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8,998 results for “Adaptation”

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zenodo44/100

Dataset for: Infectious disease responses to human climate change adaptations

Original and derived data products referenced in the original manuscript are provided in the data package. <h3>Description of the data and file structure</h3> Original data: <code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript. <ol> <li>ID: The paper identification number</li> <li>Topic: The broad topic (i.e., each row of Table 1)</li> <li>Authors: The names of the authors of the paper</li> <li>Article Title: The title of the paper</li> <li>Source Title: The name of the journal in which the paper was published</li> <li>Abstract: The paper's abstract, retrieved from the Web of Science search</li> <li>study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li>pathogen_broad: Broad classification of the type of pathogen discussed in the paper.</li> <li>transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li>pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li>country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> Derived data: <code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript. <ol> <li>County Name: The name of the county in Kenya</li> <li>Sheep and goats 1980: The estimated number of sheep and goats in 1980</li> <li>Sheep and goats 2016: The estimated number of sheep and goats in 2016</li> <li>pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016</li> <li>Cattle 1980: The estimated number of cattle in 1980</li> <li>Cattle 2016: The estimated number of cattle in 2016</li> <li>pct_change_cattle: The percent change in cattle numbers from 1980 to 2016</li> <li>Camel 1980: The estimated number of camels in 1980</li> <li>Camel 2016: The estimated number of camels in 2016</li> <li>pct_change_camel: The percent change in camel numbers from 1980 to 2016</li> <li>human_pop 1980: The estimated human population in the county in 1980</li> <li>human_pop 2016: The estimated human population in the county in 1980</li> <li>pct_change_human: The percent change in the human population from 1980 to 2016</li> <li>area_sq_km: The land area of the county</li> <li>change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016</li> <li>change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016</li> <li>change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016</li> </ol> <code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript. <ul> <li>Country: The country in which the schistosome prevalence studies were performed.</li> <li>Latitude: The latitute in decimal degrees</li> <li>Longitude: The longitute in decimal degrees</li> <li>Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript. <ul> <li>Precipitation (mm): Binned annual precipitation values</li> <li>1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period</li> <li>1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period</li> <li>1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> Data were derived from the following sources: <ul> <li> Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249 </li> <li> London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0 </li> <li> World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections </li> </ul>

opencc-by-4.0Aug 2024View details →

zenodo44/100

Excel data collection template on descriptive political representation in national parliaments of the projects Pathways to Power and InclusiveParl adapted for the ActEU project

This file contains the empty data collection template and variable and value labels to code biographical data on legislators for WP4 in the ActEU project. It is an abbreviated version of the codebooks produced by the Pathways to Power project and by the InclusiveParl project.

opencc-by-nc-4.0Sep 2024View details →

zenodo44/100

Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability: Subjective fidelity study data

Supplementary Material to the Paper: Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability This folder contains all collected data and scripts that were used to analyze the subjective fidelity study.

opencc-by-4.0Oct 2024View details →

zenodo44/100

Geometry and tool motion planning for curvature adapted CNC machining

Examples of 5-axis CNC machining tool paths and corresponding g-codes for a concave, convex and a freeform surface milling with a toroidal cutter (supported information for the paper "Geometry and tool motion planning for curvature adapted CNC machining", DOI 10.1145/3450626.3459837). In the 'path.txt' files, each line contains three numbers that are Euclidean coordinates of the contact points; in the 'positions.txt' files, each line contains six numbers: the first three being the coordinates of the centers of the torus and the other three being the coordinates of the unit axis vector of the tool, pointing outside the surface.

opencc-by-4.0Oct 2021View details →

zenodo44/100

Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"

LM is the linear inversion seafloor topography model NLM is the nonlinear inversion seafloor topography model PM is the prior seafloor topography model

opencc-by-4.0Nov 2022View details →

zenodo44/100

Evolution of left-right asymmetry in the sensory system and foraging behavior during adaptation to food-sparse cave environments

Laterality in relation to behavior and sensory systems is found commonly in a variety of animal taxa. Despite the advantages conferred by laterality (e.g., the startle response and complex motor activities), little is known about the evolution of laterality and its plasticity in response to ecological demands. In the present study, a comparative study model, the Mexican tetra (Astyanax mexicanus), composed of two morphotypes, i.e., riverine surface fish and cave-dwelling cavefish, was used to address the relationship between environment and laterality. The use of a machine learning-based fish posture detection system and sensory ablation revealed that the left cranial lateral line significantly supports one type of foraging behavior, i.e., vibration attraction behavior, in one cave population. Additionally, left-right asymmetric approaches toward a vibrating rod became symmetrical after fasting in one cave population but not in the other populations. Based on these findings, we propose a model explaining how the observed sensory laterality and behavioral shift could help adaptation in terms of the tradeoff in energy gain and loss during foraging according to differences in food availability among caves. This repository contains all of raw videos used in this study. Please let us know if you have any question on these videos

opencc-by-4.0Dec 2022View details →

zenodo44/100

Alignments from "Caecilian genomes reveal molecular basis of adaptation and convergent evolution of limblessness in vertebrates"

Compressed file containing the alignments at both nucleotide and amino acid level for the manuscript "Caecilian genomes reveal molecular basis of adaptation and convergent evolution of limblessness in vertebrates"

opencc-by-4.0Dec 2021View details →

zenodo44/100

DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

We present the data used in "DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". It was also used in the conference paper presented in Machine Learning and the Physical Sciences workshop at NeurIPS 2022: "Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". A plethora of AI methods, has already shown huge promise in increasing quality and speed of work with astronomical datasets, but high complexity of AI methods leads to extraction of dataset-specific non-robust features, which leads to models that cannot work on multiple datasets at the same time. We develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain adaptation, that can be applied to datasets with different data distributions and class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. We apply our model to three examples of galaxy morphology classification tasks of different complexities (3-class and 10-class problems), with anomaly detection i.e. in all our experiments we have one extra class in the unlabeled target dataset, which represents our anomaly class.   DATA: 1) DA across two different data releases of the same survey (LSST 1 and 10 years of observation): We use data from Ciprijanovic et al. 2022. which can also be found on Zenodoo: <a href="https://zenodo.org/record/5514180#.Y6SM7y-B2_w">https://zenodo.org/record/5514180</a> . Data contains three classes: spiral (0), elliptical (1) and merging galaxies (3, anomaly class). 2) DA across two surveys (SDSS and DeCALS): We create datasets using data and labels from the Galaxy Zoo project. Datasets contain 10 classes (9 known classes present in both SDSS and DeCALS data, and one unknown anomaly class present only in DeCALS data): disturbed (0), merging (1), round smooth (2), cigar shaped smooth (3), barred spiral (4), unbarred tight spiral (5), unbarred loose spiral (6), edge-on without bulge (7), edge-on with bulge (8), lenses (9, unknown anomaly class). SDSS (wide filed): datasets is split into two files  - sdss_1.h5, sdss_2.h5 DeCALS:  decals.zip 3) DA between wide and deep observing fields of the same survey (SDSS): We create datasets using data and labels from the Galaxy Zoo project. Datasets contain same 10 classes as in 2), with the final lens anomaly class being only present in the SDSS deep field. SDSS (wide filed): the same data as in 2) SDSS (Strip 82 deep field): sdss_stripe82.zip All SDSS and DECaLS files contain full datasets (train, validation and test). Exact split that we performed (0.6 : 0.2 : 0.2) can be done using the code that accompanies this publication: <a href="https://github.com/deepskies/DeepAstroUDA">https://github.com/deepskies/DeepAstroUDA</a> .

opencc-by-4.0Feb 2023View details →

zenodo44/100

raw data of Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice

Microbiome dataset for "Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice" publication https://doi.org/10.1016/j.jcmgh.2023.02.013

opencc-by-4.0Mar 2023View details →

zenodo44/100

Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals

Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals Sophie Teulleta,#, Marie-Ka Tilaka, Amandine Magdeleinea, Roxane Schaubb,c, Nora M. Weyerd, Wendy Panainod,e, Andrea Fullerd, William. J. Loughryf, Nico L. Avenantg, Benoit de Thoisyh,i, Guillaume Borrelj and Frédéric Delsuca,# aInstitut des Sciences de l’Evolution de Montpellier (ISEM), Univ Montpellier, CNRS, IRD, Montpellier, France bCIC AG/Inserm 1424, Centre Hospitalier de Cayenne Andrée Rosemon, Cayenne, French Guiana cTropical Biome and immunopathology, Université de Guyane, Labex CEBA, DFR Santé, Cayenne, French Guiana dBrain Function Research Group, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa eCentre for African Ecology, School of Animals, Plant, and Environmental Sciences, University of the Witwatersrand, Johannesburg, South Africa fDepartment of Biology, Valdosta State University, Valdosta, GA, USA gNational Museum and Centre for Environmental Management, University of the Free State, Bloemfontein, South Africa hInstitut Pasteur de la Guyane, Cayenne, French Guiana, France iKwata NGO, Cayenne, French Guiana, France jInstitut Pasteur, Université Paris Cité, UMR CNRS 6047, Evolutionary Biology of the Microbial Cell, Paris, France #Corresponding authors: sophie.teullet@umontpellier.fr; frederic.delsuc@umontpellier.fr   Abstract In mammals, myrmecophagy (ant and termite consumption) represents a striking example of dietary convergence. This trait evolved independently at least five times in placentals with myrmecophagous species comprising aardvarks, anteaters, some armadillos, pangolins, and aardwolves. The gut microbiome plays an important role in dietary adaptation, and previous analyses of 16S rRNA metabarcoding data have revealed convergence in the composition of the gut microbiota among some myrmecophagous species. However, the functions performed by these gut bacterial symbionts and their potential role in the digestion of prey chitinous exoskeletons remain open questions. Using long- and short-read sequencing of fecal samples, we generated 29 gut metagenomes from nine myrmecophagous and closely related insectivorous species sampled in French Guiana, South Africa, and the USA. From these, we reconstructed 314 high-quality bacterial genome bins of which 132 carried chitinase genes, highlighting their potential role in insect prey digestion. These chitinolytic bacteria belonged mainly to the family Lachnospiraceae, and some were likely convergently recruited in the different myrmecophagous species as they were detected in several host orders (i.e., Enterococcus faecalis, Blautia sp), suggesting that they could be directly involved in the adaptation to myrmecophagy. Others were found to be more host-specific, possibly reflecting phylogenetic constraints and environmental influences. Overall, our results highlight the potential role of the gut microbiome in chitin digestion in myrmecophagous mammals and provide the basis for future comparative studies performed at the mammalian scale to further unravel the mechanisms underlying the convergent adaptation to myrmecophagy.   Main figures and corresponding datasets Figure_1_dataset.zip contains: <ul> <li>FIGURE 1. Phylogenetic position of the 314 high-quality selected bins reconstructed from 29 gut metagenomes of the nine focal myrmecophagous species within a reference prokaryotic phylogeny. A: Phylogeny of the 314 selected bins (red branches) with 2496 prokaryote reference genomes. Circles respectively indicate (from inner to outer circles): the bacterial phyla and kingdom to which these genome bins were assigned based on the Genome Taxonomy Database release 7 (Parks et al, 2021). Clades, where a subtree was defined, are highlighted in blue for the Firmicutes (Fig. 1B), green for the Bacteroidetes, and pink for the Proteobacteria (Figs. S2 A and B, respectively). B: Subtree within Fimircutes showing myrmecophagous-specific clades (blue highlights; dark blue corresponds to the three clades mentioned in the results, light blue to the other clades). The outer circle indicates the bacterial family to which these genome bins were assigned based on the Genome Taxonomy Database. Bins’ names of the myrmecophagous-specific clades are indicated at leaves of the phylogenetic tree together with the genus to which they were assigned to.</li> <li>phylophlan_LR_SR_ToL_FINAL_concatenated.aln: Alignment of the concatenated markers assembled by PhyloPhlAn v3.0.58.</li> <li>phylophlan_LR_SR_ToL_FINAL.tre: Phylogenetic tree reconstructed by PhyloPhlAn v3.0.58 for the 314 high quality selected genome bins and the 2496 prokaryote reference genomes.</li> </ul> Figure_2_dataset.zip contains: <ul> <li>FIGURE 2. Phylogeny of the 394 GH18 sequences identified in 132 high-quality selected bins reconstructed from 29 gut metagenomes of the nine focal myrmecophagous species and relatives. Red branches indicate the 237 sequences having an active chitinolytic site (DXXDXDXE). Circles respectively indicate (from inner to outer circles): the bacterial family and phyla of the bin the sequence was retrieved from. Colored sequence names indicate the host species. Colored circles at certain nodes indicate enzymes to which sequences are similar when blasting them against the NCBI non-redundant protein database. Sequence names are indicated at leaves of the tree and begin with the genus to which the bin they were identified in was assigned to. </li> <li>GH18_sequences_from_selected_bins_alignment.fasta: Alignment of the 394 GH18 sequences identified in 132 high quality selected bins computed with MAFFT v7.450.</li> <li>GH18__sequences_from_selected_bins_tree.newick: Phylogenetic tree of the 394 GH18 sequences inferred with RAxML v8.2.11 within Geneious Prime 2022.0.2.</li> </ul> Figure_3_dataset.zip contains: <ul> <li>FIGURE 3. Detection of the 314 high-quality bacterial genomes (lines) in the 29 gut metagenomes (columns) of the nine focal species. Each square indicates the detection of a genome bin in a sample as estimated by anvi’o v7 (Eren et al, 2021). Names of bins are indicated on the left with red indicating chitinolytic bins (Table S2). The names begin with the genus to which the bin was assigned to. Asterisks (*) indicate bins detected in at least one soil sample (detection > 0.25) (Fig. S4, Table S2, and detection table available via Zenodo). Phylogenetic relationships of host species distinguished by different color strips are represented at the bottom of the graph. Columns on the right indicate (from left to right): the number of GH18 sequences identified in each bin (from 0 to 17), the bin’s taxonomic phylum, class, order, and family. The phylogeny of the 314 selected bins inferred with PhyloPhlAn v3.0.58 (Asnicar et al, 2020) is also represented on the right of the graph (see Fig. S1). Silhouettes were downloaded from phylopic.org.</li> <li>detection_bins_across_gut_metagenomes.txt: Detection table as tab-delimited file containing the detection values inferred by anvi'o v7 for the 314 high quality selected bins across the 29 gut metagenomes from the nine focal myrmecophagous species. </li> </ul> Figure_4_dataset.zip contains: <ul> <li>FIGURE 4. Distribution of chitinolytic selected bins (red links) among the nine focal myrmecophagous species and relatives. Phylogenies of the 314 high-quality selected bins (Fig. S1) and of the nine host species (downloaded from timetree.org) are represented respectively on the left and the right of the graph. Links illustrate, for each bin, in which host species the bin was detected (detection threshold > 0.25). Red links indicate bins in which at least one GH18 sequence with an active chitinolytic site (DXXDXDXE) was found (chitinolytic bins). The size of the circles at the tips of the host phylogeny is proportional to the number of samples (n = 1 for D. kap; n = 2 for D. nov, C. uni and M. tri; n = 3 for T. tet and O. afe; n = 4 for D. sp. nov FG; n = 6 for P. cri and S. tem). Bins’ names are indicated at the tip of the bins’ phylogeny and main bacterial phyla are indicated by colored vertical bars. This graph was done with the cophylo R package within the phytools suite (Revell, 2012). Silhouettes were downloaded from phylopic.org.</li> <li>presence_absence_MAGs_in_metagenomes.txt: Presence/absence matrix of the 314 selected genome bins across the 29 gut metagenomes.</li> <li>host_species_phylo_reduced_fig4.newick: Host phylogenetic timetree.</li> </ul> Table_1_sample_infos.xls: Detailed sample information for the 33 fecal samples collected. N.B.: Diet was determined based on field observations (i.e., dissections) and the literature.   Supplementary results Supplementary_results_Teullet_etal_2023.zip includes a comparison of genome statistics of the selected bins reconstructed from the long-read vs the short-read datasets, a phylogeny of the set of selected bins before dereplication (n = 407) and a comparison of the distribution of shared and specific genome bins carrying GH18 among host orders.   Supplementary material Supplementary_material_Teullet_etal_2023.zip contains <ul> <li>Supplementary figures (S1-S4) and tables (S1-S4).</li> <li>phylophlan_314_bins_phylogeny_FINAL_concatenated.aln and phylophlan_314_bins_phylogeny_FINAL.tre: Alignment of the concatenated markers and the final tree (respectively) reconstructed by PhyloPhlAn v3.0.58 for the 314 high-quality selected and dereplicated genome bins.</li> <li>phylophlan_407_selected_bins_nodRep_concatenated.aln and phylophlan_407_selected_bins_phylogeny_FINAL.tre: Alignment of the concatenated markers and the final tree (respectively) reconstructed by PhyloPhlAn v3.0.58 for the 407 high-quality selected genome bins before dereplication.</li> <li>abundance_bins_across_gut_metagenomes.txt: A tab-delimited file corresponding to the absolute abundance values inferred by anvi'o v7 for the 314 high-quality selected bins across the 29 gut metagenomes from the nine focal myrmecophagous species. </li> <li>detection_bins_across_soil_samples.txt: A tab-delimited file corresponding to the detection values inferred by anvi'o v7 for the 140 high-quality selected bins reconstructed from the aardvark, ground pangolin and southern aardwolf gut metagenomes across the eight soil samples collected on sample sites in South Africa.</li> </ul>   Assemblies Long-read_metagenomic_assemblies_polished.zip contains the 31 long-read metagenomes assembled with metaFlye strain v2.9 and polished with short reads using Pilon v1.4, which were used for binning. Long-read_metagenomic_assemblies_not_polished.zip contains the 33 long-read metagenomes assembled with metaFlye strain v2.9 before polishing. Short-read_metagenomic_assemblies.zip contains the 31 short-read metagenomes assembled with metaSPAdes and MEGAHIT. N.B: <ol> <li>Two samples (DASY M1746 and DASY VLD168) were not sequenced using Illumina short reads. Only long reads were generated and assembled for these two samples and are made available here. As these assemblies could not be polished, these samples were not included in downstream analyses.</li> <li>Two samples (CAB M3141 and MYR M5293) were highly contaminated by host reads and not used in downstream analyses. As they were still assembled with the other samples, the corresponding metagenomes are made available here.</li> </ol>   Binning: genome bins and dereplication results High-quality_selected_bins_dereplicated.zip contains the 314 high quality selected bins (>90% completion, <5% redundancy) reconstructed from long- and short-read metagenomes with metaBAT2 and dereplicated with dRep at 98% ANI. metaBAT2_short-read_assemblies_bins.zip contains all bins reconstructed from the short-read assemblies with metaBAT2 (i.e., output of metaBAT2). metaBAT2_long-read_assemblies_bins.zip contains all bins reconstructed from the long-read polished assemblies with metaBAT2 (i.e., output of metaBAT2). Output_dRep_98ANI_407_bins_long-short-reads.zip contains the output of the dereplication analysis done on the set of 407 high-quality selected genome bins reconstructed from long- (n = 201) and short-read (n = 206; labeled "spad") metagenomes. It was performed with dRep using default parameters. After this step, the final dataset included 314 high-quality non-redundant genome bins. This folder includes: <ul> <li>LR_SR_407_bins_dRep_98ANI_Primary_clustering_dendrogram.pdf: The primary clustering of selected genome bins using the Mash algorithm with an ANI threshold of 90%.</li> <li>LR_SR_407_bins_dRep_98ANI_Secondary_clustering_dendrograms.pdf: The secondary clustering of selected genome bins using the fastANI algorithm with an ANI threshold of 98%.</li> <li>LR_SR_407_bins_dRep_98ANI_Cluster_scoring.pdf: The clustering score attributed to each genome bin during dereplication. Asteriks (*) indicate genomes chosen to be the representative genomes of their cluster.</li> </ul> <ul> </ul>

opencc-by-4.0Jun 2023View details →

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Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures

Processed data associated with the manuscript DOI: <a href="https://doi.org/10.1016/j.xgen.2023.100421" target="_blank" rel="noopener">10.1016/j.xgen.2023.100421 </a> Analysis code on GitHub: <a href="../doi/10.5281/zenodo.8253917" target="_blank" rel="noopener">10.5281/zenodo.8253917</a>

opencc-by-4.0Oct 2023View details →

zenodo44/100

Challenges of cultural heritage adaptive reuse: a stakeholders-based comparative study in three European cities. Dataset

Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807. <ul> <li>Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019</li> <li>Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.</li> <li>Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.</li> <li>Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and  c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.</li> <li>Language of the data: English.</li> <li>References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602</li> </ul>

opencc-by-4.0Mar 2023View details →

zenodo44/100

Evaluation of an adapted semi-automated DNA extraction for human salivary shotgun metagenomics

This deposit contains : - a RMarkdown filte containing the codes for the mcirobial analysis of saliva samples - the html report with codes, results and figures - a RData containing microbial datasets (MSp species abundance table, genus, family and phylum abundance tables, matrix of genes correlations, taxonomy) - a RData containing associated metadata

opencc-by-4.0Aug 2023View details →

zenodo44/100

Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"

Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization". Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a>

opencc-by-4.0Oct 2023View details →

edi44/100

Relyea, R. A. 2001. The lasting effects of adaptive plasticity: Predator-induced tadpoles become long-legged frogs. Ecology 82:1947-1955.

Changes in environmental conditions often alter the traits of individuals; however, we have a poor understanding of how changes in phenotypically plastic traits early in development may affect traits later in life. Such effects are of particular interest in organisms with complex life cycles in which early and late life stages can have drastically different morphologies and occupy different habitats. In this study, I examined how differences in the mass, morphology, and larval period of wood frog tadpoles (Rana sylvatica) subsequently affected the mass and morphology of metamorphic frogs. I found three major patterns: (1) larval mass and larval period were positively related to metamorphic mass; (2) larval period was positively related to metamorph hindlimb and forelimb length and negatively related to metamorph body width; and (3) larval body length was positively related to metamorph forelimb size. I then used these correlations to interpret the connection between the traits of predator-induced tadpoles and the subsequent traits of metamorphic frogs. Tadpoles reared with caged predators (aeshnid dragonflies) developed relatively deeper tail fins and had shorter bodies, lower mass, and longer developmental times than tadpoles reared without predators. Metamorphs emerging from larval predator environments exhibited no differences in mass but developed relatively large hindlimbs and forelimbs and narrower bodies than metamorphs emerging from predator-free larval environments. These differences arose primarily due to predator-induced changes in larval development time and not due to the predator-induced changes in larval morphology. By focusing on a large number of traits and a wide range of trait values, one can readily generate predictions about how a variety of environments, which alter traits early in development, can subsequently alter traits later in development.

openCC (other)Jun 2024View details →

edi44/100

The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).

The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr

openOpenDec 2008View details →

edi44/100

Coastal SEES Collaborative Research: Coastal Sustainability: A cross-site comparison of salt marsh persistence in response to sea-level rise and feedbacks from social adaptations

Coastal ecosystems are often valued for decision-making purposes based on monetized market and non-market values of goods and services, and associated economic impacts. Examples include values of fishery landings, price changes for waterfront homes, and tourism revenues. Monetized quantities such as these do not provide a comprehensive characterization of the values provided by these ecosystems. Human reliance on the goods and services provided by ecosystems and the global decline in the health of many of these ecosystems suggests the need for ecosystem valuation to help inform decision-making and conservation policy. However, traditionally employed economic valuation methods are rarely able to capture the full scope of the benefits ecosystems provide, including benefits provided by "cultural" ecosystem services. Qualitative methods such as focus groups can provide insight on these values not available through quantitative methods alone. This research explores public perceptions of salt marsh value through the use of semi-structured focus groups in marsh-adjacent communities in Massachusetts, Virginia, and Georgia. The data include de-identified focus group transcripts from three 90-minute focus groups held in each state. Initial questions were drawn from the same semi-structured question list in each focus group, with exploratory follow-up questions based on participant responses. Results of text analysis suggest that in case study communities, outdoor experiences in salt marshes inspire serenity in Massachusetts, influence shore identities in Virginia, and promote stewardship cultivation in Georgia. Perceived threats to these benefits, such as the threat of residential development, industrial pollution, and increasing flood risk, together constitute the context for various community responses related to marsh protection. Results supplement information from extant economic valuations and show the importance of utilizing diverse methods to elicit information on soci

openCustomJun 2017View details →

zenodo40/100

Brainport, Highway pilot, car in manual mode, but receiving adaption instructions

Scenario description: The driving adaptation car is driven around the track in manual mode, but driving instructions are communicated to the driver. Session description: 12 laps with Jaguar F-Pace on Automotive Campus Test Track Datasets descriptions: AUTOPILOT_BrainPort_HighwayPilot_AdasCommand: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle AUTOPILOT_BrainPort_HighwayPilot_Anomaly: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_HighwayPilot_Hazard: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage: Data sent between all devices, vehicles and services Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_HighwayPilot_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

opencc-by-4.0Jan 2020View details →

zenodo40/100

Brainport, Highway pilot, driving adaptation at hazards locations

Scenario description: The driving adaptation car drives around the track in simulated autonomous mode (ACC) and applies ADASINs at Hazards locations. Session description: 25 laps with Jaguar F-Pace on Automotive Campus Test Track Datasets descriptions: AUTOPILOT_BrainPort_HighwayPilot_AdasCommand: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle AUTOPILOT_BrainPort_HighwayPilot_Anomaly: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_HighwayPilot_Hazard: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage: Data sent between all devices, vehicles and services Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_HighwayPilot_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

opencc-by-4.0Jan 2020View details →

zenodo40/100

FIG. 50 in Mystacodon selenensis, the earliest known toothed mysticete (Cetacea, Mammalia) from the late Eocene of Peru: anatomy, phylogeny, and feeding adaptations

FIG. 50. — Lateral view of the right pelvic bones of some extant mysticetes. A, Balaenoptera musculus; B, Balaenoptera musculus; C, Megaptera novaeangliae; D, E, Balaena mysticetus. The iliac, pubic and ischial portions are, respectively, in blue, yellow and green. Modified from Struthers (1893).

opencc-zeroMay 2019View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record