Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
181
datasets available to search
ShareScore release 0.9.0
Dataset results
181 results for “outdoor”
Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
Brutal outdoor concrete hexagon flower pot
Brutal outdoor concrete hexagon flower pot public space planter - **test for a community 3D print** https://en.wikipedia.org/wiki/Brutalist_architecture @ Usti nad Labem, regional goverment building https://goo.gl/maps/pGcUzD3mnbHxHVxbA Legendary 1980s design by Prefa Přeštice, more info: https://www.fb.com/architektura489/posts/1214134412117289 Photogrammmetry scan 160x24MP, Free download, 16K texture. Source: Objaverse 1.0 / Sketchfab
Dual Brutal outdoor concrete hexagon flower pot
Dual Brutal outdoor concrete hexagon flower pot public space planter https://en.wikipedia.org/wiki/Brutalist_architecture @ Prague, redisential area street https://goo.gl/maps/HR3kP8ytP5YtneL47 Legendary 1980s design by Prefa Přeštice, more info: https://www.fb.com/architektura489/posts/1214134412117289 Photogrammmetry scan 160x24MP, Free download, 3x8K texture Created in RealityCapture by Capturing Reality Source: Objaverse 1.0 / Sketchfab
Outdoor Scan - Abandonned Rusty Machines
Quick scan of some random rusty old machines found in the small city of Couze , Dordogne, France. Source: Objaverse 1.0 / Sketchfab
Outdoor fingerprint localization with BLE beacons
<p><strong>Introduction</strong></p><p>The data set contains received signal strength (RSS) measurements made with Bluetooth Low Energy (BLE) technology, which can be used for outdoor fingerprint-based localization applications, as presented in an article "<a href="https://ieeexplore.ieee.org/document/9900607">LOG-a-TEC Testbed Outdoor Localization Usign BLE Beacons</a>".</p><p><strong>Measurement setup</strong></p><p>The measurements were created with WL1837MOD radio connected to a <a href="https://log-a-tec.eu/hw-lgtc.html">in-house embedded device</a>. The data set was collected with 40 nodes of the <a href="https://log-a-tec.eu">LOG-a-TEC testbed</a> positioned at the campus of the Jožef Stefan Institute, Ljubljana. The experimentation area is composed of 5 x 26 positions separated by 1.2 m covering 150 square meters. On each position a mobile phone was broadcasting BLE advertising beacons with power of -2 dBm in interval of 100 ms. Surrounding testbed nodes were collecting the beacons for approximately a minute for each position.</p><p><strong>Data set</strong></p><p>Measurements are stored in JSON format where each object contains rss measurement (in dBm) with corresponding timestamp (in seconds). The folder contains two JSON files:</p><ul><li>spring_data.json - measurements made in May 2022,</li><li>winter_data.json - smaller measurements made in December 2021. This data set contains only the measurements from the middle row of the campus park.</li></ul>
Data from: Parentage of 920 gray-sided voles (Myodes rufocanus) born in a 3-ha outdoor enclosure between September 1992 and May 1994
<p class="MsoNormal"><span>This dataset provides the estimated birth location, sex, assigned parents, and estimated birth and death dates of 920 gray-sided voles (<em>Myodes rufocanus</em>) born in a 3-ha outdoor enclosure in Sapporo, Japan between September 1992 and May 1994, as well as capture–recapture data for 30 trapping sessions.</span><span> We introduced 22 males and 25 females from several natural populations into the enclosure in late September 1992. Individuals in the enclosure were captured using live traps every two weeks until late April 1994, except for periods with deep snow cover. The location, body weight, and reproductive status of each vole were monitored throughout the study period. Complementary trapping was performed within the home ranges of breeding females to mark juveniles as early as possible. Upon first capture, each individual was marked by toe clipping for subsequent identification, and the cut toes were used as DNA samples. For each individual, candidate parents were selected based on female reproduction history and capture points, and then parentage was determined using genotypes at 3–5 microsatellite loci with the CERVUS program. The results show that individuals born in the enclosure (<em>N</em> = 920) were derived from 215 litters, among which multiple males sired 51 litters. We used this database to elucidate the promiscuous mating system and inbreeding-avoidance mechanism of the gray-sided vole and to develop a ne</span><span>w method for estimating the frequency of multiple-male mating. This dataset will contribute to future behavioral ecological research on this and other small mammal species.</span></p>
Brutal outdoor concrete flower pot
Brutalist outdoor concrete flower pot - **test for a community 3D print** https://en.wikipedia.org/wiki/Brutalist_architecture @ Usti nad Labem https://goo.gl/maps/5Q1B4EUKtv2czRhT9 Legendary 1980s design by Prefa Přeštice, more info: https://www.fb.com/architektura489/posts/1214134412117289 **Free 3D printable file (edited and cleaned .STL) by @pasta3d: ** https://sketchfab.com/3d-models/brutal-outdoor-concrete-flower-pot-print-edit-1c6e58990abb488ab15cc95329b6cefa Free download, Draft capture, 16K texture. Source: Objaverse 1.0 / Sketchfab
Outdoor reception area
Quaaout Lodge, British Columbia, Canada Scan created from a 2 minute video on iPhone 12 - processed with Spectre3D Cloud Created using https://www.spectre3d.io/ Follow us on [Twitter](https://twitter.com/Spectre_3D) Source: Objaverse 1.0 / Sketchfab
Data from: Odor source distance is predictable from time-histories of odor statistics for large scale outdoor plumes
<p>Odor plumes in turbulent environments are intermittent and sparse. Lab-scaled experiments suggest that information about the source distance may be encoded in odor signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here we analyze odor signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odor signal statistics can yield useful estimates of distance. We show that achieving accurate estimates of distance requires integrating statistics from 5-10 seconds, with a high temporal encoding of the olfactory signal of at least 20 Hz. By combining distance estimates from a linear model with wind-relative motion dynamics, we achieved source distance estimates in a 60x60 m<sup>2</sup> search area with median errors of 3-8 meters, a distance at which point odor sources are often within visual range for animals such as mosquitoes.</p>
Data and R code for machine learning modelling of favourite places and routes of outdoor recreation
<p>This is a script showing the analysis used in a paper submitted for review in Landscape and Urban Planning, titled "Seeing through their eyes: Revealing recreationists’ landscape preferences through viewshed analysis and machine learning", by Carl Lehto, Marcus Hedblom, Anna Filyushkina and Thomas Ranius. </p> <p>The zip file contains an R script, data saved in .rds format and a R workspace. </p>
Utilization of outdoor spaces for specific activities (Barcelona, Rotterdam, Gothenburg): ratings and reasoning
<p>What outdoor spaces are more likely to be used for different activities?</p> <p>In this repository, we share the data we collected and analysed to explore the impact of physical characteristics of outdoor spaces on<br>the probability of utilization across diverse individuals, considering various age and gender groups.</p> <p>To collect this data we performed a crowdsourcing campaign. We recruited 409 participants from 21 European countries. To ensure a diverse range of outdoor spaces’ physical characteristics, our selection includes a range of spaces such as public squares, open marketplaces, greenspaces, pocket parks, play spaces, and streets, sourced from three European cities: Rotterdam, Barcelona, and Gothenburg. In our experiments, we presented to participants five different outdoor spaces and asked them to indicate to what degree, and why, they consider them suitable for any of the aforementioned activities.</p> <p>In total we collected data for 413 spaces representing 102 public open spaces, 107 streets, 114 greenspaces, 91 pocket parks, and 84 play spaces.</p> <p>In particular this dataset includes:</p> <ul> <li>9700 ratings of outdoor spaces likely use for different activities (Likert scale 1-5)</li> <li>6388 short explanations of these ratings</li> <li>Each space is assigned a geolocation and a gsv_id which links this location to its corresponding google street view image</li> </ul>
Outdoor NB-IoT and 5G coverage and channel information data in urban environments
<p>This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).</p> <p>Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a> </p> <p>The dataset includes a metadata file providing the following information for each campaign: </p> <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> <p>Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.</p> <p>The dataset contains the following data for NB-IoT:</p> <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> <p>The dataset contains the following data for 5G:</p> <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> <p>In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:</p> <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> <p>A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:</p> <p>L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. <span>DOI: </span><a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener"><span>10.3390/s23094266</span></a>.</p> <p>The second interpolation approach is instead introduced and described in:</p> <p>L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.</p> <p>Positioning using the 5G data was furthermore in investigated in: </p> <p>K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," <span>IEEE Communications Magazine, Volume 62, Issue 5, pp</span><span>. 44-49, May</span><span> 202</span><span>4</span><span>. DOI: </span><a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener"><span>10.1109/MCOM.011.2200707</span></a><span>.</span></p> <p><span>G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener"><span>10.1109/OJCOMS.2024.3499370</span></a>.</span></p> <p>Please refer to the above publications when using and citing the dataset. </p>
Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks
<p>We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.</p>
Data from: Low levels of outdoor recreation alter wildlife behavior
<p>Public interest in nature-based recreation is growing, including visitation to protected areas. However, the level of recreation in these areas that causes detectable changes in wildlife behavior remains unknown, and many studies that investigate wildlife responses to humans do so in high-visitation areas. </p> <p>We used camera traps to investigate the spatial and temporal responses of brown bears (<em>Ursus arctos</em>), black bears (<em>Ursus americanus</em>), moose (<em>Alces alces</em>), and wolves (<em>Canis lupis</em>) to experimentally manipulated levels of human activity in Glacier Bay National Park, Alaska during summers 2017 and 2018. Human activity was restricted at some sites and concentrated at others, and these human impact treatments were swapped mid-season. The park has very low on-land visitation (~40,000 on-land tourists per year), making it a unique study system to investigate wildlife responses to low levels of human activity. </p> <p>Detections did not exceed five per week for any species unless human activity was absent (zero photos of humans were taken). However, spatial and temporal patterns of wildlife activity in relation to human activity were nuanced and species-specific. Moose shifted their activity patterns to better align with when people were most active. Black bears were more likely to be detected in areas of high human activity but used high-use areas less intensely than low-use areas. Wolves used areas of high human impact more intensely, but shifted their activity to be more strongly nocturnal. </p> <p>Our results highlight the importance of considering both spatial and temporal responses of wildlife to human activity. Additionally, and arguably most importantly, we detected changes in wildlife behavior in response to humans in a national park with relatively low tourism. Although natural processes may dominate in protected areas, our results indicate that even low levels of human activity can alter wildlife behavior. </p> <p>Synthesis and applications: We demonstrated that nearly any level of human activity in a protected area may alter wildlife behavior. However, it is unreasonable to expect protected areas to be completely devoid of human activity. Thus, management of these areas will need to balance the desires of humans to view wildlife with the likely impacts. </p>
Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture
<p>Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. These are measurements of a module of the same type as “<a href="zenodo.org/record/2667772">Outdoor monitoring data of an Insolight B-series module - CPV sub-module</a>” however, in the previous measurements the module was mounted on a two-axis tracker to benchmark its performance, while in these measurements, the module’s integrated planar micro tracking system was used. This data was presented at IEEE PVSC 46 in June 2019 in Chicago. <strong><a href="https://zenodo.org/record/3349781">See preprint of conference article</a>.</strong></p> <p><strong>Monitoring campaign:</strong></p> <ul> <li>Location: 40.453°N, -3.727°E. <a href="https://www.google.com/maps/place/40%C2%B027'11.6%22N+3%C2%B043'37.3%22W/@40.453215,-3.7275722,142m/data=!3m2!1e3!4b1!4m13!1m6!3m5!1s0x0:0xc636231f90c3bbeb!2sInstituto+de+Energ%C3%ADa+Solar!8m2!3d40.4531766!4d-3.7269107!3m5!1s0x0:0x0!7e2!8m2!3d40.4532142!4d-3.7270248">Instituto de Energía Solar</a>, Universidad Politécnica de Madrid. 28040 Madrid, Spain.</li> <li>Fixed Mounting Angle: Due South, Slope Angle = 30°</li> <li>Starting date: 30 May 2019</li> <li>End date: 14 June 2018</li> </ul> <p><strong>Description of data file:</strong></p> <ul> <li><strong>Data files format:</strong> single comma-separated text file; headers in first row; all of the following parameters; order below is the same as order in file</li> <li><strong>Measurement time</strong>: the vector of times represents the times at which the Insolight module firmware sampled the current values of the III-V and Si outputs (measured simultaneously). <ul> <li>Date Time (dd/mmm/yyyy HH:MM:SS): time in CEST / UTC+2</li> </ul> </li> <li><strong>Measured meteorological data</strong>: these values are measured directly by the IES meteorological station with 1-minute resolution. They have been re-interpolated to match the measurement times. <ul> <li>DNI (W/m2): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley on a solar tracker. Spectral Range: 250-3000 nm. Field of view: 5°</li> <li>DNI_Top (W/m2): equivalent direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the ICU-3J35 Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 300 - 680 nm. Field of view: 5.7º</li> <li>DNI_Mid (W/m2): equivalent direct normal irradiance as measured by a middle component cell of a lattice-matched III-V triple-junction cell in the <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 680 - 900 nm. Field of view: 5.7º</li> <li>GNI (W/m2): global normal irradiance at the aperture plane as measured with a pyranometer on a solar tracker. Spectral range: 305 – 2800 nm.</li> <li>G(41°) (W/m2): Global Inclined Irradiance as measured with a pyranometer mounted facing due south and at a slope angle of 41° (near to local latitude). Spectral range: 305 – 2800 nm.</li> <li>T_Amb (°C): ambient temperature </li> <li>Wind Speed (m/s): wind speed</li> <li>Wind Dir. (m/s): wind direction</li> </ul> </li> <li><strong>Processed meteorological data</strong>: these values are calculated from the above meteorological data and provided for convenience <ul> <li>DII (W/m2): Direct Inclined (plane of array) Irradiance corresponding to the module slope angle has been calculated using the sun’s known declination and hour angle from the time.</li> <li>GII (W/m2): The Global Inclined (plane of array) Irradiance is calculated by first calculating the DII(41°), that is the DII corresponding to the G(41°) measurement, and finding the Diffuse Inclined Irradiance Diff(41°) = G(41°) – DII(41°). It is assumed that the Diffuse Inclined Irradiance at 41° and 30° is equal, so GII = DII + Diff(41°).</li> <li>SMR_Top_Mid (n.d.): “Spectral Matching Ratio”. This is the ratio between DNI_Top and DNI_Mid. A value of unity indicates a spectrum that is equivalent to AM1.5D with regards to the energy balance between top and middle subcells.</li> </ul> </li> <li><strong>Measured module data:</strong> The module was placed in a short-ciruit condition and allowed to track using its integrated tracking system. The short circuit current was measured using shut resistors and integrated A/D channels. This hybrid module features both III-V micro cells (under concentration, with planar microtracking) and large area silicon solar cells (for diffuse capture). <ul> <li>ISC_measured_IIIV (A):</li> <li>ISC_measured_Si (A)</li> </ul> </li> <li><strong>Estimated module data:</strong> As is explained in the IEEE PVSC 46 manuscript (<a href="https://zenodo.org/record/3349781">see Preprint</a>) the following values are estimated using the previously listed measured data. <ul> <li>T_Backplane (°C)</li> <li>PMP_estimated_IIIV (W)</li> <li>PMP_estimated_Si (W)</li> </ul> </li> </ul>
Resources for "BMF CP 93: Characteristics of people likely to reduce outdoor activities due to wildfire smoke"
<p>The current study is conducted to examine the following research questions:</p> <ul> <li>Who were people more likely to reduce outdoor activities during the smoke event in the summer of 2018 in the Boise Metropolitan Area in Idaho?</li> <li>Who were people having more days with reduced outdoor activities during the smoke event in the summer of 2018 in the Boise Metropolitan Area in Idaho?</li> </ul>
Outdoor_DEEC_withRamp_Segmentation
<p>Shown here is a bag with 148 seconds of recording outside the DEEC of the University of Coimbra (UC), where the robot has the opportunity to climb a small ramp. The bag contains data captured by an RGB-D camera (D435i), a stereo camera (Mynt Eye S1030), wheel odometry (given by the Pioner P3-DX), and a 2D LiDAR (Hokuyo URG-04L). The IMU data from both cameras was also recorded. Alongside this bag is another containing the semantic segmentation generated by the PSPNet network, from the Cityscapes dataset. The alpha has a opacity of 255.</p> <p>The transformations are defined as follows (approximately):</p> <p> RGB-D camera: 0.18 0.005 0.71 0 -0.0872 0 base_link realsense_link<br> Mynt Eye camera: 0.205 0.0 0.63 -1.57 0 -1.72 base_link mynteye_link<br><br>It should be noted that the laser transformations have already been recorded on the bag. The segmented images are in BGR format. <br>The ground truth (GT) was generated using RTAB-Map, combining odometry data from various sensors in a fusion module. While it is considered highly accurate, it may still contain some associated errors.</p>
Outdoor Navigation Dataset
<p>Dataset for outdoor navigation, containing labelled 1251 640x640 images in an urban setup, with the following labels: 'Crosswalk', 'Pedestrian', 'Ramp', 'Road', 'Sidewalk'.</p> <p>The format for the annotations is YOLO format for segmentation.</p>
Highlighting altruism in geoscience careers aligns with diverse US student ideals better than emphasizing working outdoors
<p>We surveyed students enrolled in College Algebra at a large, urban, Hispanic-serving, R-1 public university in the southwestern United States. The data span five semesters, from Fall 2018 through Spring 2020. The survey was composed of demographic questions as well as questions using the Likert scale, in which students rated how much they agreed or disagreed with a particular statement regarding descriptions of their “ideal career.” Students were also asked to rate statements about careers in different science fields and engineering. Demographic questions were included at the end of the survey in order to mitigate stereotype threat.</p> <p>Our study tests (i) whether altruistic factors, personal achievement, or work environment are most important to college students (early in their undergraduate program) for their future careers, (ii) whether the ratings of these ideals differ between male/female, URM/non-URM, and first-generation/non-first-generation college students, and (iii) how student perceptions of the geosciences are in regard to those ideals compared to other STEM fields. </p>
Building Object and Outdoor Scene Segmentation (BOOSS) - Multi-channel (RGB + Thermal) Aerial Imagery Datasets
<p>The dataset of <em>Building Object and Outdoor Scene Segmentation (BOOSS)</em> is based on multi-channel aerial imagery data. It covers </p> <p>- Ground Truth</p> <p>- RGB</p> <p>- Thermal</p> <p>The annotations in version 1.0 include roofs, facades, cars, roof equipment, and ground equipment</p> <p>Please cite as:</p> <p>Hou, Yu, Meida Chen, Rebekka Volk, and Lucio Soibelman. "An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets." <em>Remote Sensing</em> 13, no. 21 (2021): 4357.</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.