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517 results for “October”
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
LPL5 - October 2023
<p>Measurements of LPL5 (Tejeda) in October 2023</p> <p>Gran Canaria</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL5">info</a></p>
LPL1 - October 2023
<p>Measurements of LPL1 (Teide NP) in October 2023</p> <p>Tenerife</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL1">info</a></p>
LPL4 - October 2023
<p>Measurements of LPL4 (Ilha do Corvo) in October 2023</p> <p>Corvo</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL4">info</a></p>
LPL12 - October 2023
<p>Measurements of LPL12 (Ilhas Desertas) in October 2023</p> <p>Madeira</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL12">info</a></p>
LPL3 - October 2023
<p>Measurements of LPL3 (Madeira East Coast) in October 2023</p> <p>Madeira</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL3">info</a></p>
LPL8 - October 2023
<p>Measurements of LPL8 (Timanfaya NP) in October 2023</p> <p>Lanzarote</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL8">info</a></p>
SG - October 2023
<p>Measurements of SG (Global) in October 2023</p> <p></p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/SG">info</a></p>
Daily climate data for Pousada Aguape, MS, Brazil, November 2021-October 2022
<p class="MsoNormal"><span>The extent to which animals may compensate for rising temperatures through behavioral thermoregulation is an important unknown in predicting responses to climate change. Flexibility in temporal activity in particular may be an important factor in determining species' vulnerability. In this study, we examine whether the white-lipped peccary, a predominantly diurnal Neotropical mammal, shifts its temporal activity and microclimate selection in response to daily temperature variation in the Pantanal biome of Brazil. From November 2021 to October 2022, we deployed camera traps and microclimate data loggers to record white-lipped peccary activity and air temperature on a fine spatiotemporal scale. We found that the percentage of peccary activity that is nocturnal increased with maximum daily temperature at a rate of 3.7% per 1°C past a threshold of 31.2°C, and increased with maximum daily wet-bulb temperature at a rate of 4.7% per 1°C past a threshold of 24.9°C. The afternoon period (i.e. solar noon to sunset) went from having the highest frequency of peccary activity at the lower end of our observed temperature range to having the lowest frequency of activity at the higher end. We did not find evidence of microclimate selection. Our findings indicate that white-lipped peccaries in the Pantanal demonstrate substantial behavioral flexibility in their response to high temperatures, which may help to buffer them against the impact of rising temperatures caused by climate change.</span></p> <p class="MsoNormal"><span>This dataset includes all data from the microclimate date loggers. Camera trap records can be accessed through Wildlife Insights.</span></p>
Pathogen Transmission Ontology: October 2022 Release
<p>This release includes a minor update identifying the ontology root term. The 32 pathogen transmission terms, all with definitions, remain unchanged.</p>
Topic Model October 2023 (200 texts, 40 topics)
<p>Topic Model of the MiMoText roman18 corpus (Oct 2023)</p>
FloodNet flood sensor data - October 2020 to October 2023
<p>Before accessing our data, please review our <a href="https://docs.google.com/document/d/1jd5Q2UYj_0PwMRplFISmhT6LswpS08D9/edit">Data Access License Agreement</a>, which outlines the terms and conditions associated with the use of FloodNet data.</p><p>We also encourage you to thoroughly review this description, particularly the section regarding noise in sensor data. Floods are not the only events that show up in this dataset, and we want to reduce misinterpretation of data as much as possible. If you have questions about the dataset, please contact <a href="mailto:info@floodnet.nyc">info@floodnet.nyc</a>.</p><h2><strong>Data Summary</strong></h2><p>The contents of the CSVs are as follows:</p><p><strong>deployment_id - </strong>This is the unique identifier assigned to the sensor deployment</p><p><strong>time - </strong>The ISO-8601 date-time format in <strong>UTC</strong>. e.g. <strong>2023-02-22T14:20:30.000Z</strong></p><p><strong>depth_proc_mm - </strong>Time-series filtered depth in mm, sampled every ~1min using a series of custom noise filters designed to remove common manifestations of sensor noise. <strong>This is the recommended depth field to use.</strong></p><p><strong>depth_filt_mm - </strong>Time-series raw depth in mm, sampled every ~1min, except that values below 10mm are set to zero. This data has more noise, but you can use this to verify that depth_proc_mm did not accidentally filter a flood.</p><p><strong>depth_raw_mm - </strong>Time-series raw depth in mm, sampled every ~1min.</p><h3><strong>How do the sensors capture data?</strong></h3><p>The FloodNet sensors <strong>measure distance</strong> at a regular interval, currently <strong>once every 60 seconds</strong>. Certain older sensors were programmed to upload once every 5 minutes, so don't be alarmed if you see sensors with different upload rates.</p><p>The sensors work using ultrasound. They send out sound waves at a frequency outside the range of human hearing and then capture the echo when it bounces back off the closest reflective surface. That means that what the sensor is ultimately measuring is a difference in time. The sensor then uses the travel time to calculate the distance to that reflective surface (hopefully the ground!).</p><p>Sometimes the sensor will send out a ping, but will never receive an echo back. In these cases, the measurement is invalid and we record a null value. <strong>If you see a null measurement, this is likely the cause</strong>.</p><p>Distance is calculated using d=v∆t2 where d is the distance to the ground, v is the speed of sound, and ∆t/2 is the time that it takes for the sound to bounce back and return to the sensor (divided by two because it's traveling twice the distance we're measuring).</p><p>If you know a bit of physics, you might be saying "Wait a second, but the speed of sound isn't constant! It varies depending on the properties of the medium that it's traveling through!" Good catch! The speed of sound depends on a few factors including the molecular composition and density of the medium, in this case, "air". Assuming dry air (low humidity), the speed of sound, in units of m/s, is represented as v=20.05TC where TC is the ambient temperature in Celsius. The FloodNet sensors have an internal temperature sensor that it uses to do these calculations.</p><p>In order to calculate flood depth, the measured distance between the sensor and the ground below must be known under non-flooded conditions. This value is calculated through a dynamic calibration procedure that occurs at 5 AM daily, in which the distance measurements z collected over the previous three nights (between 10 PM and 5 AM are analyzed to determine the median value (z_nighttime~median). Day-time measurements are excluded from the calibration because of their temperature-related variance caused by direct sunlight producing erroneously high measurements on the sensor's internal temperature sensors. If the standard deviation of the night-time distance measurements exceeds 5 mm signifying either a flood or erroneous high variance), the previous day's z_nighttime~median calculation is used. To calculate flood depth D_t at time point t, the sensor's distance measurement at that time z_t) is subtracted from z_nighttime~median: Dt = z_nighttime~median - z_t</p><p>This dynamic calibration approach allows data collection to adapt to changes in sensor height, caused for example by a shift in signpost position if there is a vehicle impact, or seasonal variation in baseline z_nighttime~median readings.</p><h3><strong>Why is there noise in the data?</strong></h3><p>It may have occurred to you "How do you know that you're measuring the ground, and not a pigeon?" Well, the short answer is, we don't! That's why you may see some values that are not zero but are not actually floods.</p><p>The issue of noise is a challenge that our team is focused on and is actively researching. We have developed some methods designed to remove common manifestations of noise. The noise categories we have characterized are:</p><p><strong>Blips:</strong> a momentary jump in the data, where it returns to the previous value a sample or two later. This is often caused by someone or something passing under the sensor while it is taking its measurement.</p><p><strong>Boxes:</strong> a sudden and persistent jump in depth. This is commonly because something is placed beneath the sensor, such as garbage bags, bicycles, or loose trash.</p><p><strong>Pulse Chain:</strong> a chaotic chain of pulse/box-like noise that can occur for an extended period of time. This can be caused by aberrant reflections on uneven, sloped, or complex surfaces such as the spokes on a bike wheel. </p><p>The FloodNet data analysis pipeline uses a series of custom filters to address and filter some of this noise, and includes blip filters, detecting momentary jumps in the data, and box filters, detecting sustained jumps to a higher, near-constant value. These filters were optimized to minimize the risk of distorting the data and removing floods. We also employ a gradient filter that looks at the rate of change in depth to determine if the change observed is physically plausible. The gradient threshold used is 10 inches per minute, which is 7 times the maximum rate observed in Hurricane Ida in late summer of 2021.</p><h3><strong>Why isn't the sensor showing a flood?</strong></h3><p>As ultrasonic sensors need to be perpendicular and directly above the location that they are measuring, they are highly dependent on available mounting locations. To deploy these sensors, we are using existing street infrastructure such as signposts. This largely limits the locations on a street that we can use to install sensors, meaning that the sensors are not always located at the lowest point on the street, where floods would first develop. </p><p>Streets have variable topography and therefore the depth measurements our sensors capture will not always reflect the depth at every part of the street. For example, if our sensor is 4 inches higher than the lowest point in the area, our sensor would be reading zero for any flooding below 4 inches, and a reading of 10 inches actually corresponds to 14 inches of depth at the lowest point. That offset calculation requires detailed elevation maps and is information that our data scientists are working to compile for any of our sensor deployments, but is not yet available.</p>
Number of individuals of each species monthly (from October, 2022 to October, 2023) collected at a shrubby Caatinga area, municipality of São José do Piauí, Piauí state, Brazil.
<p>Dataset included in the manuscript "The orchid bee fauna (Hymenoptera, Apidae, Euglossina) from the Capadócia Nordestina, Piauí State, Brazil", submitted to Biota Neotropica. </p>
Radiation Impact of the Halloween GLE Events During the October to November 2003 Period - Data
<p>Computed data that was used within the "Radiation Impact of the Halloween GLE Events During the October to November 2003 Period" paper. The repository has data for GLEs 65-67 and contains the planetary cut-off maps, SEP spectra information, Forbush reconstruction information, interpolated CRAC:DOMO effective dose yield functions, and planetary dose computations. Some animated gifs are included to simply convey the change in dose over each GLE. </p>
Meteorological Data from Kardamyla, Chios: October 2024 Baseline Measurements for the MUSICA Project
<p>October 2024 reflected the gradual shift to autumn conditions in Kardamyla. With cooler temperatures, no rainfall, and steady winds predominantly from the north, the month was characterized by stable weather patterns. Below is an analysis of the observed data for October, shedding light on the climatic trends of the region.</p> <h4><strong>Temperature:</strong></h4> <ul> <li>Average temperature: <strong>19.9°C</strong>.</li> <li>Highest temperature: <strong>30.1°C</strong> on October 6.</li> <li>Lowest temperature: <strong>9.2°C</strong> on October 27 and 28.</li> <li>Days with temperatures ≥ 32°C: <strong>0 days</strong>.</li> </ul> <h4><strong>Rainfall:</strong></h4> <ul> <li>Total rainfall: <strong>0.0 mm</strong>.</li> <li>Days with rainfall: <strong>None</strong>.</li> </ul> <h4><strong>Wind:</strong></h4> <ul> <li>Average wind speed: <strong>8.6 km/h</strong>.</li> <li>Maximum wind speed: <strong>53.1 km/h</strong> on October 17.</li> <li>Dominant wind direction: <strong>North (N)</strong>.</li> </ul> <h4><strong>Conclusion:</strong></h4> <p>October marked the end of the dry season, with mild to cool temperatures and no rainfall. The north winds remained dominant, often reaching significant speeds, reflecting the stable yet shifting weather patterns typical for autumn in Kardamyla.</p>
ASIT Wind and Waves 16-17 October 2019
<p>Measurements made from the Air-Sea Interaction Tower south of Martha's Vineyard on 16-17 October 2019.</p> <p>Waves were measured by Lidar at 10 Hz.</p> <p>Wind speed at 10 m height is a 20 min average.</p>
Dataset: HowFaster.NET - Global Ranks - Edition 2 - October 2020 (full month)
<p>A distinction is made between the level of abstraction for:</p> <p>- Autonomous systems;<br> - Continents;<br> - Countries;<br> - Regions;<br> - Cities;<br> - ZIP codes;<br> - Time zones;</p> <p>Data is provided in CSV and JSON formats.</p> <p>Filename format.</p> <p>edition.[value-edition].[value-label].[value-type].[value-format]</p> <p>possible [value-edition] content:<br> - integer from 1..</p> <p>possible [value-label] content:<br> - as;<br> - continent;<br> - countrycode;<br> - regionname;<br> - cityname;<br> - zipcode;<br> - timezone;</p> <p>possible [value-type] content:<br> - global_ranks;<br> - local_ranks;</p> <p>possible [value-format] content:<br> - csv;<br> - json;</p> <p>Example names:</p> <p>- edition.1.as.global_ranks.csv;<br> - edition.1.as.global_ranks.json;</p>
LPL5 - October 2021
<p>Measurements of LPL5 (Tejeda) in October 2021</p> <p>Tejeda, Gran Canaria</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL5">info</a></p>
LPL4 - October 2021
<p>Measurements of LPL4 (Isla de Corvo) in October 2021</p> <p>Azores</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL4">info</a></p>
LPL1 - October 2021
<p>Measurements of LPL1 (Güímar) in October 2021</p> <p>Tenerife</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL1">info</a></p>
ScienceDex guides
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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.