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Table 1 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 1</b> <i>Anopheles</i> species composition in surveyed areas before and after IRS</p><table><tbody><tr><th>Species</th><th>Before IRS (May 2016–April 2017)</th><th>After 1st round of IRS (June 2017–March 2018)</th><th>Control (Bembereke, Kouande)</th><th>After 2nd round of IRS (June 2018–November 2018)</th><th>Control (Bembereke– Kouande)</th><th>Total</th></tr></tbody><tbody><tr><th><i>An.gambiae</i> (<i>s.l</i>.)</th><td>2465</td><td>2379</td><td>1546</td><td>1286</td><td>929</td><td>8605</td></tr><tr><th><i>An.funestus</i></th><td>54</td><td>54</td><td>16</td><td>9</td><td>7</td><td>140</td></tr><tr><th><i>An.coustani</i></th><td>9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9</td></tr><tr><th><i>An.pharoensis</i></th><td>3</td><td>2</td><td>5</td><td>2</td><td>3</td><td>15</td></tr><tr><th><i>An.paludis</i></th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th><i>An.nili</i></th><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td></tr><tr><th><i>An.ziemanni</i></th><td>2</td><td>2</td><td>0</td><td>1</td><td>0</td><td>5</td></tr><tr><th>Total</th><td>2534</td><td>2437</td><td>1567</td><td>1299</td><td>939</td><td>8776</td></tr></tbody></table>
Table 3 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 3</b> Parity rates of <i>Anopheles gambiae</i> (<i>s.l</i>.) collected before and after the first round IRS</p><table><tbody><tr><th>IRS area</th><th>Variable</th><th>Before IRS (June–October 2016)</th><th>After 1st round of IRS (June– October 2017)</th><th><i>Χ</i> 2-value</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Alibori-Donga</th><td>No. dissected</td><td>1422</td><td>1214</td><td>–</td><td>–</td></tr><tr><th></th><td>No. parous</td><td>996</td><td>457</td><td>–</td><td>–</td></tr><tr><th></th><td>Parous (%)</td><td>70.04</td><td>37.64</td><td>276.57</td><td><0.0001</td></tr></tbody></table>
Table 2 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 2</b> Frequency of sibling species of the <i>An. gambiae</i> (<i>s.l.</i>) complex in IRS and control areas</p><table><tbody><tr><th>Period</th><th>No. of</th><th>Alibori (IRS Area 1)</th><th></th><th>Donga (IRS Area 2)</th><th></th><th>Total (IRS Areas)</th><th></th><th>Bembereke (Control area)</th></tr></tbody><tbody><tr><th></th><td>analysed samples in IRS areas</td><td><i>An. gambiae</i></td><td><i>An.coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An.arabiensis</i></td></tr><tr><th>Before IRS (May 2016–October 2016)</th><td>980</td><td>170</td><td>271</td><td>0</td><td>387</td><td>152</td><td>0</td><td>557</td><td>423</td><td>0</td><td>–</td><td>–</td><td>–</td></tr><tr><th>After IRS (June 2017–November 2018)</th><td>1794</td><td>466</td><td>176</td><td>17</td><td>520</td><td>147</td><td>5</td><td>986</td><td>323</td><td>22</td><td>277</td><td>181</td><td>5</td></tr><tr><th>Proportion (%)</th><td>–</td><td>70.71</td><td>26.70</td><td>2.57</td><td>77.38</td><td>21.87</td><td>0.74</td><td>74.07</td><td>24.26</td><td>1.65</td><td>59.82</td><td>39.09</td><td>1.07</td></tr><tr><th>Total (Before+after IRS)</th><td>2774</td><td>636</td><td>447</td><td>17</td><td>907</td><td>299</td><td>5</td><td>1543</td><td>746</td><td>22</td><td>277</td><td>181</td><td>5</td></tr><tr><th>Proportion (%)</th><td>–</td><td>57.81</td><td>40.63</td><td>1.54</td><td>74.89</td><td>24.69</td><td>0.41</td><td>66.76</td><td>32.28</td><td>0.95</td><td>59.82</td><td>39.09 1.07</td></tr></tbody></table>
Table 8 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 8</b> Blood-feeding rates in <i>An. gambiae</i> (<i>s.l.</i>) in treated and control areas</p><table><tbody><tr><th>Period</th><th>Area</th><th>No. collected</th><th>Unfed</th><th>Fed</th><th>Gravid</th><th>Half-gravid</th><th>Bloodfeeding rate %</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Before IRS:June–October 2016</th><td>IRS areas</td><td>1001</td><td>26</td><td>922</td><td>12</td><td>41</td><td>96.20</td><td><0.0001</td></tr><tr><th>After 1st round of IRS:June–October 2017</th><td></td><td>273</td><td>99</td><td>171</td><td>1</td><td>2</td><td>63.37</td><td></td></tr><tr><th>After 1st round of IRS:June–September 2017</th><td>IRS areas</td><td>208</td><td>78</td><td>129</td><td>0</td><td>1</td><td>62.5</td><td><0.0001</td></tr><tr><th></th><td>Control areas</td><td>244</td><td>16</td><td>187</td><td>31</td><td>10</td><td>80.74</td><td></td></tr></tbody></table>
Table 6 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 6</b> Human-biting rate, sporozoite index and entomological inoculation rate in IRS and control areas</p><table><tbody><tr><th>Variable</th><th>After 1st IRS campaign (June–September 2017)</th><th>After 2nd IRS campaign (June–August 2018)</th></tr></tbody><tbody><tr><th></th><td>IRS areas</td><td>Control areas</td><td>Reduction (%)</td><td>IRS areas</td><td>Control areas</td><td>Reduction (%)</td></tr><tr><th></th><td>(Alibori–Donga)</td><td>(Bembereke–Kouande)</td><td>(Alibori–Donga)</td><td>(Bembereke–Kouande)</td></tr><tr><th>HBR/night</th><td>7.56</td><td>10.81</td><td>–</td><td>7.76</td><td>11.01</td><td>–</td></tr><tr><th>SI (%)</th><td>0.71</td><td>3.7</td><td>80.81</td><td>0.6</td><td>3</td><td>80</td></tr><tr><th>EIR (ib/person/night)</th><td>0.053</td><td>0.403</td><td>–</td><td>0.05</td><td>0.325</td><td>–</td></tr><tr><th>EIR (ib/person/month)</th><td>1.6</td><td>12.11</td><td>86.78</td><td>1.5</td><td>9.75</td><td>84.61</td></tr></tbody></table><p><i>AbbreviatioNS</i>: SI,sporozoite index;HBR,human-biting rate;EIR,entomological inoculation rate;ib,infective bite</p>
Table 5 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 5</b> Human-biting rate, sporozoite index and entomological inoculation rate in Alibori and Donga regions (IRS areas) before and after the first round of IRS 2017</p><table><tbody><tr><th>Region</th><th>Variable</th><th>Before IRS (June–October 2016)</th><th>After 1st round of IRS (June– October 2017)</th><th>Reduction (%)</th></tr></tbody><tbody><tr><th>IRS areas (Alibori, Donga)</th><td>HBR/night</td><td>8.43</td><td>7.37</td><td>–</td></tr><tr><th></th><td>SI (%)</td><td>8.4</td><td>1.2</td><td>85.71</td></tr><tr><th></th><td>EIR (ib/person/night)</td><td>0.707</td><td>0.09</td><td>–</td></tr><tr><th></th><td>EIR (ib/person/month)</td><td>21.21</td><td>2.7</td><td>87.27</td></tr></tbody></table><p><i>AbbreviatioNS</i>: SI,sporozoite index;HBR,human-biting rate;EIR,entomological inoculating rate;ib,infected bite</p>
Hacettepe University Event (HUE) Dataset - Indoor - Part 2
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - Indoor - Part 1
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - Indoor - Part 3
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Indoor lighting design for healthier workplaces natural and electric light assessment for suitable circadian stimulus
<p>Dataset of "Indoor lighting design for healthier workplaces natural and electric light assessment for suitable circadian stimulus" research</p>
AALTO - Channel Characterization at Sub-THz Band with Measurements and Ray Tracing in Indoor Case - DATA
<p>The data set includes simulation results from radio propagation modelling of TERAWAY links (at 90, 95 and 100 GHz) in realistic university corridor environment. The modelling is performed using a Ray Tracing Tool developed in MATLAB environment at Aalto University. Ray tracing technique used in this tool is based on Image Theory (IT) algorithm. Unlike a quasi three-dimensional environment, it supports ray tracing in full three dimension.</p> <p>This data set contains propagation modelling results of the TERAWAY link. Output data includes (but is not limited to): Multipath component IDs, Path Distance (meter), Angle of Arrival AoA (degree), Angle of Departure AoD (degree), Direction of Arrival DoA (degree), Direction of Departure DoD (degree), E-Field (Volt/meter), H-Field (Ampere/meter), Phase (Radians), Power (Watts), Number of reflections a path experienced, Number of diffractions a path experienced, information that is it ground reflected path or not, Receiver location (x and y coordinates), information that is it rooftop path or not.</p>
Data for: The indoor mycobiomes of daycare centers are affected by occupancy and climate
<p>With an alarming increase in chronic diseases like childhood asthma and allergies, there is an increased focus on the exposure of young children to indoor biological and chemical air pollutants. Our study of 125 daycares throughout Norway demonstrates that the indoor mycobiome not only reflects cooccurring outdoor fungi but also includes a high abundance of yeast and mold fungi with an affinity for indoor environments.</p>
Indoor point cloud dataset for BIM related applications
<p>Annotated point cloud of the CRAS labs@FEUP. The point cloud is in ASCII format. Variables: Point X coordinate (m); Point Y coordinate (m); Point Z coordinate (m); Point colour (R); Point colour (G); Point colour (B); Intensity; Label. A total of 21 scans were made, producing 584,701,977 points. The point clouds from the 21 scans were registered with Leica Cyclone Register 360 (3 mm average error). The points were labeled according to 33 classes: 0-unassigned; 1-ceiling; 2-floor; 3-wall; 4-door; 5-window; 6-desk; 7-chair; 8-cabinet; 9-mobile cabinet; 10-shelf; 11-vents; 12-water tank; 13-bin; 14-box; 15-board; 16-computer; 17-screen; 18-printer; 19-vest; 20-switch; 21-paper dispenser; 22-alcohol dispenser; 23-cable; 24-phone; 25-robot; 26-water kettle; 27-stairs; 28-ladder; 29-oil heater; 30-divider; 31-hanger; 32-fan; 33-water dispenser.</p> <p>Additionally, the as-built IFC model of the space is provided in order to test Scan-to-BIM and Scan-vs-BIM algorithms.</p>
Sensed Data of Indoor Monitoring Framework in Aedes Architecture Forum Exhibition called Living Prototypes
<p>The presented Dataset is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The collected data is part of Indoor Monitoring Framework sensed data (temperature and humidity) stored in InfluxDB in CSV format for Living Prototype Exhibition at Aedes Forum in Berlin for a period of 35 days. The dataset can be used in InfluxDB and Grafana for visualization. </p>
Timelapse of Indoor Monitoring of 3D printed Panels installed in Living Prototypes Exhibition in Aedes Forum, Berlin
<p>The presented timelapse is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The timelapse is part of Indoor Monitoring Framework developed for Living Prototype Exhibition at Aedes Forum in Berlin. </p>
Performance Analysis of LoRa in Indoor Settings: A Data Descriptor
<p>This work is a description of the experiment conducted to understand the reception<br> of LoRa in closed environments, such as a building.<br> The experiment was carried out on 04/05/2023, in the NW1 building of University of<br> Bremen. The data’s primary goal is to provide researchers with the understanding of<br> factors such as distance, obstacles, interference with other wireless devices that<br> dictates LoRa’s performance.</p>
Indoor Location Competition 2.0 Dataset
<p>This is the dataset of our Mobicom 2023 paper titled "The Wisdom of 1,170 Teams: Lessons and Experiences from a Large Indoor Localization Competition". We organized an indoor location competition in 2021. 1446 contestants from more than 60 countries making up 1170 teams participated in this unique global event. In this competition, a first-of-its-kind large-scale indoor location benchmark dataset (60 GB) was released. The dataset for this competition consists of dense indoor signatures of WiFi, geomagnetic field, iBeacons etc. as well as ground truth locations collected from hundreds of buildings in Chinese cities. Here we upload a sample data to Zenodo, and the whole dataset can be found at https://www.kaggle.com/c/indoor-location-navigation.</p>
Indoor Air Temperature and Occupant Behavior in Classroom of higher education building in Mediterranean climate
<p>Data collection Include the measurement of indoor and outdoor environmental parameters (air temperature and relative humidity) and occupant interactions with building systems (window and door status: open/closed, blind state, and thermostat/air-conditioning adjustment).</p><p>The outdoor air temperature, relative humidity, and wind speed were collected as potential control variables to indicate different outdoor conditions.</p><p>The indoor air temperature and relative humidity in the classroom were monitored using wireless sensors. Six RHT sensors were placed at different locations: one in the center (F98), two on the ceiling next to grilles (FA1 et F9B), one on the carpentry of one of the windows (F9D), one near the writing board (F94), and one in the corridor outside the classroom (F96). Indoor parameters were recorded at ten minutes intervals.</p><p>The number of occupants was determined hourly (morning and afternoon) by counting and surveying (attendance sheets). The usage schedules of the classroom were 8:30–18:00. The number of occupants varied from 0 to 31.</p><p>The states of doors and windows (open or close) were monitored using magnetic sensor that detects the opening of doors and windows. The states of the door and windows were recorded at ten minute intervals.</p><p>The window-blind closing rate was determined by visual observation. The closing rates were 0%, 25%, 50%, 75%, and 100%. Observations were conducted throughout the day in the morning and afternoon at 1 h intervals.</p><p>The state of the heating/air-conditioning system was determined to be off or on every hour (in the morning and afternoon) using the HVAC control panel (HMI)</p>
Dataset for Vehicle Indoor Positioning in Industrial Environments with Wi-Fi, inertial, and odometry data
<p>Dataset collected in an indoor industrial environment using a mobile unit (manually pushed trolley) that resembles an industrial vehicle equipped with several sensors, namely, Wi-Fi, wheel encoder (displacement), and Inertial Measurement Unit (IMU).</p> <p>Sensors were connected to a Raspberry Pi (RPi 3B +), which collected the data from the sensors. Ground truth information was obtained with video camera pointed towards the floor, registering the times when the trolley passed by reference tags.</p> <p>List of sensors:</p> <ul> <li>4x <strong>Wi-Fi interfaces</strong>: Edimax EW7811-Un</li> <li>2x <strong>IMUs</strong>: Adafruit BNO055</li> <li>1x <strong>Absolute Encoder</strong>: US Digital A2 (attached to a wheel with a diameter of 125 mm)</li> </ul> <p>This dataset includes:</p> <ul> <li>1x <strong>Wi-Fi radio map</strong> that can be used for Wi-Fi fingerprinting.</li> <li>6x <strong>Trajectories</strong>: including sensor data + ground truth.</li> <li><strong>APs Information</strong>: list of APs in the building, including their position and transmission channel.</li> <li><strong>Floor plan:</strong> image of the building's floor plan with obstacles and non-navigable areas.</li> <li><strong>Python package</strong> provided for: <ul> <li>parsing the dataset into a data structure (Pandas dataframes).</li> <li>performing statistical analysis on the data (number of samples, time difference between consecutive samples, etc.).</li> <li>computing Dead Reckoning trajectory from a provided initial position.</li> <li>computing Wi-Fi fingerprinting position estimates.</li> <li>determining positioning error in Dead Reckoning and Wi-Fi fingerprinting.</li> <li>generating plots including the floor plan of the building, dead reckoning trajectories, and CDFs.</li> </ul> </li> </ul> <p> </p> <p>When using this dataset, please cite its data description paper:</p> <p>Silva , I.; Pendão, C.; Torres-Sospedra, J.; Moreira, A. Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data. <em>Data</em> <strong>2023</strong>, <em>8</em>, 157. <a href="https://doi.org/10.3390/data8100157" target="_blank" rel="noopener">https://doi.org/10.3390/data8100157</a> </p> <p> </p>
Behavioral Intervention in Reducing Indoor Tanning
ClinicalTrials.gov study NCT03448224. IPD Sharing: NO. Countries: 1. Publications: 1.
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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.