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965 results for “mounting”
Lake Sediment Pollen from Guilder Pond in Mount Washington MA from 13882 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen records from New England to explore how postglacial changes in the composition and spatial patterns of vegetation were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C years before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term trends and abrupt shifts in temperature and precipitation, as well as b
High-frequency, hourly, and daily measurements from Mount Fleming Meteorological Station (FLMM), McMurdo Dry Valleys, Antarctica (2011-2025, ongoing)
As part of the McMurdo Dry Valleys Long-Term Ecological Research program, a spatially distributed, long-term climate monitoring network was established across the McMurdo Dry Valleys region of Antarctica, consisting of fourteen research-grade weather stations that continuously measure a standard suite of environmental parameters. Ecosystem processes in this region are strongly regulated by climatic drivers that exhibit high variability across both time and space, making accurate measurement of environmental variables at high temporal and spatial resolution essential to understanding the biophysical dynamics of this polar desert ecosystem. This data package includes measurements from the Mount Fleming Meteorological Station (FLMM), which was established in 2011 on top of Mount Fleming in Wright Valley, McMurdo Dry Valleys, Antarctica. Parameters include air temperature, relative humidity, barometric pressure, and wind speed and direction. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. Since there is no universally accepted standard for handling gaps in time-series data, users are encouraged to work with the high-frequency data and establish their own criteria for acceptable data completeness to minimize any potential bias.
Split-beam echosounder data from keel-mounted EK60 during PolarFront 2023-08 cruise
<p><strong>PolarFront 2023-08 EK60</strong></p><p>The keel-mounted Simrad EK60® split-beam echosounder continuously recorded hydroacoustic data at 18, 38, and 120 kHz. The ping rate was set to 1.5 seconds and pulse length to 1,024 µs. </p>
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
<p>Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Porté-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>
Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars
<p>Dataset of the paper " Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1]. " published in Wind Energy Science [1].</p> <p>[1] Brugger, P., Markfort, C., and Porté-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, 2022.</p>
MCR LTER: Coral Reef: Sensor Network: Bottom-mounted CTD Data - GUMPR, 2006-2012
Physical oceanographic data from bottom-mounted instrumentation (Seabird 16+ CTD) were sampled year-round on Gump reef in Cooks Bay on Moorea, French Polynesia (GUMPR site). Sampling began in 2006 until early 2012. The CTD measured conductivity, temperature, pressure, from which density and salinity were calculated. Data were collected every 5 minutes, processed and reported every 20 minutes. The instrument is mounted 2 m above the bottom in 6 m of depth. These data streamed near real-time as part of the Digital Moorea project (no longer active.) Daily, weekly, monthly and yearly means were calculated for temperature, salinity, and density. This is a completed timeseries which ended early 2012. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
NYU FloodSense street sign mounted distance sensor
<p>Ultrasonic distance data in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Data is collected at ~5min intervals. Time fields are in local time (New York).</p> <p>Two types of erroneous data has been observed:</p> <ul> <li>Large spikes in distance that always manifest at 5000mm - can be excluded</li> <li>There are ~1% rises in distance measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is prelimary and is for prototyping purposes. Not to be used as a reliable data source as it is.</p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p>
NYU FloodSense street sign mounted flood depth sensor
<p>Water depth level in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. Ultrasonic technology is used to detect flood water depth.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Depth data is collected at ~5min intervals. Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>Erroneous data has been observed:</p> <ul> <li>There are ~1% decreases in depth measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is preliminary and is for prototyping purposes. </p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p>
NYU FloodSense Gowanus canal mounted sensor depth
<p>Water depth level in mm from a sensor mounted mounted above the Gowanus Canal, Brooklyn, NY (40.674490, -73.994458).</p> <p>The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. This one is used for data validation.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Data is collected at ~5min intervals. Time fields are in local time (New York). Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>One type of erroneous data has been observed:</p> <ul> <li>There are ~1% rises in distance measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is prelimary and is for prototyping purposes. Not to be used as a reliable data source as it is.</p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p> <p> </p>
A simple, static and stage mounted direct electron detector based electron backscatter diffraction system
<h3><strong>Data set for </strong><i><strong>A simple, static and stage mounted direct electron detector based electron backscatter diffraction system</strong></i></h3><p>T.Zhang, T. B. Britton</p><p> </p><h3><strong>Contents</strong></h3><p><strong>- New in v2.0.0: CAD drawings of the stage</strong></p><p> </p><p>- Single Si(100) diffraction patterns at 4 camera lengths, and at 4 corners of the sample</p><p>- Horizontal and vertical line scan on Si(100) with 20 grid points</p><p>- 20x20 mapping scan on a polycrystalline Cu sample</p><p>Scan parameters for the line scans and map are included in logfiles within each subfolder.</p><p> </p><p>All pattern files are provided in .h5 format and .tif format. Analyses of the patterns were performed with AstroEBSD and MTEX.</p>
Data for 'Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro'
<p>Title: Data for ‘Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro’</p> <p>Recommended Citation: Masao CA, Prescott GW, Snethlage MA, Urbach D, Torre-Marin Rando A, Molina-Venegas R, Mollel NP, Hemp C, Hemp A, Fischer M (2022). People and Nature.</p> <p>Principal Investigator:<br> - Markus Fischer (markus.fischer@ips.unibe.ch)</p> <p>Authors:<br> * joint first-author<br> - Catherine A. Masao (ndeutz@yahoo.com, ORCID: 0000-0002-1242-9117) *<br> - Graham W. Prescott (graham.prescott.research@gmail.com, ORCID: 0000-0001-5123-514X) *<br> - Mark A. Snethlage (mark.snethlage@ips.unibe.ch, ORCID: 0000-0002-1398-8869) *<br> - Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834) *<br> - Amor Torre-Marin Rando (amor.torre@ips.unibe.ch)<br> - Rafael Molina Venegas (rafmolven@gmail.com, ORCID 0000-0001-5801-0736)<br> - Neduvoto P. Mollel (neduvotomollel@yahoo.com, ORCID: 0000-0002-4402-4667)<br> - Claudia Hemp (claudiahemp@yahoo.com, ORCID: 0000-0002-5369-2122)<br> - Andreas Hemp (andreas.hemp@uni-bayreuth.de, ORCID: 0000-0001-9170-7113)<br> - Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)</p> <p>Date of data collection: 2018-09<br> Location of data collection: Moshi, Kilimanjaro Region, Tanzania<br> Date of final file release: 2022-01-13</p> <p>Data Overview:</p> <p>We conducted a three-day stakeholder workshop in Moshi, Tanzania, in September 2018. The workshop was attended by 73 participants (16 women and 57 men), whom we invited to represent various sectors and local communities. We established the list of invitees through an extensive online search validated and complemented by key local informants. We divided registered participants into five groups based on their sectoral affiliation: 16 residents of local communities, including farmers (herein ‘Community’), 14 researchers and scientists (‘Research’), 16 professionals in conservation and management (‘Conservation’), 17 professionals in forestry, agriculture, and water management and governance (‘Resources’), and 10 other professionals mainly drawn from the tourism sector (‘Other’).</p> <p>We used two questionnaires—herein ‘habitat’ and ‘ecosystem services’— with open and closed questions. Closed questions were scored using a Likert-type scale.</p> <p>File overview:</p> <p>1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv</p> <p>Data from the ‘habitat’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-09-22). Individual perceptions about the state of and trends in habitats and species diversity and about the direct and indirect factors driving these trends. We invited participants to fill out separate questionnaires for each habitat of importance to their sector or for which they had knowledge, starting with the most important one.</p> <p>2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv</p> <p>Data from the ‘ecosystem services’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-01-09). The ‘ecosystem services’ questionnaire collected individual perceptions about the state of, trends in, and importance of NCP (Nature's Contributions to People), as well as about the factors driving observed changes in access and provision. With reference to the preliminary group discussion on NCP, we invited participants to fill out separate forms for each NCP they deemed important to their sector or had knowledge about and to indicate which habitat(s) provide(s) each of them.</p> <p>3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv</p> <p>Adapted from the ecosytem services questionnaire data (kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv), coding the reasons for change in access to NCP.</p> <p>4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv</p> <p>Tally of recommended measures towards recorded from the carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. See Table S7 for details.</p> <p><br> Code used for analysis:<br> R code used for the statistical analysis and to create the figures available from: https://github.com/grahamprescott/kilimanjaro.ipbes.workshop.paper</p> <p>File details:</p> <p>1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv</p> <p>143 observations of 73 variables</p> <p>Key Variables:<br> - Group<br> (categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)<br> - Biome2<br> (categorical - standardised habitat categories used in the analysis, coded by Mark A. Snethlage)<br> - Habitat.area<br> (categorical - trends in habitat area over past 10 years (2008-2018); Decreased, Not Changed, Increased, No Answer)<br> - Habitat.condition<br> (categorical - trends in habitat condition over past 10 years (2008-2018); Deteriorated, Not Changed, Improved, No Answer)<br> - Habitat.area.will<br> (categorical - prediction for trend in habitat condition over next 10 years (2018-2028); Decrease Not Change, Increase, No Answer)<br> - Habitat.condition.will<br> (categorical - trends in habitat condition over past 10 years (2018-2028); Decrease, Not Change, Increase, No Answer)<br> Variables beginning with ES., DIR., IND., ACT. refer to ecosystem services (i.e. NCP), direct drivers, indirect drivers, and recommended actions associated with each habitat form. They are numerical and scored as 1 if that variable is mentioned (present) or 0 if not mentioned (absent). In a few cases where different ecosystem services listed by the participant are coded to the same variable the number is the number of times that ecosystem service is mentioned.</p> <p>Codes for ecosystem services (ES.): HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories. </p> <p>Codes for direct drivers (DIR.): ACT = ‘Human Activities’, CC = Climate Change, IAS = Invasive Alien Species, LUC = Land-Use Change, OVR = Overexploitation, POL = Pollution.</p> <p>Codes for indirect drivers (IND.): CLT = Cultural, DEM = Demographic, ECO = Economic, GOV = Governance, S.T = Science and Technology.</p> <p>Codes for recommended actions (ACT.): AWR = Awareness Raising, ECO = Livelihood, Economic & Moral Incentives, EDU = Education & Training, ENF = Law Enforcement & Prosecution, INS = Institutional Development, LAN = Land / Water Management, LAW = Legal & Policy Frameworks, PRT = Conservation Designation & Planning, RSR = Research & Monitoring, SPC = Species Management.</p> <p>2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv</p> <p>144 observations of 38 variables</p> <p>Key variables:</p> <p>- Group<br> (categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)<br> - Service.original (free text response to which ecosystem service the participant was filling out the form)<br> - ESCODE<br> (categorical - NCP category to which we assigned the free text response. Abbreviations: HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories.)<br> - Biome<br> (categorical - which habitat provided the ecosystem service)<br> - Why.changed.provision<br> (free text response for why Provision changed)<br> - Why.changed.access<br> (free text response for why Access changed) [Note: although we theoretically expected a distinction between provision and access of each ecosystem service, we observed that this distinction was not strictly followed in practice and deemed the responses about access to be most accurate]<br> - Access<br> (categorical - changes in access to the ecosystem service over the last 10 years (2008-2018); Decreased, No Change, Increased, No Answer)<br> - Access.will<br> (categorical - predicted changes in access to the ecosystem service over the next 10 years (2018-2028); Deteriorate, Not Change, No Answer, Improve (note: no one responded ‘Improve’)) </p> <p><br> 3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv</p> <p>144 observations of 7 variables</p> <p>We took the following variables from the ecosystem services questionnaire:<br> - ESCODE<br> (categorical - NCP category to which we assigned the free text response)<br> - Access<br> (whether access to this NCP increased or decreased between 2008-2018)<br> - Why.changed.access<br> (free text response for why Access changed)<br> And created a new variable to synthesise the drivers of change in NCP access:<br> - Why.changed.access.code</p> <p>Note: a challenge with the ‘Why.changed.access’ variable is that many drivers are listed in the same response. To process this, we duplicated the rows with multiple drivers so that there would be one row per driver. We did this using Microsoft Excel for Mac. We did this so that each link from a driver to an increase or decrease in a given NCP could be visualised. The individual links are not standardised by individual respondent or response. They represent every instance of a reported link between a driver of change and a change in access to a given NCP. Responses or respondents who listed multiple instances of NCP access change and/or multiple drivers have therefore contributed more to the Sankey figure (Figure 4). We chose this approach because the aim in this case was to document the complex web of drivers leading to changes in NCP access, drawing upon the collective expertise of the respondents, not to test for individual differences between groups or respondents. Graham W. Prescott and Mark A. Snethlage independently coded each of the drivers and reached a consensus on any disagreements. Graham W. Prescott edited the final file.</p> <p>4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv</p> <p>11 observations of 6 variables<br> <br> We also conducted a carousel session in which participants could suggest actions and actors that could contribute towards achieving a sustainable future for people and nature at Mt. Kilimanjaro. This file contains the tally of recommended measures arising from this carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. For full list of measures, see Table S7.</p> <p> </p> <p> </p>
FloodSense street sign mounted flood depth sensor
<p><strong>Flood Depth Data (FDD)</strong> collected by a fleet of sensors deployed across 5 boroughs of New York City with a resolution of half an inch or less. The metadata for the sensors is included in the metadata.csv to identify the deployment coordinates of sensors, each with a unique <strong><em>deployment_id</em></strong>. </p> <p>The depth data is collected at least every five minutes and every minute in some locations depending on the ability to harvest solar energy at that deployment location. </p> <p>The final depth data field is <strong><em>depth_proc_mm</em></strong>, and the raw data is <strong><em>dist_mm</em></strong>. </p> <p>The raw measurement values received from the sensor are distance measurements (dist_mm), which are simply distance measurements collected from a ranging ultrasonic-based sensor. These distance measurements are converted to depths using <strong><em>night_median_dist_mm</em></strong> which is a daily calculated median of nighttime sensor readings. Direct sunlight affects ranging measurements due to high variance in the air column between the sensor and the concrete surface that it is mounted over. Additionally, the housing internally heats up when under direct sunlight, which affects the sensor readings and appears as if the surface dips with the daily increase and decrease in temperature during the daytime.</p> <p>After converting to raw depth values, a simple range filter is applied to the data removing any anomalies that lie below 10 millimeters and above unrealistic depth values (for example a person - between 5ft to 6ft), which is named <strong><em>depth_filt_mm</em></strong>.</p> <p>Further, this filtered depth value is processed through data filters eliminating blips, any pulse chains, or a flat line due to garbage or a car parked underneath the sensor. The output of these filters is labeled <strong><em>depth_proc_mm</em></strong>. </p> <p>This data is intended for use by communities, researchers, and New York City government agencies to better understand the frequency, severity, and impacts of flooding in New York City. </p> <p>Here is the live dashboard for these sensors deployed: <a href="https://dataviz.floodnet.nyc/">FloodNet Data Dashboard</a></p> <p>More about this project at <a href="https://www.floodnet.nyc/">FloodNet.NYC</a></p> <p>This is an open-source project and for more information on the sensors and build manuals see the <a href="https://github.com/floodnet-nyc/flood-sensor">FloodNet FloodSensor GitHub page</a></p>
Sulphur Dioxide (SO2) emitted by Mount Etna on 4 March 2021
<p>Tracking volcanic Sulphur Dioxide (SO2) emitted by Mount Etna on 4 March 2021 that reached China with Sentinel-5P/TROPOMI data (<a href="https://www.volcanodiscovery.com/kunlun/news/124081/SO2-cloud-detected-in-the-area-of-Kunlun-volcano-Tibet-could-it-be-from-a-volcanic-eruption.html">Volcano Discovery article</a>).</p> <p> </p>
A Systematic Review on the Visualization of Avatars and Agents in AR & VR displayed using Head-Mounted Displays
<p>This repository contains the data for the article "Systematic Review on the Visualization of Avatars and Agents in<br> AR & VR displayed using Head-Mounted Displays" including a BibTex file and supporting figures.</p>
Data file for: Electrical imaging for the subduction channel north of Mount Everest
<p>The magnetotellurics data were used to study the the subduction channel north of Mount Everest, conducted by Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences. The data file of zf.dat was generated by the Matlab code EM3DVP.</p> <p>You are recommended to refer to the Kelbert et al., 2014 paper: https://doi.org/10.1016/j.cageo.2014.01.010 for a brief understanding of the data file formats.</p>
MCR LTER: Coral Reef: pH Time Series from Bottom-mounted SeaFET on the Fringing Reef, January-February 2011
Bottom-mounted instrumentation (SeaFET, Seabird thermistors) sampled for 6 weeks on the fringing reef of Moorea Island, French Polynesia at site LTER Fringe 1. Sampling began in January 2011. The instruments were secured to a cement piling at 3.3 meters depth and 0.7 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. The Seabird thermistors provided measures of seawater temperature at 10-minute sampling intervals. These data are published in Rivest, E.B. and G.E. Hofmann. 2014. Responses of the metabolism of the larvae of Pocillopora damicornis to ocean acidification and warming. PLoS ONE DOI:10.1371/journal.pone.0096172
Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))
<p>Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))</p>
Figure 7 in The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve
Figure 7. Ochrotrichia anticheirion sp. n., male genitalia. A) Segments VIII–IX, lateral (base of phallus crosshatched). B) Segments IX–X, dorsal. C) Inferior appendages, ventral. D) Phallus, lateral. E) Phallus, dorsal.
Figure 8 in The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve
Figure 8. Rhyacopsyche totuma sp. n., male genitalia. A) Segments IX–X, lateral. B) Segments IX–X, dorsal. C) Inferior appendages, ventral. D) Phallus, lateral. E) Phallus, dorsal.
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.