally intensive. Moreover, researchers struggle to employ the most biologically appropriate phylogenetic tools for identifying adaptive evolution. Here, we detail an established but greatly underutilized phylogenetic comparative framework—the Ornstein-Uhlenbeck process—that explicitly models long-term adaptation. We discuss challenges in implementing and interpreting the model, and we outline potential solutions. We demonstrate use of the model through studying the evolution of thermal physiology in treefrogs. Frogs of the family Hylidae have twice colonized the temperate zone from the tropics, and such colonization likely involved a fundamental change in physiology due to colder and more seasonal temperatures. However, which traits changed to allow colonization is unclear. We measured cold-temperature tolerance and characterized thermal performance curves in jumping for twelve species of treefrogs distributed from the Neotropics to temperate North America. We then conducted phylogenetic comparative analyses to examine how tolerances and performance curves evolved and to test whether that evolution was adaptive. We found that tolerance to low temperatures increased with the transition to the temperate zone. In contrast, jumping well at colder temperatures was unrelated to biogeography and thus did not adapt during dispersal. Overall, our paper shows how comparative phylogenetic methods can be leveraged in biomechanics and physiology to test the evolutionary drivers of variation among species.</p>
SCALE-AMPS data: Simulations of SHEBA case
<p>This contains all the 2D idealized simulation results of SHEBA mixed-phase clouds by SCALE-AMPS, which are mainly used for validating the model.</p>
DATA SET OF EXISTING AND ONGOING CASE STUDIES IN THE ARTS 2008-2020
<p>PURPOSE: D2.1, Data Set of Case Studies in the Arts 2008-2020 fulfils the following purposes:<br> 1. Build a research database for past and ongoing good practices in the field of arts-based social interventions in AMASS partner countries.<br> 2. Construct a solid foundation for the development of new interventions with similar objectives.<br> 3. To avoid obstacles that made many previous efforts in this field unsustainable and unadaptable.<br> 4. To offer a valid and authentic knowledge repository for policy makers, developers of future projects and researchers to identify motivations, philosophies, modes of engagement and impact of arts-based social interventions.</p>
Input data for use cases of TransportTools
<ul> <li>Use case I: Disclosing rare transient tunnels and their usage by water molecules in 15 simulations of DhaA dehalogenase <ul> <li>tunnel data</li> <li>water traces data</li> <li>configuration file for TransportTools</li> </ul> </li> <li> <p>Use case II: Understanding the effect of mutations by contrasting simulations of three different variants of LinB dehalogenase</p> <ul> <li>tunnel data</li> <li>water traces data</li> <li>molecular dynamics simulations with relevant water molecules only</li> <li>configuration file for TransportTools</li> </ul> </li> <li> <p>Use Case III: Inferring selectivity of transport pathways in LinB86 dehalogenase for a substrate molecule from almost 600 simulations</p> <ul> <li>tunnel data</li> <li>substrate traces data</li> <li>configuration file for TransportTools</li> </ul> </li> </ul>
In-depth analysis of 18 case studies on state-of-the-art of cultural tourism interventions
<p>The aim of Work Package 3 of the SmartCulTour project (<a href="http://www.smartcultour.eu">www.smartcultour.eu</a>) is to provide a state-of-the-art overview of cultural tourism interventions implemented in European cities and regions, thereby identifying best practices and the impacts and success conditions of cultural tourism interventions. Based on the previous, desk-researched 107 interesting cultural tourism interventions see "Desk research of 107 case studies on state-of-the-art of cultural tourism interventions", DOI: 10.5281/zenodo.5213017 ), a further 18 interventions were selected for in-depth analysis.</p> <p>These 18 cultural tourism interventions were analyzed via a series of expert interviews, combined with document and literature analysis, performed in the period January 2021-March 2021. Description of the case studies follows a standardized <strong>case study data collection form</strong>. This form is included here as "Case study data collection form". The form collect data on:</p> <ul> <li>Context and background information;</li> <li>The 'reason why' of the intervention;</li> <li>The intervention;</li> <li>Resources and tools necessary to design and implement the interventions;</li> <li>Impacts (expected, perceived and measured);</li> <li>(Perceived) success conditions and limiting factors.</li> </ul> <p>The semi-structured interviews left some room for adaptability depending on the case studies. A <strong>general guide on potential interview questions</strong> is included here as "Potential questions list for case study interviews".</p> <p>All case studies are included as individual odt-files, starting with "detailed case study + name of the intervention".</p>
Double-stranded RNA structural elements holding the key to translational regulation in cancer: the case of editing in RNA Binding Motif Protein 8A
<p>Raw data supporting the manuscript</p> <p>Abukar, A.;Wipplinger, M.;<br> Hariharan, A.; Sun, S.; Ronner, M.;<br> Sculco, M.; Okonska, A.;<br> Kresoja-Rakic, J.; Rehrauer, H.; Qi, W.;<br> et al. Double-Stranded RNA<br> Structural Elements Holding the Key<br> to Translational Regulation in Cancer:<br> The Case of Editing in RNA-Binding<br> Motif Protein 8A. Cells 2021, 10, 3543.<br> https://doi.org/10.3390/<br> cells10123543</p>
Data from the Groningen case
<p>These data include the survey data on the Noorderplantsoen 25 year later, and the transcriptions of the interviews with a number of stakeholders.</p>
Classification of blood cells dynamics with convolutional and recurrent neural networks: a sickle cell disease case study
<p>The fraction of red blood cells (RBC) adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease (SCD). Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples(out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. These videos are of different durations (from 6 to more than 100 frames).</p> <p>This dataset contains four adult patients with SCD. They were enrolled in the study Drepaforme (approved by the institutional review board CPP Ouest 6 under the reference n°2018A00679-46) and were sampled weekly for several months. The movies were processed using in-house routines in Matlab (Matlab, R2016a) and RBC were detected individually and tracked over time. The database provided in this repository are already pre-processed sequences of tracked and centered RBC over time, each time step image being normalized to 31x31 pixels. Within the 32 experiments, the total number of sequences (or samples) is nearly 150 000. All sequences were semi-automatically labelled into 3 classes, depending on the dynamic of the cell: tank-treading, flipping and unreliable (140 000 are unreliable). The percentage of tank-treading cells with respect to all reliable cells (tank-treading+flipping) in every experiment is the final goal of this study.</p> <p>This dataset is very interesting to the community as it is a large database for cell dynamics classification: the class depends on the movement of the cell.</p> <p>An automatic processing of the database using a 2-stage deep learning model is available here https://github.com/icannos/redbloodcells_disease_classification</p> <p>For opening the data in python:</p> <p> from scipy.io import loadmat<br> x=loadmat('BG20191003shear10s01_Export.mat')</p> <p> * x['Norm_Tab'] is of size nb_samples x max_len_sequences x 31 x 31, where max_len_sequences is the length of the longest sequence of the series, typically ~150 to 180. The other sequences are padded with 31x31 zero matrices at the end in order to fill this maximal length.</p> <p> * x['Labels_Num'] is the corresponding label of each sequence, of size nb_samples. Label can be:<br> - 0 : "tank-treading" (or healthy)<br> - 1 : "flipping" (or tumbling, i.e. related to a SCD)<br> - 2 : "unreliable"</p>
Summary statistics for association tests between human and Plasmodium falciparum genetic variants in 3,346 severe malaria cases from The Gambia and Kenya
<p>This dataset contains summary statistics for association tests between human and<br> <em>Plasmodium falciparum</em> malaria parasite genetic variants, using data from 3,346 severe malaria cases from The Gambia and Kenya. These results underlie the analysis described in our paper:</p> <p><strong>"Malaria protection due to sickle haemoglobin depends on parasite genotype"</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia M. Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>:: <a href="https://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a><br> <br> The genotype data underlying these summary statistics has also been deposited on Zenodo<br> (<a href="https://zenodo.org/record/4973477">doi:10.5281/zenodo.4973477</a>). The <a href="https://www.well.ox.ac.uk/~gav/hptest)">HPTEST software</a> used to generate these results has also been deposited (<a href="https://doi.org/10.5281/zenodo.5685580">doi:10.5281/zenodo.5685580</a>). Please see the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a> for a full list of datasets which have been released with this manuscript.</p> <p><strong>Data contents.</strong></p> <p>The dataset consists of a single <a href="http://sqlite.org">sqlite database file</a> containing the results, and an accompanying README file in markdown and html format. Please see the README file for full details of the data contents.</p> <p> </p>
Fig. 1. A in The first case of gynandromorphism in Enolmis (Lepidoptera: Gelechioidea: Scythrididae)
Fig. 1. A – Enolmis sp., gynandromorph abdomen; B – Enolmis delicatella species-group, female genitalia, with the various parts marked by acronyms: anterior apophyses (AA), caudal processes (Pr), henia (He), hypostema (Hy), papillae anales (AP), and posterior apophyses (PA). C–D – Enolmis sierraenevadae Passerin d'Entrèves, 1997: C – male genitalia, with the various parts marked by the acronyms: uncus (Un), gnathos (Gn), tegumen (Te), phallus (Ph), valvae (Va), vinculum (Vi), D – male VIII segment. Scale bar = 0.2 mm.
Pareto front of the Bio-SOFC plant (Case 5) optimization
<p>Datasets of the article "Techno-Economic Optimization of an Integrated Biomass Waste Gasifier–Solid Oxide Fuel Cell Plant".</p> <p>With a growing energy demand in a carbon-constrained society, fuels cells powered by renewable fuels, and specifically solid waste, are seen as interesting contributors to the energy portfolio. The alternative energy industry needs to reduce costs, enhance efficiency, and demonstrate durability and reliability to be economically feasible and attractive. This paper addresses biomass waste gasification in distributed energy systems, using a solid oxide fuel cell (SOFC) to produce electricity and heat. The potential and optimal plant efficiency and layout (i.e., anode off-gas (AOG) recirculation point <em>via</em> small-scale turbomachinery and heat exchanger network) are analyzed through a multi-stage approach that includes scenario evaluation and multi-objective optimization <em>via</em> a hybrid optimization strategy with heuristics and mathematical programming. The results in this paper summarize the most convenient operating conditions and provide an optimized heat exchanger network (HEN). The AOG recirculation toward the gasifier combustor is the preferred option; the electrical and thermal efficiencies can separately go up to 49 and 47%, respectively. The combined total efficiency ranges between 76 and 82%, and the area of heat exchange, which corresponds to an amount of heat exchanged between 91 and 117 kW, is within 6–14 m<sup>2</sup>.</p>
Data for "Do electric vehicles mitigate urban heat? The case of a tropical city"
<p>This dataset contains the underlying data used in the publication "Do electric vehicles mitigate urban heat? The case of a tropical city", which is under review in <em>Front. Environ. Sci. .</em></p> <p>The dataset includes two folders:</p> <p>1. <strong>data</strong> <br> Include COSMO-DCEP-BEP model inputs and output needed to reproduce the results in the manuscript (NetCDF). </p> <p>2. <strong>script</strong><br> Include post-processing scripts used to generate the figures in the manuscript (Jupiter Python 3 Notebook).</p> <p><em> </em></p>
FIG. 11. — A-C in Bryozoan faunas at the Tortonian-Messinian transition. A palaeoenvironmental case study from Crete Island, eastern Mediterranean
FIG. 11. — A-C, Bryocryptella torquata (Jullien, 1903); A, colony fragment, frontal view, POTB13: AMPG(IV) 2815a; B, colony fragment, dorsal view, POTB13: AMPG(IV) 2815b; C, detail view of zooids, FAN31: AMPG(IV) 3500a; D, Tessaradoma boreale (Busk, 1860), colony fragment, FAN28: AMPG(IV) 3613. E-F, Kionidella excelsa Koschinsky, 1885; E, colony fragment, basal view, FAN7: AMPG(IV)3570a; F, frontal view of a few zooids, FAN16: AMPG(IV) 3572. Scale bars: A, 500 µm; B, E, 200 µm; C, F, 100 µm; D, 1 mm.
FIG. 9. — A, B in Bryozoan faunas at the Tortonian-Messinian transition. A palaeoenvironmental case study from Crete Island, eastern Mediterranean
FIG. 9. — A, B, Cupuladria cf. canariensis (Busk, 1859); A, frontal view of a whole colony, KER39: AMPG(IV) 3078a; B, dorsal view of a whole colony, KER39: AMPG(IV) 3078b; C, D, Discoporella reussiana (Manzoni, 1869); C, frontal view of a whole colony, KER30: AMPG(IV) 3102; D, dorsal view of a whole colony, KER30: AMPG(IV) 3101; E, Nellia tenella (Lamarck, 1816), detail of an internode showing two zooids in frontal view, KER17: AMPG(IV) 3150a; F-H, Canda rectangulata Udin, 1964; F, dorsal view of an internode fragment, FAN35: AMPG(IV) 3506a; G, frontal view of an internode fragment, FAN35: AMPG(IV) 3506b; H, Detail of the same fragment, FAN35: AMPG(IV) 3506b. Scale bars: A-D, 1 mm; E, H, 100 µm; F-G, 200 µm.
FIG. 8. — A-C in Bryozoan faunas at the Tortonian-Messinian transition. A palaeoenvironmental case study from Crete Island, eastern Mediterranean
FIG. 8. — A-C, Crisia aculeata Hassall, 1841; A, internode, frontal view, POTB13: AMPG(IV) 2828a; B, internode, dorsal view, POTB13 2828b: AMPG(IV); C, internode with gonozooid, KAP22: AMPG(IV) 3339. D-F, Crisia denticulata (Lamarck, 1816); D, internode, frontal view, POTB13: AMPG(IV) 2829a; E, internode dorsal view, POTB13: AMPG(IV) 2829b; F, gonozooid, POTB13: AMPG(IV) 2829c. Scale bars: A, 500 µm; B-F, 200 µm.
FIG. 6 in Bryozoan faunas at the Tortonian-Messinian transition. A palaeoenvironmental case study from Crete Island, eastern Mediterranean
FIG. 6. — Theoretical zonation in a transect from coastal to upper bathyal palaeoenvironments following Pérès & Picard (1964). This bionomic depth zonation illustrates the situation on flat muddy bottoms in areas with very clear waters, like in the oligotrophic (eastern) Mediterranean Sea. The distribution of bryozoan growth-form assemblages (in average number of fragments per sample) and the average number of species in each assemblage is indicated in a grey circle (partly inspired from Moissette 2000).
FIG. 1. — A in Bryozoan faunas at the Tortonian-Messinian transition. A palaeoenvironmental case study from Crete Island, eastern Mediterranean
FIG. 1. — A, Situation map of Crete within the eastern Mediterranean; B, geological sketch map of the island of Crete (after Krijgsman et al. 1994), with location of the studied sections.
FIG. 7. — A, B in Bryozoan faunas at the Tortonian-Messinian transition. A palaeoenvironmental case study from Crete Island, eastern Mediterranean
FIG. 7. — A, B, Anguisia verrucosa Jullien, 1882; A, bifurcating branch, FAN36: AMPG(IV) 3471; B, encrusting base (left) and erect peristome of the encrusting proximal zooid of a running branch (right), FAN35: AMPG(IV) 3470; C, D, Exidmonea atlantica (Forbes in Johnston, 1847); C, fragment of a branch, frontal view (with a gonozooid on the left upper part), POTB13: AMPG(IV) 2853a; D, fragment of a branch, dorsal view, POTB13: AMPG(IV) 2853b; E-G, Ybselosoecia typica (Manzoni, 1878); E, fragment of a branch (with a large gonozooid), frontal view, POTB13: AMPG(IV) 2881a; F, detail of the ooeciostome of another gonozooid, POTB13: AMPG(IV) 2881b; G, fragment of a branch, dorsal view, POTB13: AMPG(IV) 2881c. Scale bars: A, D, 200 µm; B, G, 100 µm; C-E, 500 µm.
FIG. 2 in Bryozoan faunas at the Tortonian-Messinian transition. A palaeoenvironmental case study from Crete Island, eastern Mediterranean
FIG. 2. — Schematic sedimentary log of Potamida composite section with sample location and semi-quantitative abundances of bryozoan species.
FIG. 10. — A-C in Bryozoan faunas at the Tortonian-Messinian transition. A palaeoenvironmental case study from Crete Island, eastern Mediterranean
FIG. 10. — A-C, Scrupocellaria cf. elliptica (Reuss, 1847); A, view of some zooids showing opesia with 4 spine bases on the outer distal part and 3 spine bases + 1 scutal spine on the inner distal rim, FAN36: AMPG(IV) 3587; B, zooids with 7 spine bases and the scutum, FAN35: AMPG(IV) 3586a; C, dorsal surface of a colony fragment with small vibracularia and radicular pores, FAN35: AMPG(IV) 3586b; D, E, Scrupocellaria scrupea Busk, 1852; D, ovicellate colony fragment, FAN35: AMPG(IV) 3590a; E, detail frontal view showing opesia with five spine bases, FAN35: AMPG(IV) 3590b; F-H, Cellaria salicornioides Lamouroux, 1816; F, part of a slender internode, FAN18: AMPG(IV) 3513a; G, detail view showing denticles and endotoichal ovicells, POTB13: AMPG(IV) 2821a; H, avicularia in frontal and lateral view (arrow), POTB13: AMPG(IV) 2821b; I, Gemellipora eburnea Smitt, 1873, broken internode showing two zooids with two oval scars separated by a thin interzooidal groove, CAP032: AMPG(IV) 3426. Scale bars: A-E, G, I, 100 µm; F, H, 200 µm.
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.