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17,039 results for “Natural”
Diet can alter the cost of resistance to a natural parasite in Caenorhabditis elegans
<p>Resistance to parasites confers a fitness advantage, yet hosts show substantial variation in resistance in natural populations. Evolutionary theory indicates that resistant and susceptible genotypes can coexist if resistance is costly, but there is mixed evidence that resistant individuals have lower fitness in the absence of parasites. One explanation for this discrepancy is that the cost of resistance varies with environmental context. We tested this hypothesis using Caenorhabditis elegans and its natural microsporidian parasite, Nematocida ironsii. We used multiple metrics to compare the fitness of two near-isogenic host genotypes differing at regions associated with resistance to N. ironsii. To quantify the effect of the environment on the cost associated with these known resistance regions, we measured fitness on three microbial diets. We found that the cost of resistance varied with both diet and the measure of fitness. We detected no cost to resistance, irrespective of diet, when fitness was measured as fecundity. However, we detected a cost when fitness was measured in terms of population growth, and the magnitude of this cost varied with diet. These results provide a proof-of-concept that, by mediating the cost of resistance, environmental context may govern the rate and nature of resistance evolution in heterogeneous environments.</p>
Data and code for: Local infectious disease experience influences vaccine refusal rates: a natural experiment
<p>Vaccination has been critical to the decline in infectious disease prevalence in recent centuries. Nonetheless, vaccine refusal has increased in recent years, with complacency associated with reductions in disease prevalence highlighted as an important contributor. We exploit a natural experiment in Glasgow at the beginning of the 20th century to investigate whether prior local experience of an infectious disease matters for vaccination decisions. Our study is based on smallpox surveillance data and administrative records of parental refusal to vaccinate their infants. We analyse variation between administrative units of Glasgow in cases and deaths from smallpox during two epidemics over the period 1900–1904, and vaccine refusal following its legalisation in Scotland in 1907 after a long period of compulsory vaccination. We find that lower local disease incidence and mortality during the epidemics were associated with higher rates of subsequent vaccine refusal. This finding indicates that complacency influenced vaccination decisions in periods of higher infectious disease risk, responding to local prior experience of the relevant disease, and has not emerged solely in the context of the generally low levels of infectious disease risk of recent decades. These results suggest that vaccine delivery strategies may benefit from information on local variation in incidence.</p>
Sustaining Cities, Naturally Webinar: Recording Day 2
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This presentation was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Data from: Hurry Up and Wait: Managing the Inherent Mismatches in Timescales in Natural and Artificial Photosynthetic Systems
<p>Houle, Frances A.; Yano, Junko; Ager, Joel W.;<br> Hurry Up and Wait: Managing the Inherent Mismatches in Timescales in<br> Natural and Artificial Photosynthetic Systems</p> <p><strong>Files</strong><br> Supplementary data coupled reaction-transport and cascades.xlxs<br> Input parameters for Kinetiscope calculations and associated figure data</p> <p>CO2R microkinetic model-DRC.ipynb<br> CO2R microkinetic model-DRC.pdf<br> Jupyter notebook and pdf for degree of rate control in CO2 reduction<br> Packages used:<br> Python: 3.7.7<br> Numpy: 1.21.5<br> SciPy: 1.7.3<br> Matplotlib: 3.5.1</p> <p>Wang 2018 Pathway 1 only DRC.ipynb<br> Wang 2018 Pathway 1 only DRC.pdf<br> Jupyter notebook and pdf for degree of rate control photoelectrocatalytic water oxidation<br> Python: 3.7.7<br> Numpy: 1.21.5<br> SciPy: 1.7.3<br> mpmath: 1.2.1<br> Matplotlib: 3.5.1</p>
Urbanus and Cosgrove Nature Communications (2023) - scRNAseq fastq files GFP positive sample in Figure 2
<p>This dataset contains .fastq files for the GFP negative sample in the scRNAseq dataset used in Figures 2. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 18 months scRNAseq fastq files (mouse 2) for Figures 5 and 6
<p>This dataset contains .fastq files for the 18 month timepoint (mouse 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p> <p> </p> <p> </p>
List of non-naturalized plant species present in France extracted from Pl@ntNet data (exotic ornamental and cultivated plants in particular).
<p>This dataset contains the list of plant species that have been observed on the French territory using the <a href="https://plantnet.org/">Pl@ntNet </a>application and that are NOT know as being either native or naturalized according to Kew's Plants of the World Online repository (<a href="https://powo.science.kew.org/">POWO</a>). Such species are typically exotic species managed by humans in anthropized environments such as guardens, houses or cultivated areas. This includes commercialized plants for various usage such as ornemental plants, eatable plants, phytotherapy, etc. The list contains 5,589 species, each associated with its scientific name and the number of valid Pl@ntNet observations of that species geo-localized in the metropolitan French territory. </p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 6 months scRNAseq fastq files (mouse 1 and 2) for figures 5 and 6
<p>This dataset contains .fastq files for the 6 month timepoint (mouse 1 & 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing
<p><strong>Objectives</strong></p> <p>There is much interest in utilizing clinical data for developing prediction models for Alzheimer disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured Electronic Health Record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.</p> <p><strong>Materials and Methods</strong></p> <p>We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by two clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings.</p> <p><strong>Results</strong></p> <p>Documentation rates for each phenotype varied in the structured versus unstructured EHR. Inter-annotator agreement was high (Cohen's kappa = 0.72–1) and positively correlated with the NLP-based phenotype extraction pipeline's performance (average F1-score = 0.65-0.99) for each phenotype.</p> <p><strong>Discussion</strong></p> <p>We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine-learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success.</p> <p><strong>Conclusion</strong></p> <p>Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability. </p>
Polarization dataset - Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies
<p>This data are relative to the polarization measurements of the paper </p> <p>"Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies"</p> <p>The dataset consists in 8 .txt files that represent the polarization measurements of mixtures of FeS and Olivine (ol) with different mass ratios. In the name of each file it is specified the mass percentage of the two components respect with the total mass of the sample (eg. data_ol_FeS_10-90_530.txt is the sample composed by 10% olivine and 90% FeS, measured at 530 nm). </p> <p>In each file, the data are organized in the following columns: </p> <p>#phase_angles[°] #Q/I #U/I #V/I #DOLP #DC #delta_Q/I #delta_U/I #delta_V/I #delta_DOLP #delta_dc</p> <p>"delta" refers to the standard deviation of the measurement upon rotation of the sample on the azimuthal axis. <br> </p>
Efficient coding of natural scenes improves neural system identification
<p>Dataset for <a href="https://www.biorxiv.org/content/10.1101/2022.01.10.475663v3">Qiu et al., 2022</a>.</p> <blockquote> <p>This work was supported by the German Research Foundation (DFG; SFB 1233, Robust Vision: Inference Principles and Neural Mechanisms, projects 10 and 12, project number 276693517; GRK2381, project number 335549539), the Germany’s Excellence Strategy (EXC 2064/1, project number 390727645), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (agreement No 674901); the Max Planck Society (M.FE.A.KYBE0004); the German Ministry of Education and Research (BMBF; FKZ: 01GQ1002), and the Tübingen AI Center (FKZ: 01IS18039A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p> </blockquote>
Dataset related to the responses of eggplant plants to Bemisia Tabaci whitefly in presence of one of its natural enemies, the Macrolophus pygmaeus plant bug
<p>This dataset assesses the response of eggplant plants to 3 conditions: noninfested control plants, plants infested by Bemisia tabaci whitefly, and plants infested by Bemisia tabaci where Macrolophus pygmaeus plant bug was also released.</p> <p>The data of the various parameters relating to the responses of the different eggplant plants to the three tested conditions were analyzed using a one-way ANOVA. Where significant differences were detected, the means were separated by Tukey’s HSD test (p < 0.05). Statistical analysis was carried out using the program Statistica (StatSoft, TIBCO Software Inc., Tulsa, OK, USA).</p> <p>The provided dataset was used in the investigation of whether the Macrolophus pygmaeus (Hemiptera: Miridae) plant bug can mitigate the damage caused to plants by the Bemisia tabaci whitefly (Hemiptera: Aleyrodidae). A discussion and interpretation of the data, as well as subsequent results, is provided in the scientific publication <a href="https://zenodo.org/record/7660447#.Y_Soh3aZObg">"Can Macrolophus pygmaeus (Hemiptera: Miridae) Mitigate the Damage Caused to Plants by Bemisia tabaci (Hemiptera: Aleyrodidae)?"</a>, published in the <em><strong><a href="https://www.mdpi.com/2075-4450/14/2/164">Insects</a> </strong></em>journal. </p> <p>This research was conducted within the VIRTIGATION project, which is part of the EU Open Research Data pilot. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101000570.</p>
Figs 6–8 in A new species of Xenomyia Malloch, 1921 (Diptera: Muscidae) from South Africa, a probable natural antagonist of blackflies (Diptera: Simuliidae)
Figs 6–8. Xenomyia osculata sp. n. ♂ (paratype). 6. Sternite 5, dorsal view. 7. Hypandrium and aedeagus, lateral view. 8. Hypandrium and some aedeagal processes, dorsal view. a - epiphallus; b - phallapodeme; c - postgonite (paramere); d - praegonite (gonopod).
Figs 3–5 in A new species of Xenomyia Malloch, 1921 (Diptera: Muscidae) from South Africa, a probable natural antagonist of blackflies (Diptera: Simuliidae)
Figs 3–5. Xenomyia osculata sp. n. ♂ (paratype). 3. Head, lateral view. 4. Fore femur, posterior view. 5. Wing.
IODP Expedition 385 Natural gamma radiation
<p>Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.</p>
The turnover of plant-frugivore interactions along plant range expansion: consequences for natural colonisation processes
<p><span>Plant-animal mutualisms such as seed dispersal are key interactions for sustaining plant range shifts. Whether the organisation of interactions with seed dispersers is reconfigured along the expansion landscape template, and its effects accelerating or slowing colonisation, remain elusive. Here we analyse plant-frugivore interactions in a scenario of rapid population expansion of a Mediterranean juniper. We combined complex network analyses with intensive field surveys, sampling interactions between individual plants and frugivores by DNA-Barcoding and phototrapping over two seasons. We assess the role of intrinsic and extrinsic intraspecific variability in shaping interactions and we estimate the contribution of individual plants to seed rain. The whole interaction network was highly structured, with a distinct set of modules including individual plants and frugivore species arranged concordantly along the expansion gradient. The modular configuration found was partially shaped by individual neighbourhood context (density and fecundity) and phenotypic traits (cone size). Interaction reconfiguration resulted in a higher and uneven contribution to seed dispersal rain by individuals of the expansion boundaries, providing signals of the colonisation local-history. Our study provides novel insights into the key role of mutualistic interactions in colonisation scenarios by promoting fast plant expansion processes.</span></p>
VCF file from: Signatures of local adaptation to current and future climate in phenology-related genes in natural populations of Quercus robur
<p>Qrob_seqcap_87_18799.vcf - containing 18,799 single nucleotide variants (SNPs) in phenology-related genes in 87 individuals of Quercus robur from 6 populations, described in Meger et al.</p> <p> </p>
Swiss Dwellings: A large dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality and geometric analysis
<p><strong>Introduction</strong></p> <p>This dataset contains detailed data on over 45,000 apartments (370,000 rooms) in ~3,100 buildings including their geometries, room typology as well as their visual, acoustical, topological, and daylight characteristics. Additionally, we have included location-specific characteristics for the buildings, including climatic data and points of interest within walking distance.</p> <p><strong>Changelog</strong></p> <ul> <li><strong>v3.0.0 (2023-03-31):</strong> <ul> <li>Updated the dataset increasing the total number of apartments to 45176 and incorporating fixes to some of the sites. The update includes re-digitized apartments and thus alters some ID values.</li> </ul> </li> <li><strong>v2.2.1 (2023-03-10):</strong> <ul> <li>A file, <code>location_ratings.csv</code>, has been included to provide ratings of the locations in which the buildings are situated. The ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at the buildings' addresses. Details for the different dimensions are provided below.</li> <li>The file <code>location.csv</code> has been updated to include the minimum and maximum temperatures for the locations in which the buildings are situated.</li> </ul> </li> <li><strong>v2.1.0 (2022-12-23)</strong>: <ul> <li>A file, <code>locations.csv</code>, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated</li> </ul> </li> <li><strong>v2.0.0 (2022-10-17):</strong> <ul> <li>Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field <code>unit_usage</code> describes whether an area belongs to a commercial, residential, janitor or public part of the building</li> <li>Added the fields <code>elevation</code> and <code>height</code> to <em>geometries.csv</em> to describe the elevation above the terrain surface and the height of objects.</li> <li>Added the field <code>plan_id</code> which allows identifying which floors are based on the same floor plan (in some cases multiple floors of a building share the same floor plan</li> <li>Improved the ordering of fields in the CSV files (instead of alphabetic order)</li> <li>Minor changes to individual sites</li> </ul> </li> </ul> <p><strong>Procurement</strong></p> <p>The data is sourced from commercial clients of <a href="https://www.archilyse.com/">Archilyse AG</a> specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure the accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).</p> <p><strong>Geometries</strong></p> <p>The dataset contains a file <code>geometries.csv</code> which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.</p> <p>In total, the datasets contain the 2D geometry of ~1.7 million separators (walls, railings), ~715,000 openings (windows, doors), ca. 520,000 areas (rooms, bathrooms, kitchens, etc.), and ~315,000 features (sinks, toilets, bathtubs, etc.).</p> <p>Each row contains:</p> <ul> <li><code>apartment_id</code>: The ID of the apartment (for features, areas), <em>note</em>: an apartment id is only unique per site</li> <li><code>site_id</code>: The ID of the site</li> <li><code>building_id</code>: The ID of the building</li> <li><code>floor_id</code>: The ID of the floor</li> <li><code>plan_id</code>: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same plan</li> <li><code>unit_id</code>: The ID of the unit in which the element is spatially contained (for features, areas)</li> <li><code>area_id</code>: The ID of the area in which the element is spatially contained (for features)</li> <li><code>unit_usage</code>: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITOR</li> <li><code>entity_type</code>: The entity type (<em>area, separator, opening, feature</em>)</li> <li><code>entity_subtype</code>: The entity’s sub-type (e.g. <em>WALL</em>)</li> <li><code>geometry</code>: The element’s geometry as a <a href="https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry">WKT</a> geometry in meters. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.</li> <li><code>elevation</code>: The object's elevation above the terrain surface in meters. We assume one terrain baseline per building, thus all walls in a given floor share the same elevation value. However, windows in particular might start at different elevations and have differing heights.</li> <li><code>height</code>: The height of the entity in meters, <em>note</em>: In many cases, a default height is assumed</li> </ul> <p>An example:</p> <table> <thead> <tr> <th scope="col">column</th> <th scope="col"> </th> </tr> </thead> <tbody> <tr> <td>apartment_id</td> <td> <p>d4438f2129b30290845ce7eef98a5ba7</p> </td> </tr> <tr> <td>site_id</td> <td>127</td> </tr> <tr> <td>building_id</td> <td>164</td> </tr> <tr> <td>plan_id</td> <td>492</td> </tr> <tr> <td>floor_id</td> <td>861</td> </tr> <tr> <td>unit_id</td> <td>63777</td> </tr> <tr> <td>area_id</td> <td>767676</td> </tr> <tr> <td>unit_usage</td> <td>RESIDENTIAL</td> </tr> <tr> <td>entity_type</td> <td>area</td> </tr> <tr> <td>entity_subtype</td> <td>LIVING_ROOM</td> </tr> <tr> <td>geometry</td> <td> <p>POLYGON ((-6.1501158933490139 -4.8490786654693...</p> </td> </tr> <tr> <td>elevation</td> <td>0</td> </tr> <tr> <td>height</td> <td>2.6</td> </tr> </tbody> </table> <p><strong>Simulations</strong></p> <p>Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file <code>simulations.csv</code> contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns <code>area_id</code>, <code>unit_id</code>, <code>apartment_id</code>, <code>floor_id</code>, <code>building_id</code>, <code>site_id</code> as defined above as well as 367 simulation columns. Each simulation column is formatted as:</p> <pre><code><simulation_category>_<simulation_dimensions>_<aggregation_function></code></pre> <p>For instance. the column <code>view_buildings_median</code> describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.</p> <p><strong>Layout</strong></p> <p>The <em>layout</em> features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.</p> <p>Area Basics / Geometry</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_area_type</td> <td>The area’s area type</td> </tr> <tr> <td>layout_net_area</td> <td>The area’s share of the apartment’s net area (e.g. 0 for a balcony)</td> </tr> <tr> <td>layout_area</td> <td>The area’s actual area</td> </tr> <tr> <td>layout_perimeter</td> <td>The area’s perimeter</td> </tr> <tr> <td>layout_compactness</td> <td>The area’s compactness (the Polsby–Popper score)</td> </tr> <tr> <td>layout_room_count</td> <td>The area’s share to the apartment’s room count</td> </tr> <tr> <td>layout_is_navigable</td> <td>True if the area is navigable by a wheelchair</td> </tr> </tbody> </table> <p>Area Features</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_has_sink</td> <td>True if the area has a sink</td> </tr> <tr> <td>layout_has_shower</td> <td>True if the area has a shower</td> </tr> <tr> <td>layout_has_bathtub</td> <td>True if the area has a bathtub</td> </tr> <tr> <td>layout_has_toilet</td> <td>True if the area has a toilet</td> </tr> <tr> <td>layout_has_stairs</td> <td>True if the area has stairs</td> </tr> <tr> <td>layout_has_entrance_door</td> <td>True if the area is directly leading to an exit of the apartment</td> </tr> </tbody> </table> <p>Area Windows / Doors</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_number_of_doors</td> <td>The number of doors directly leading to the area</td> </tr> <tr> <td>layout_number_of_windows</td> <td>The number of windows of the area</td> </tr> <tr> <td>layout_door_perimeter</td> <td>The sum of all door lengths directly leading to the area</td> </tr> <tr> <td>layout_window_perimeter</td> <td>The sum of all window lengths of the area</td> </tr> </tbody> </table> <p>Area Walls / Railings</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_open_perimeter</td> <td>The sum of all of the boundaries of the area that are neither walls nor railings</td> </tr> <tr> <td>layout_railing_perimeter</td> <td>The sum of all of the boundaries of the area that are railings</td> </tr> <tr> <td>layout_mean_walllengths</td> <td>The mean length of the area’s sides</td> </tr> <tr> <td>layout_std_walllengths</td> <td>The standard deviation of the lengths of the area’s sides</td> </tr> </tbody> </table> <p>Area Adjacency</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_connects_to_bathroom</td> <td>True if the area connects to a bathroom</td> </tr> <tr> <td>layout_connects_to_private_outdoor</td> <td>True if the area connects to an outside area that is private to the apartment</td> </tr> </tbody> </table> <p><strong>View</strong></p> <p>The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water, etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a particular object category occupies in the spherical field of view.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, the column <code>view_greenery_p20</code> describes the amount of greenery that can be seen from at least 20% of the positions in the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>view_buildings</td> <td>The amount of visible buildings</td> </tr> <tr> <td>view_greenery</td> <td>The amount of visible greenery</td> </tr> <tr> <td>view_ground</td> <td>The amount of visible ground</td> </tr> <tr> <td>view_isovist</td> <td>The amount of visible isovist</td> </tr> <tr> <td>view_mountains_class_2</td> <td>The amount of visible mountains of UN mountain class 2</td> </tr> <tr> <td>view_mountains_class_3</td> <td>The amount of visible mountains of UN mountain class 3</td> </tr> <tr> <td>view_mountains_class_4</td> <td>The amount of visible mountains of UN mountain class 4</td> </tr> <tr> <td>view_mountains_class_5</td> <td>The amount of visible mountains of UN mountain class 5</td> </tr> <tr> <td>view_mountains_class_6</td> <td>The amount of visible mountains of UN mountain class 6</td> </tr> <tr> <td>view_railway_tracks</td> <td>The amount of visible railway_tracks</td> </tr> <tr> <td>view_site</td> <td>The amount of visible site</td> </tr> <tr> <td>view_sky</td> <td>The amount of visible sky</td> </tr> <tr> <td>view_tertiary_streets</td> <td>The amount of visible tertiary_streets</td> </tr> <tr> <td>view_secondary_streets</td> <td>The amount of visible secondary_streets</td> </tr> <tr> <td>view_primary_streets</td> <td>The amount of visible primary_streets</td> </tr> <tr> <td>view_pedestrians</td> <td>The amount of visible pedestrians</td> </tr> <tr> <td>view_highways</td> <td>The amount of visible highways</td> </tr> <tr> <td>view_water</td> <td>The amount of visible water</td> </tr> </tbody> </table> <p><strong>Sun</strong></p> <p>Sun simulations help to understand the impact of solar radiation on the object. The outcome of the sun simulations helps to identify surfaces that have great solar potential. Sun simulations are defined by the amount of solar radiation on each individual hexagon from the analyzed object. The sun simulation not only includes direct sun but also considers scattered light. The sun simulation values are given in Kilolux (klx). Simulations are performed for the days of the summer solstice, winter solstice, and the vernal equinox.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, column <code>sun_201806211200_median</code> describes the median amount of direct daylight received on the positions in the area.</p> <p>Vernal Equinox</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201803210800</td> <td>Daylight at 08:00 on 21st of March</td> </tr> <tr> <td>sun_201803211000</td> <td>Daylight at 10:00 on 21st of March</td> </tr> <tr> <td>sun_201803211200</td> <td>Daylight at 12:00 on 21st of March</td> </tr> <tr> <td>sun_201803211400</td> <td>Daylight at 14:00 on 21st of March</td> </tr> <tr> <td>sun_201803211600</td> <td>Daylight at 16:00 on 21st of March</td> </tr> <tr> <td>sun_201803211800</td> <td>Daylight at 18:00 on 21st of March</td> </tr> </tbody> </table> <p>Summer Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201806210600</td> <td>Daylight at 06:00 on 21st of June</td> </tr> <tr> <td>sun_201806210800</td> <td>Daylight at 08:00 on 21st of June</td> </tr> <tr> <td>sun_201806211000</td> <td>Daylight at 10:00 on 21st of June</td> </tr> <tr> <td>sun_201806211200</td> <td>Daylight at 12:00 on 21st of June</td> </tr> <tr> <td>sun_201806211400</td> <td>Daylight at 14:00 on 21st of June</td> </tr> <tr> <td>sun_201806211600</td> <td>Daylight at 16:00 on 21st of June</td> </tr> <tr> <td>sun_201806211800</td> <td>Daylight at 18:00 on 21st of June</td> </tr> <tr> <td>sun_201806212000</td> <td>Daylight at 20:00 on 21st of June</td> </tr> </tbody> </table> <p>Winter Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201812211000</td> <td>Daylight at 10:00 on 21st of December</td> </tr> <tr> <td>sun_201812211200</td> <td>Daylight at 12:00 on 21st of December</td> </tr> <tr> <td>sun_201812211400</td> <td>Daylight at 14:00 on 21st of December</td> </tr> <tr> <td>sun_201812211600</td> <td>Daylight at 16:00 on 21st of December</td> </tr> </tbody> </table> <p><strong>Noise / Window Noise</strong></p> <p>Noise level and the distribution of elements from an area help to understand how an object is exposed to the acoustics of this area. The acoustic simulation calculates the noise intensity on each individual hexagon from the analyzed object considering traffic and train noise datasets. Adjacent buildings are considered noise-blocking elements. The values are expressed in dBA (decibels).</p> <p>Window Noise</p> <p>The noise per window of a given area is aggregated via <code>min</code> and <code>max</code>. For instance, <code>window_noise_train_day_max</code> represents the maximum amount of noise received on any window of the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>window_noise_traffic_day</td> <td>The amount of noise received on the area’s windows from daytime car traffic</td> </tr> <tr> <td>window_noise_traffic_night</td> <td>The amount of noise received on the area’s windows from night-time car traffic</td> </tr> <tr> <td>window_noise_train_day</td> <td>The amount of noise received on the area’s windows from daytime train traffic</td> </tr> <tr> <td>window_noise_train_night</td> <td>The amount of noise received on the area’s windows from night-time train traffic</td> </tr> </tbody> </table> <p>Area-Wise Noise</p> <p>The area-wise noise describes the amount of noise received from a noise source aggregated over the whole area in an unaggregated form. For instance, <code>noise_traffic_night</code> describes the dBA of noise received in the area from car traffic at night when propagating noise from all windows.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>noise_traffic_day</td> <td>The amount of noise received in the area from daytime car traffic</td> </tr> <tr> <td>noise_traffic_night</td> <td>The amount of noise received in the area from night-time car traffic</td> </tr> <tr> <td>noise_train_day</td> <td>The amount of noise received in the area from daytime train traffic</td> </tr> <tr> <td>noise_train_night</td> <td>The amount of noise received in the area from night-time train traffic</td> </tr> </tbody> </table> <p><br> <strong>Connectivity</strong></p> <p>Centrality simulations help to analyze a floor plan, whether it’s a shopping mall and you want to identify prominent areas in order to select the most prominent spot or it’s an interior design circulation path and you want to determine open floor plan areas. Centrality simulations are done using topological measures that score grid cells by their importance as a part of a grid cell network.</p> <p>The distances and centralities are aggregated via <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, <code>connectivity_balcony_distance_min</code> describes the shortest distance to the next balcony from the point closest to the balcony in the area.</p> <p>Distances</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_room_distance</td> <td>Distance to the next area of type ROOM</td> </tr> <tr> <td>connectivity_living_dining_distance</td> <td>Distance to the next area of type LIVING_DINING</td> </tr> <tr> <td>connectivity_bathroom_distance</td> <td>Distance to the next area of type BATHROOM</td> </tr> <tr> <td>connectivity_kitchen_distance</td> <td>Distance to the next area of type KITCHEN</td> </tr> <tr> <td>connectivity_balcony_distance</td> <td>Distance to the next area of type BALCONY</td> </tr> <tr> <td>connectivity_loggia_distance</td> <td>Distance to the next area of type LOGGIA</td> </tr> <tr> <td>connectivity_entrance_door_distance</td> <td>Distance to the next apartment exit</td> </tr> </tbody> </table> <p>Centralities</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_eigen_centrality</td> <td>The Eigen-Centrality value</td> </tr> <tr> <td>connectivity_betweenness_centrality</td> <td>The Betweenness-Centrality value</td> </tr> <tr> <td>connectivity_closeness_centrality</td> <td>The Closeness-Centrality value</td> </tr> </tbody> </table> <p><strong>Location Properties</strong></p> <p>In addition to the apartment-related data, we also provide simulation data on the climatic, and infrastructural characteristics of the locations. The file <code>locations.csv</code> contains the simulation data aggregated on a per-building basis. Each row contains the identifier <code>building_id</code> corresponding to the building ids referenced in <code>geometries.csv</code> and <code>simulations.csv</code>.</p> <p><strong>Climate</strong></p> <p>The climate features represent 39 simple features based on the spatial climate analysis of Meteo Swiss as derived from <a href="https://www.meteoswiss.admin.ch/climate/the-climate-of-switzerland/spatial-climate-analyses.html.">MeteoSwiss</a>. Each column is formatted as <code>climate_<category>_<period>. </code>For instance, the column <code>climate_tnorm_january</code> describes the monthly mean temperature in degrees Celsius (from the norm period of 1991-2020) at the location of the building. The aggregation methods vary per simulation category and are described in detail below.</p> <p>Temperature Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_tnorm_year</td> <td>The yearly mean temperature in degrees Celsius of the current norm period from 1991 to 2020 (TnormY9120)</td> </tr> <tr> <td>climate_tnorm_january</td> <td>The monthly mean temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tnorm_februry</td> <td>The monthly mean temperature in February in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_tnorm_december</td> <td>The monthly mean temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tminnorm_january</td> <td>The monthly minimum temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TminnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tminnorm_december</td> <td>The monthly minimum temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tmaxnorm_january</td> <td>The monthly maximum temperature in January in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tmaxnorm_december</td> <td>The monthly maximum temperature in December in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> </tbody> </table> <p>Sunshine Duration Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_snorm_year</td> <td>The yearly mean relative sunshine duration in percent of the current norm period from 1991 to 2020 (SnormY9120). Relative sunshine duration (RSD) is the ratio between the effective sunshine duration and the duration maximally possible if no clouds were covering the sun. A period with sunshine is defined as a period when the direct solar irradiance exceeds 200 W/m²</td> </tr> <tr> <td>climate_snorm_january</td> <td>The monthly mean relative sunshine duration for January in percent of the current norm period</td> </tr> <tr> <td>climate_snorm_februry</td> <td>The monthly mean relative sunshine duration for February in percent of the current norm period</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_snorm_december</td> <td>The monthly mean relative sunshine duration for December in percent of the current norm period</td> </tr> </tbody> </table> <p>Precipitation Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_rnorm_year</td> <td>The yearly mean precipitation in mm of the current norm period (RnormY9120)</td> </tr> <tr> <td>climate_rnorm_january</td> <td>The monthly mean precipitation for January in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>climate_rnorm_februry</td> <td>The monthly mean precipitation for February in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_rnorm_december</td> <td>The monthly mean precipitation for December mm of the current norm period (RnormM9120)</td> </tr> </tbody> </table> <p><strong>10-Minute Walkshed Infrastructure</strong></p> <p>Based on OpenStreetMap data and its tagging system we counted all 465 tags (key and value tuples as listed here: https://wiki.openstreetmap.org/wiki/Map_features) which can be reached within a 10-minute walk from the location of the building. Each column is formatted as <code>walkshed_<poi_category>_<poi_type>. </code>For instance, the column <code>walkshed_shop_coffee</code> describes the number of coffee shops located within 10 minutes of walking from the building.</p> <p>The following is an excerpt of support categories and their corresponding types.</p> <ul> <li><code>shop: antique, art, ...</code></li> <li><code>amenity: art, atm, ...</code></li> <li><code>tourism: alpine, attraction, ...</code></li> <li><code>leisure: amusement, beach, ...</code></li> <li><code>healthcare: clinic, dentist, ...</code></li> <li><code>historic: archaeological, battlefield, ...</code></li> <li><code>ariaelway: station</code></li> </ul> <p><strong>Location Ratings</strong></p> <p>The location ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at locations in which the buildings are situated. The file location_ratings.csv provides the following information:</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>location_rating_MIKRAT_W</td> <td>Living situation - Overall (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_IMAGE_W</td> <td>Living situation - Image (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_DL_W</td> <td>Living situation - Service Quality (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_FZ_W</td> <td> <p>Living situation - Leisure Quality (1.0=worst, 5.0=best)</p> </td> </tr> <tr> <td>location_rating_NASE_W_DOM</td> <td>The most dominant segment of demand:<br> <br> 1 Rural-traditional<br> 2 Modern worker<br> 3 Transitional-alternative<br> 4 Traditional middle class<br> 5 Liberal middle class<br> 6 Established alternative<br> 7 Upper middle class<br> 8 Professional elite<br> 9 Urban elite<br> 10 Unknown<br> <br> <a href="https://en.fpre.ch/marktdaten/nachfragersegmente/nachfragersegmente-im-wohnungsmarkt/">More Information</a></td> </tr> <tr> <td>location_rating_FGFRQZ</td> <td> <p>The mean number of pedestrians per hour throughout a day between 7 am and 8 pm of an average working day.<br> <br> 1 <50<br> 2 50-100<br> 3 100-200<br> 4 200-500<br> 5 >500</p> </td> </tr> </tbody> </table>
Data and code_Ombadi et al., Nature (2023)_A warming induced reduction in snow fraction amplifies rainfall extremes
<p>This repository contains intermediary and final outputs of the analysis presented in the article: "Ombadi et al., Nature (2023), A warming-induced reduction in snow-fraction amplifies rainfall extremes.". Additionally, the repository also contains codes for analysis and visualization of results presented in this article. The codes are primarily written in Python and provided as Jupyter Notebooks (.py files) with the exception of one code written in R language. </p>
Data for: Natural genetic variation in a dopamine receptor is associated with variation in female fertility in Drosophila melanogaster
<p>Fertility is a major component of fitness but its genetic architecture remains poorly understood. Using a full diallel cross of 50 <em>Drosophila</em> <em>melanogaster</em> Genetic Reference Panel inbred lines with whole genome sequences, we found substantial genetic variation in fertility largely attributable to females. We mapped genes associated with variation in female fertility by genome-wide association analysis of common variants in the fly genome. Validation of candidate genes by RNAi knockdown confirmed the role of the dopamine 2-like receptor (<em>Dop2R</em>) in promoting egg laying. We replicated the <em>Dop2R</em> effect in an independently collected productivity dataset and showed that the effect of the <em>Dop2R</em> variant was mediated in part by regulatory gene expression variation. This study demonstrates the strong potential of genome-wide association analysis in this diverse panel of inbred strains and subsequent functional analyses for understanding the genetic architecture of fitness traits.</p>
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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
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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.