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Figure 10. N in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 10. N. dolichopus sp. n., holotype. Pereopods III–IV, detail of pereopod IV dactylus. Retinacle of pleopod II. Uropods I–III. Telson.
Figure 2 in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 2. Distribution of characters ''body shape'' (character 2, CI51, RI51; left) and ''body size'' (character 1, CI50.28, RI50.37; right). Note that the body shape is plesiomorphic in most of the ''Orniphargus'' taxa, and large body size is a highly convergent (possibly troglomorphic) character.
Figure 1. A in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 1. A strict consensus tree of 34 most parsimonious trees (length5472; CI50.26, RI50.60). Values of Bremer support index (decay index) are indicated below branches. Taxa traditionally assigned to the ''Orniphargus'' species aggregate, as well as clades for which character analysis was performed, are encircled in boxes. Note 1: Despite of its position on the cladogram N. pectinicauda has never been considered as an ''Orniphargus'' taxon. Note 2: Taxa are named according to their lowest rank. For full names, see Tables I and II.
Figure 5 in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 5. Distribution of characters ''type of setae/spines along postero-dorsal margin of pleonites'' (character 14, CI50.2, RI50.5; left) and ''uropod I rami-spines'' (character 58, CI50.37, RI50.73; right). Both characters are supposed to be characteristics of ''Orniphargus'' (S. Karaman (1950c)).
Text-fig. 47. Scanning electron microscope (SEM) images of reticulate-foveolate pollen on the surface of a fruit of Serialis antiquum; Torres Vedras locality, Portugal. a, b) Pollen grain showing angular outline and tectum perforated by foveolae of different sizes; note the braided tectum ornamentation formed by aggregations of small ridges (b); aperture not known; c, d) Pollen grain showing angular outline, tectum perforated by foveolae that become smaller toward the presumed pole; note the relatively smooth tectum and faint pattern of braiding ridges (d); aperture not known. Specimens, TV43-S171535-01 (a, b), TV43-S171535-02 (c, d). Scale bars 6 Μm (a, c), 1 Μm (b, d). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 47. Scanning electron microscope (SEM) images of reticulate-foveolate pollen on the surface of a fruit of Serialis antiquum; Torres Vedras locality, Portugal. a, b) Pollen grain showing angular outline and tectum perforated by foveolae of different sizes; note the braided tectum ornamentation formed by aggregations of small ridges (b); aperture not known; c, d) Pollen grain showing angular outline, tectum perforated by foveolae that become smaller toward the presumed pole; note the relatively smooth tectum and faint pattern of braiding ridges (d); aperture not known. Specimens, TV43-S171535-01 (a, b), TV43-S171535-02 (c, d). Scale bars 6 Μm (a, c), 1 Μm (b, d).
Synthetic dataset for mobile wireless networks with SUMO -- aggregated traces
<p>Aggregated dataset from a published wireless dataset generator base in SUMO mobility model. </p> <p> </p> <p>This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020 and SFRH/BD/132053/2017.</p>
Vertically pointing doppler radar profiles (24 GHz Metek MRR-2) at Mario Zucchelli Station (Terra Nova Bay, Antarctica), aggregated to 5min, monthly netCDF archive
<p>Vertical profiles along the first kilometre of atmosphere above the ground (from 105 to 1050 m AGL) of equivalent radar reflectivity factor (Ze), Doppler velocity (W) and Doppler spectral width (Sw) from a 24-GHz vertically pointing Micro Rain Radar MRR-2 by METEK GmbH positioned at Mario Zucchelli Station (Terra Nova Bay, Antarctica).</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/6fe32f1f-247e-493d-9cd3-88714e5b38ef" target="_blank" rel="noopener">https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/6fe32f1f-247e-493d-9cd3-88714e5b38ef</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure: </p> <h2>File "MZS_MRR_MeK_201912_5min.nc"</h2> <pre><strong> dimensions</strong>: <em>range </em>= 31; <em>time </em>= UNLIMITED; // (8928 currently) <strong>variables</strong>: float <em>Ze</em>(range=31, time=8928); :description = "Equivalent reflectivity factor relative to the most significant peak, dealiased, 5min non-logarithmic average. NaN means clear sky at the specified height."; :units = "dBZ"; :_ChunkSizes = 31U, 1U; // uint long <em>time_UTC</em>(time=8928); :description = "Measurement time. Timestamp indicates the end of the aggregation interval, e.g. 01-Mar-2020 00:05:00 represents the average of the variables between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>W</em>(range=31, time=8928); :description = "Mean Doppler Velocity of the most significant peak, dealiased, 5min average. Value not available in clear-sky conditions."; :units = "m s^-1"; :_ChunkSizes = 31U, 1U; // uint float <em>height</em>(range=31, time=8928); :description = "Height above instrument."; :units = "m"; :_ChunkSizes = 31U, 1U; // uint float <em>spectralWidth</em>(range=31, time=8928); :description = "Doppler Spectral Width of the most significant peak, dealiased, 5min average. Value not available in clear-sky conditions."; :units = "m s^-1"; :_ChunkSizes = 31U, 1U; // uint // <strong>global attributes</strong>: :<em>title </em>= "Micro rain radar data processed with IMProToo (Maahn, M. and Kollias, P., 2012), aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "IMProToo has been developed for improved snow measurements. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Dec 2019"; :<em>source </em>= "Micro Rain Radar 2 (MRR-2), METEK GmbH, at MZS (Antarctica), frequency: 24 GHz, power: 50 mW, antenna diameter: 60 cm [https://metek.de/product/mrr-2/]"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome (IT), l.baldini@isac.cnr.it"; :<em>location </em>= "Mario Zucchelli Station (Terra Nova Bay, Antarctica, 74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "22-Oct-2024 17:40:46 UTC"; :<em>coverage </em>= "Monthly coverage (Dec 2019): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created with IMProToo v0.107 [https://github.com/maahn/IMProToo], aggregated to 5 minutes temporal resolution with an average of the 1-minute values if least 3 out of 5 are not NaN."; </pre>
DATASET of INTEGRADDE Expression of Interest, including aggregated and anonymized results.
<p>This dataset corresponds to the one generated with information about those applying to the Expression of Interest (EOI) including participants’ attributes, e.g. country of origin, TRLs, type of applicants, scores obtained in the evaluation process, and funding status after the funnel process. All of this, for statistical purposes and analysis of the performance of the Expression of Interest</p>
Data set. Optical properties. J-aggregate:PVA polaritonic films.
<p>Optical properties (real and imaginary part of permittivity) of J-aggregate:PVA materials analysed in manuscript entitled "Bio-inspired building blocks for all-organic metamaterials from visible to near-infrared". </p> <p>arXiv preprint arXiv:2210.02315</p> <p> </p> <p> </p> <p> </p>
M100 dataset: time-aggregated data for anomaly detection
<p>This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, <a href="https://www.hpc.cineca.it/hardware/marconi100">https://www.hpc.cineca.it/hardware/marconi100</a>). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast. <br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.</p> <p>This particular dataset is made for anomaly detection purposes, it contains the same data as the main dataset but aggregated over time, with one Parquet file for each node. The data is distributed in tarballs, each one including all the files relative to the nodes contained in a given rack. For each file, the rows represent periods of 15 minutes, with the columns being aggregated values (average, standard deviation, min, max) over all the IPMI metrics that are available for the node; an additional column contains anomaly labels from Nagios.</p> <p>More details can be found in the companion repository: <a href="https://gitlab.com/ecs-lab/exadata">https://gitlab.com/ecs-lab/exadata</a>, including the spatial distribution of the nodes in the room.</p>
Molecular origin of the two-step mechanism of gellan aggregation
<p>Data presented in the article entitled <strong>Molecular origin of the two-step mechanism of gellan aggregation</strong>.</p>
Risk Factors that modify the familial aggregation for ischemic cardiovascular illness
<p><strong>Introduction:</strong> Ischemic cardiovascular disease is a global health problem. <strong>Objective:</strong> To determine the risk factors that modified the familial aggregation for ischemic cardiovascular disease in affected individuals. <strong>Material and Methods:</strong> An observational, analytical, longitudinal and retrospective case/control study was carried out from the population of the Banes municipality, Holguín province during May 2020-May 2022. The universe included all the diagnosed individuals and their families. By simple random sampling, the sample (149 cases) was obtained and the control group was formed at a ratio of 3:1 (447 individuals with no history of disease). The bioethical requirements were met. Inclusion/exclusion criteria were applied. The statistics were used: Chi square, Odd Ratio (OR), including p and confidence interval. The variables were operationalized: age, degree of consanguinity and risk factors. The family tree was obtained. <strong>Results:</strong> The relatives of first and second degree of consanguinity showed the highest incidence of disease. The age group 60-69 years was more affected. Familial aggregation for the disease was demonstrated (X<sup>2</sup>=45.93 OR=5.85 99.9% CI (3.29; 10.41)). Ischemic cardiomyopathy (43.3%) and arrhythmias (20.7%) were notable forms of presentation. The risk factors showed an association for the disease (X<sup>2</sup>=67.11 p ≤0.001). Arterial hypertension (X<sup>2</sup>=57.9 OR=3.04 99.9% CI (2.27; 4.06)) and smoking (X<sup>2</sup>=45.7 OR=2.66 99.9% CI (2.3 ; 5,6)) expressed a highly significant association for ischemic cardiovascular disease. <strong>Conclusions:</strong> The risk factors: arterial hypertension, smoking and family history modified the family aggregation for ischemic cardiovascular disease.</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>
Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
<p>Short Description:</p> <p>In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.</p> <p>Code and More Details: https://github.com/lepotatoguy/aqi</p>
Aggregated data issued from the JOBIM 2021 'Gender equality observational study'
<p>JOBIM 2021 Gender analysis</p> <p>This repository contains data and script related to our observation study of gender impact on asking behavior during JOBIM 2021. In agreement with our <a href="https://research.pasteur.fr/en/project/jobim-2021-pilot-project-gender-speaking-differences-in-academia/">data policy and RGPD regulations</a>, only aggregated, anonymous and/or publicly available information are posted in this repository. Zoom exports, registration survey and observation files containing names of askers and their accompanying scripts remains private.</p> <p>Citation</p> <p>If you wish to use our data please cite our manuscript: .https://www.biorxiv.org/content/10.1101/2022.03.07.483337v3</p>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</p>
Aggregated Earnings Call Dataset
<p>Aggregated Earnings Call Dataset for the paper "FLAG: Financial Long Document Regression via AMR-based GNN"</p> <p>It contained earnings calls data, along with associated price data, that we collected from companies in the S&P 1500 Composite Index from 2010 to 2019.</p> <p>CSV files without "tech" in their filenames contain the full amount of earnings calls we collected, and CSV files with "tech" in their filenames contain the portion of the dataset from the technology sector, which we use in the experiments detailed in the paper.</p>
Landscape composition and shannon diversity of landuse classes aggregated from CORINE land cover 2012
<p>Based on Copernicus <a href="https://land.copernicus.eu/pan-european/corine-land-cover">CORINE land cover data</a> from 2012, we aggregated the original CORINE land use classes into 8 classes: urban, agriculture, grassland, Broad-leaved forest, Coniferous forest, Mixed forest, natural/seminatural vegetation, and water (see clc_legend.txt). We calculated the landscape composition (percentage of each land use type according to corine land type) and shannon diversity for 100 - 5000 meters (100-1000 meter with 100 meter intervals, 1000 - 5000 meter with 500 meter interval) buffer area around Landklif plots.</p> <p>LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Aggregation of symbionts on hosts depends on interaction type and host traits
<p>Symbionts tend to be aggregated on their hosts, such that few hosts harbor the majority of symbionts. This ubiquitous pattern can result from stochastic processes, but aggregation patterns may also depend on the type of host-symbiont interaction, plus traits that affect host exposure and susceptibility to symbionts. Untangling how aggregation patterns both within and among populations depend on stochastic processes, interaction type and host traits remains an outstanding challenge. Here, we address this challenge by using null models to compare aggregation patterns in a neutral system of Balanomorpha barnacles attached to patellid limpets and a host-parasite system of Trinidadian guppies (Poecilia reticulata) and their Gyrodactylus spp. monogeneans. We first used a model to predict patterns of symbiont-host aggregation due to random partitioning of symbionts to hosts. This null model accurately predicted the aggregation of barnacles on limpets, but the degree of aggregation varied across 303 quadrats. Quadrats with larger limpets had less aggregated barnacles, whereas aggregation increased with variation in limpet size. Across 84 guppy populations, Gyrodactylus spp. parasites were significantly less aggregated than predicted by the null model. As in the neutral limpet-barnacle system, aggregation decreased with mean host size. Parasites were also significantly less aggregated on males than females because male guppies tended to have higher prevalence and lower parasite burdens than predicted by the null model. Together, these results suggest stochastic processes can explain aggregation patterns in neutral but not parasitic systems, though in both systems host traits affect aggregation patterns. Because the distribution of symbionts on hosts can affect symbiont evolution via intraspecific interactions, and reciprocally host behavior and evolution via host-symbiont interactions, identifying the drivers of aggregation enriches our understanding of host-symbiont interactions.</p>
Holopelagic Sargassum aggregations provide warmer microhabitats for associated fauna
<p><span>Drifting aggregations of <em>Sargassum</em> algae provide critical habitat for endemic, endangered, and commercially important species. They may also provide favorable microclimates for associated fauna. To quantify thermal characteristics of holopelagic <em>Sargassum</em> aggregations, we evaluated thermal profiles of 50 aggregations in situ in the Sargasso Sea. </span><span>Sea surface temperature (SST) in the center of </span><span>aggregations</span><span> was significantly higher than in nearby open water, and SST differential was independent of </span><span>aggregation</span><span> volume, area, and thickness. SST differential between </span><span>aggregation</span><span> edge and open water was smaller than those between </span><span>aggregation</span><span> center and </span><span>aggregation</span><span> edge and between </span><span>aggregation</span><span> center and open water. Water temperature was significantly higher inside and below </span><span>aggregations</span><span> compared to open water but did not vary inside </span><span>aggregations</span><span> with depth. </span><span>Holopelagic </span><em><span>Sargassum</span></em><span> aggregations provide warmer microhabitats for associated fauna, which may benefit marine ectotherms, though temperature differentials were narrow (up to 0.7 °C) over the range of </span><span>aggregation</span><span> sizes we encountered (area 0.01–15 m<sup>2</sup>). </span><span>We propose a hypothetical curve describing variation in SST differential with <em>Sargassum</em> aggregation size as a prediction for future studies to evaluate across temporal and geographic ranges. Our study provides a foundation for investigating the importance of thermal microhabitats in holopelagic <em>Sargassum</em> ecosystems</span><span>.</span></p>
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.