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Decomposition, porewater, plant and animal collection, and soil temperature data in Airport Marsh, Sapelo Island, 7/2019-7/2020
Environmental gradients can affect organic matter decay within and across wetlands and contribute to spatial heterogeneity in soil carbon stocks. We tested the sensitivity of decay rates to tidal flooding and soil depth in a minerogenic salt marsh using the tea bag index (TBI). Tea bags were buried at 10- and 50- cm along transects sited at lower, middle, and higher elevations that paralleled a headward eroding tidal creek. Plant and animal communities and soil properties were characterized once while replicate tea bags and porewaters were collected 3 and 4 times respectively over one year.
Elevation and soil salinity transects at Airport Marsh and Old Beach Road on Sapelo Island, Georgia in 2001
To examine the relationship between elevation and soil characteristics, I sampled two sites on Sapelo Island (Old Beach Road and Airport Marsh) in July of 2001. At each site, I collected 160 soil samples across a transect from the high to the low marsh, and surveyed the relative elevation of each sample location with a theodolite. I determined soil water content gravimetrically, soil organic content by ashing samples, and soil salinity by rehydrating dried soils, adding deionized water, measuring the salinity of the supernatant, and back calculating to the original soil water content.
Long-term monitoring of reptiles and ground arthropods near the Phoenix-Mesa Gateway Airport, Mesa, Arizona, USA, ongoing since 2010
Reptiles and amphibians have been monitored at the Gateway site since 2010. The goals of the project have been to provide undergraduate and graduate students opportunities to learn hands-on wildlife techniques, follow seasonal patterns of herpetofauna and ground arthropods, and serve as a test bed for new projects and technologies, including development of a mobile app for data collection. Live trapping methods include 6 trap arrays of pitfall and funnel traps placed along drift fences. Arrays are checked daily when traps are actively open to trap animals. Lizards are given a unique toe clip code, but all other species are unmarked. Reptiles and amphibians are weighed and measured and released at point of capture. Ground arthropods are counted to the Order-level. Arrays are open typically from March to October and the years vary in trapping effort with arrays open from 2 to 68 days per year. The most common species captured are tiger whiptail (*Aspidoscelis tigris*) and common side-blotched (*Uta stansburiana*) lizards.
NOAA Daily Surface Meteorologic Data at NCDC Miami International Airport Station (ID-085663), South Florida, USA, January 1948 - ongoing
The National Climatic Data Center's (NOAA) daily mean, maximum, and minimum air temperatures and daily precipitation collected at Miami International Airport Station (Coop ID- 085663).
North Temperate Lakes LTER Soil Temperature - Woodruff Airport 2006 - current
Soil temperature data are being gathered at a site at the Noble F. Lee municipal airport located at Woodruff, WI. Soil temperature is measured at depths of 0.05m, 0.1m and 0.5m at 1-minute intervals. High resolution data are collected (typically at 10 minute intervals) along with 1-hour and 24-hour averages. Daily minimum and maximum soil temperatures and the times these occur are reported for these same depths. Data are automatically updated into the database every six hours. Prior to August 2006, only hourly averaged data are available. Starting in 2008, soil temperatures are only available from 0.5m depth. Sampling frequency: varies for instantaneous samples; averaged to hourly and daily values from one minute samples. Number of sites: 1. Data collection failure caused data loss for the first half of 2024.
North Temperate Lakes LTER: Meteorological Data - Woodruff Airport 1989 - current
Meteorological measurements are being gathered at a site at the Noble F. Lee municipal airport located at Woodruff, WI for three purposes: 1) to supplement the data from the raft on Sparkling and Trout Lakes used for evaporation calculations, and 2) to provide standard meteorological measurements for the North Temperate Lakes site, and 3) to measure radiation for primary production studies in the study lakes at the site. The following parameters are measured at 1-minute intervals: 1) air temperature at 1.5 m above ground, 2) relative humidity at 1.5 m above ground, 3) wind speed and direction and peak wind speed at 3 m above ground, 4) total long-wave radiation, 5) total short-wave radiation, 6) photosynthetically active radiation (PAR), 7) total solar radiation, and 8) total precipitation. High resolution data is taken typically at 1 minute intervals as well as 1-hour and 24-hour averages. Half-hourly averages of PAR and shortwave radiation are also stored. Precipitation data can be acquired from the National Weather Service (Minocqua, WI). Derived data included in this data set include dew point temperature as well as daily minimum and maximum values for some parameters. Number of sites: 1. Date/time is Central Standard Time (GMT - 06:00) throughout the year. Crash of the data collection system resulted in the loss of most of the first half of 2024.
Field measurements of aerosol particles near a runway at Narita International Airport, Japan
<p>Field measurements of aerosol particles were conducted at an observation point ~180 m from the centerline of runway A (~140 m from the edge of the runway) at Narita International Airport (NRT), Japan, in February 2018. The online aerosol instruments used for the field measurements consisted of an ultrafine condensation particle counter (UCPC; Model 3776, TSI, d<sub>50</sub> = 2.5 nm), a condensation particle counter (CPC; Model 3771, TSI, d<sub>50</sub> = 10 nm), a scanning mobility particle sizer (SMPS; Model 3080, TSI), and an engine exhaust particle sizer (EEPS; TSI). The other instruments included a carbon dioxide (CO<sub>2</sub>) monitor (Model LI-840, Li-Cor Biosciences) and meteorological sensors. The sampling inlet for the UCPC, CPC, and SMPS was switched between an unheated (room temperature) mode and a 350°C heated mode every eight hours during selected time periods to measure the total and the non-volatile particles, respectively. The EEPS was operated independently from the UCPC/CPC/SMPS inlet system and it measured the unheated particle number size distributions during the entire period.</p>
Dataset for the paper "Aircraft wake vortices affecting airport wind measurements"
<p>Dataset in support of the paper "Aircraft wake vortices affecting airport wind measurements". The dataset contains the results of the manual classification as discussed in section 2 and 3 of the paper, details can be found there.</p><p>For each take-off, one row exists in the dataset. The columns are:</p><ul><li><i>takeoff_no</i>: int, Incrementing integer</li><li><i>timestamp</i>: string, UTC time the flight passes by the anemometer</li><li><i>flight_id</i>: string, Unique identifier for the flight</li><li><i>typecode</i>: string, ICAO aircraft typecode of the flight</li><li><i>wtc</i>: string: ICAO wake turbulence category of the flight</li><li><i>groundspeed_kts</i>: float, Groundspeed [kts] at the moment of passing by the anemometer</li><li><i>alt_above_thr_m</i>: float, Altitude above runway threshold [m] at the moment of passing by the anemometer</li><li><i>wind_speed_kts</i>: float, Wind speed [kts]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>wind_dir_deg</i>: float, Wind direction [°]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>is_event_visual_assessor_1</i>: int, Classification of assessor 1 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_2</i>: int, Classification of assessor 2 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_3</i>: int, Classification of assessor 3 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_sum</i>: int, Sum of classifications of 3 assessors (0 to 3)</li><li><i>is_event_wake_model</i>: float, Classification of wheather the flight caused a wake that hit the anemometer based on P2P wake model output (only applied to flights with a sum of classifications of 2 and more)</li><li><i>is_event</i>: int, Final classification of wheather the flight caused a wake that hit the anemometer</li></ul><p> </p>
Particle number concentration and meteorological measurements at Berlin-Tegel Airport during its closure
<p>Observation data of particle number concentrations (PNC) on the airfield of Berlin-Tegel Airport (TXL) during its closure. PNC was recorded with a Grimm EDM465 UFPC, including meteorological parameters measured with a Lufft WS600-UMB.</p> <ul> <li>observations between 20. October 2020, 14:44 LT and 3 December 2020, 03:03 LT</li> <li>time interval: 5 seconds</li> <li>air inlet of CPC at 1.4 m above ground</li> <li>weather sensor at 1.3 m above the ground.</li> <li>Location: 52,561 North, 13,320 East</li> <li>Variables: <ul> <li>particle number concentration in particles/cm³ (pnc)</li> <li>wind speed in m/s (ws)</li> <li>wind direction in ° (wdir)</li> <li>air temperature in °C (temp)</li> <li>relative humidity in % (rh)</li> <li>air pressure in hPa (pressure)</li> <li>precipitation in mm (pcpn)</li> <li>date as local time</li> <li>phase: whether the airport was still open ("TXL_open") or already closed ("TXL_closed")</li> <li>no data available between between 15.11.2020 02:15:00 and 18.11.2020 11:14:55 due to hardware issues</li> </ul> </li> </ul>
A Dataset with Synthetic Landing Trajectories for Zurich Airport
<p>The archive contains synthetic datasets in npy format generated using a TimeGAN-based model designed to capture a range of aircraft landing trajectories at Zurich airport across different operational scenarios and environmental conditions. Each dataset consists of multiple groups representing distinct patterns or behaviors in the trajectory data. The trajectories are segmented in clusters, and to some of them a smoothing filter was applied.</p> <p><span>The datasets incorporate a range of variables critical for modeling aircraft landing behaviors. Continuous variables such as longitude, latitude, and altitude exhibit multimodal distributions, capturing different operational phases and conditions within each cluster. The data is stored in an array format with dimensions (number of samples, sequence length, feature dimensions). Here, the number of samples corresponds to the total number of recorded flight trajectories included in the dataset, while the sequence length represents the duration or the number of time steps over which each trajectory is recorded. The feature dimensions denote the various variables (state vector) measured at each time step, consisting of longitude, latitude and altitude. Categorical variables, such as runway identifiers and cluster labels, follow distributions that reflect operational frequencies, with certain clusters or runways being more common under specific conditions. </span></p> <p>The archive contains the following files:</p> <p>- 5clust0.npy, 5clust1.npy, 5clust2.npy, 5clust3.np & 5clust4.npy (5 clusters of landing trajectories separated)<br>- ma_5clust0.npy, ma_5clust1.npy, ma_5clust2.npy, ma_5clust3.np & ma_5clust4.npy (5 clusters of landing trajectories separated, moving average filter applied)<br>- ma_3clust0.npy, ma_3clust1.npy & ma_3clust2.npy (3 clusters of landing trajectories separated, moving average filter applied)<br>- run28_syn.npy & run24_syn.npy (groups of landing trajectories per runway)<br>- ma_run28_syn.npy & ma_run24_syn.npy (groups of landing trajectories per runway, moving average filter applied)<br>- go_around_synthetic.npy (go-around landing trajectories on runway 14)</p>
DATA: Chatbots in Airport Customer Service – Exploring Use Cases and Technology Acceptance
<p>This dataset (n=191) investigates use cases and technology acceptance of chatbots in airport customer service.</p>
Fig. 1 in Vascular flora of Milan Malpensa airport (Lombardy, Italy). Part I: checklist
Fig. 1 - Location of the study area at national (a: red arrow) and provincial level (b: red contour) and a simplified map of the Milan Malpensa airport (c): green color highlights the vegetated areas. / Localizzazione dell'area di studio a livello nazionale (a: freccia rossa) e provinciale (b: contorno rosso) e una mappa semplificata del sedime aeroportuale di Milano Malpensa (c): il colore verde evidenzia le aree vegetate. (Drawing / disegno: M. Martignoni).
Figure. The Central Black Sea Region of Turkey and sampling sites. Sampling sites: 1. Amasya: Centrum, Firingiler, 40°41′15.9″N, 35°54′45.9″E, 378 m; 2. Amasya: Göynücek, Kışlabeyi Village, 40°23′25.2″N, 35°33′43.1″E, 542 m; 3. Amasya: Gümüşhacıköy, Keçi Village, 40°49′07.5″N, 35°15′35.4″E, 777 m; 4. Amasya: Merzifon, Yakacık Village, 40°53′48.6″N, 35°25′43.9″E, 877 m; 5. Amasya: Suluova, Centrum, 40°49′24.3″N, 35°37′18.0″E, 473 m; 6. Amasya: Suluova, Çayüstü Village, 40°48′43.4″N, 35°38′24.4″E, 495 m; 7. Amasya: Taşova, 40°44′55.5″N, 36°17′49.6″E, 242 m; 8. Amasya: Taşova, Güngörmüş Village, 40°43′41.8″N, 36°17′06.3″E, 279 m; 9. Çorum: Centrum, Güney Village, 40°37′47.6″N, 35°05′58.5″E, 1170 m; 10. Çorum: Laçin, Gökgözler Village, 40°48′48.6″N, 34°50′38.6″E, 434 m; 11. Çorum: Mecitözü, Centrum, 40°31′41.1″N, 35°18′22.3″E, 767 m; 12. Çorum: Mecitözü, Hıdırlı Village, 40°29′19.6″N, 35°15′10.9″E, 918 m; 13. Çorum: Ortaköy, Senemoğlu Village, 40°19′24.0″N, 35°21′37.2″E, 533 m; 14. Çorum: Uğurludağ, Eskiçeltek Village, 40°33′46.6″N, 34°27′00.0″E, 519 m; 15. Ordu: Akkuş, Gökçebayır, 40°43′06.0″ N, 37°01′33.5″E, 920 m; 16. Ordu: Fatsa, Ayazlı, 41°00′32.9″N, 37°27′06.9″E, 130 m; 17. Ordu: Gölköy, 40°40′18.9″N, 37°36′43.4″E, 850 m; 18. Ordu: İkizce, 41°06′07.9″N, 37°07′45.3″E, 50 m; 19. Ordu: Korgan, Terzili Village, 40°42′06.6″N, 37°17′39.2″E, 1246 m; 20. Ordu: Korgan, Yenipınar Village, 40°47′58.0″N, 37°21′31.6″E, 584 m; 21. Ordu: Mesudiye, Centrum, 40°27′42.7″N, 37°46′23.0″E, 1100 m; 22. Ordu: Perşembe, Yumrutaş Village, 41°06′07.7″ N, 37°45′38.3″E, 231 m; 23. Ordu: Ünye, Cevizdere Village, 41°06′26.4″ N, 37°20′10.2″E, sea level; 24. Samsun, Terme, Centrum, 41°12′22.4″N, 36°56′14.8″E, sea level; 25. Samsun:Ayvacık, Yenice Village, 41°03′05.5″N, 36°39′17.4″E, 70 m; 26. Samsun: Bafra, Karaköy, 41°31′26.1″N, 36°00′52.5″E, 21 m; 27. Samsun: Centrum, Ataköy, 41°15′22.9″N, 36°17′26.8″E, 150 m; 28. Samsun: Centrum, entrance of Yeşiltepe (Çorak Village), 41°14′29.6″N, 36°16′52.8″E, 32 m; 29. Samsun: Havza, entrance of Mürsel Village, 40°59′26.5″N, 35°43′20.9″E, 642 m; 30. Samsun: Kavak, İdrisli Village, 41°05′45.5″N, 35°59′36.0″E, 706 m; 31. Samsun: Ladik, Tatlıcak Village, 40°55′29.6″N, 35°58′13.1″E, 870 m; 32. Samsun: Ladik, the vicinity of Lake Ladik, 40°54′06.0″N, 35°59′49.9″E, 870 m; 33. Samsun: Ondokuz Mayıs, Yörükler, 41°31′14.8″N, 36°07′23.6″E, sea level; 34. Samsun: Tekkeköy, Kerpiçli Village, 41°09′26.9″N, 36°32′04.4″E, 152 m; 35. Samsun: Vezirköprü, Pazarcı Village, 41°04′18.5″ N, 35°30′23.2″E, 690 m; 36. Tokat: Almus, Centrum, 40°22′35.5″N, 36°54′42.5″E, 803 m; 37. Tokat: Artova, Centrum, 40°06′42.1″N, 36°18′14.3″E, 1170 m; 38. Tokat: Centrum, vicinity of Tokat Airport, 40°18′23.5″N, 36°20′12.0″E, 556 m; 39. Tokat: Erbaa, Dereçiftliği, 40°33′22.3″ N, 36°37′22.4″E, 384 m; 40. Tokat: Niksar, Şahinli Village, 40°35′09.2″N, 36°53′59.5″E, 270 m; 41. Tokat: Reşadiye, Centrum, 40°23′02.9″N, 37°20′06.3″E, 511 m; 42. Tokat: Turhal, 40°20′21.1″N, 36°08′41.2″E, 507 m; 43. Tokat: Turhal, Arzupınar Village, 40°19′43.7″N, 36°10′52.3″E, 608 m. in The Ceratopogonidae (Insecta: Diptera) fauna of the Central Black Sea Region in Turkey
Figure. The Central Black Sea Region of Turkey and sampling sites. Sampling sites: 1. Amasya: Centrum, Firingiler, 40°41′15.9″N, 35°54′45.9″E, 378 m; 2. Amasya: Göynücek, Kışlabeyi Village, 40°23′25.2″N, 35°33′43.1″E, 542 m; 3. Amasya: Gümüşhacıköy, Keçi Village, 40°49′07.5″N, 35°15′35.4″E, 777 m; 4. Amasya: Merzifon, Yakacık Village, 40°53′48.6″N, 35°25′43.9″E, 877 m; 5. Amasya: Suluova, Centrum, 40°49′24.3″N, 35°37′18.0″E, 473 m; 6. Amasya: Suluova, Çayüstü Village, 40°48′43.4″N, 35°38′24.4″E, 495 m; 7. Amasya: Taşova, 40°44′55.5″N, 36°17′49.6″E, 242 m; 8. Amasya: Taşova, Güngörmüş Village, 40°43′41.8″N, 36°17′06.3″E, 279 m; 9. Çorum: Centrum, Güney Village, 40°37′47.6″N, 35°05′58.5″E, 1170 m; 10. Çorum: Laçin, Gökgözler Village, 40°48′48.6″N, 34°50′38.6″E, 434 m; 11. Çorum: Mecitözü, Centrum, 40°31′41.1″N, 35°18′22.3″E, 767 m; 12. Çorum: Mecitözü, Hıdırlı Village, 40°29′19.6″N, 35°15′10.9″E, 918 m; 13. Çorum: Ortaköy, Senemoğlu Village, 40°19′24.0″N, 35°21′37.2″E, 533 m; 14. Çorum: Uğurludağ, Eskiçeltek Village, 40°33′46.6″N, 34°27′00.0″E, 519 m; 15. Ordu: Akkuş, Gökçebayır, 40°43′06.0″ N, 37°01′33.5″E, 920 m; 16. Ordu: Fatsa, Ayazlı, 41°00′32.9″N, 37°27′06.9″E, 130 m; 17. Ordu: Gölköy, 40°40′18.9″N, 37°36′43.4″E, 850 m; 18. Ordu: İkizce, 41°06′07.9″N, 37°07′45.3″E, 50 m; 19. Ordu: Korgan, Terzili Village, 40°42′06.6″N, 37°17′39.2″E, 1246 m; 20. Ordu: Korgan, Yenipınar Village, 40°47′58.0″N, 37°21′31.6″E, 584 m; 21. Ordu: Mesudiye, Centrum, 40°27′42.7″N, 37°46′23.0″E, 1100 m; 22. Ordu: Perşembe, Yumrutaş Village, 41°06′07.7″ N, 37°45′38.3″E, 231 m; 23. Ordu: Ünye, Cevizdere Village, 41°06′26.4″ N, 37°20′10.2″E, sea level; 24. Samsun, Terme, Centrum, 41°12′22.4″N, 36°56′14.8″E, sea level; 25. Samsun:Ayvacık, Yenice Village, 41°03′05.5″N, 36°39′17.4″E, 70 m; 26. Samsun: Bafra, Karaköy, 41°31′26.1″N, 36°00′52.5″E, 21 m; 27. Samsun: Centrum, Ataköy, 41°15′22.9″N, 36°17′26.8″E, 150 m; 28. Samsun: Centrum, entrance of Yeşiltepe (Çorak Village), 41°14′29.6″N, 36°16′52.8″E, 32 m; 29. Samsun: Havza, entrance of Mürsel Village, 40°59′26.5″N, 35°43′20.9″E, 642 m; 30. Samsun: Kavak, İdrisli Village, 41°05′45.5″N, 35°59′36.0″E, 706 m; 31. Samsun: Ladik, Tatlıcak Village, 40°55′29.6″N, 35°58′13.1″E, 870 m; 32. Samsun: Ladik, the vicinity of Lake Ladik, 40°54′06.0″N, 35°59′49.9″E, 870 m; 33. Samsun: Ondokuz Mayıs, Yörükler, 41°31′14.8″N, 36°07′23.6″E, sea level; 34. Samsun: Tekkeköy, Kerpiçli Village, 41°09′26.9″N, 36°32′04.4″E, 152 m; 35. Samsun: Vezirköprü, Pazarcı Village, 41°04′18.5″ N, 35°30′23.2″E, 690 m; 36. Tokat: Almus, Centrum, 40°22′35.5″N, 36°54′42.5″E, 803 m; 37. Tokat: Artova, Centrum, 40°06′42.1″N, 36°18′14.3″E, 1170 m; 38. Tokat: Centrum, vicinity of Tokat Airport, 40°18′23.5″N, 36°20′12.0″E, 556 m; 39. Tokat: Erbaa, Dereçiftliği, 40°33′22.3″ N, 36°37′22.4″E, 384 m; 40. Tokat: Niksar, Şahinli Village, 40°35′09.2″N, 36°53′59.5″E, 270 m; 41. Tokat: Reşadiye, Centrum, 40°23′02.9″N, 37°20′06.3″E, 511 m; 42. Tokat: Turhal, 40°20′21.1″N, 36°08′41.2″E, 507 m; 43. Tokat: Turhal, Arzupınar Village, 40°19′43.7″N, 36°10′52.3″E, 608 m.
Рис. 1. Пункты сбора Staphylinidae на острове Беринга и острове Топорков. 1 – с. НикоΛьское; 2 – окрестности с. НикоΛьское, мыс ВхоΑной Риф; 3–4 – берег и пойма р. Гаванская; 5 – песчаные Αюны межΑу с. НикоΛьским и р. ΑоΑыгинская; 6 – окрестности Северо-ЗапаΑного Λежбища; 7 – Северное Λежбище; 8–9 – окрестности корΑона в бухте Старая Гавань; 10 – бухта Буян и пойма р. Буян; 11 – бухта ПоΛуΑенная, 12 – бухта ПоΑутесная; 13 – о. Топорков; 14 – окрестности аэропорта и поймы р. Каменка; 15 – бухта КоманΑор. Fig. 1. Localities of Staphylinidae on Bering and Toporkov islands. 1 – Nikolskoe vill.; 2 – vicinity of Nikolskoe vill., Cape Vkhodnoy Reef; 3–4 – coast and floodplain of Gavanskaya River; 5 – sand dunes between Nikolskoe vill. and Lodyginskaya River; 6 – vicinity of Northwest rookery; 7 – North rookery; 8–9 – vicinity of Staraya Gavan' Bay; 10 –Buyan Bay and floodplain of Buyan River; 11 – Poludennaya Bay; 12 – Podutesnaya Bay; 13 – Toporkov Island; 14 – vicinity of airport and floodplain of Kamenka River; 15 – Commander Bay. in Materials to the rove beetles fauna (Coleoptera: Staphylinidae) of the Commander Islands (Kamchatka Region, Russia)
Рис. 1. Пункты сбора Staphylinidae на острове Беринга и острове Топорков. 1 – с. НикоΛьское; 2 – окрестности с. НикоΛьское, мыс ВхоΑной Риф; 3–4 – берег и пойма р. Гаванская; 5 – песчаные Αюны межΑу с. НикоΛьским и р. ΑоΑыгинская; 6 – окрестности Северо-ЗапаΑного Λежбища; 7 – Северное Λежбище; 8–9 – окрестности корΑона в бухте Старая Гавань; 10 – бухта Буян и пойма р. Буян; 11 – бухта ПоΛуΑенная, 12 – бухта ПоΑутесная; 13 – о. Топорков; 14 – окрестности аэропорта и поймы р. Каменка; 15 – бухта КоманΑор. Fig. 1. Localities of Staphylinidae on Bering and Toporkov islands. 1 – Nikolskoe vill.; 2 – vicinity of Nikolskoe vill., Cape Vkhodnoy Reef; 3–4 – coast and floodplain of Gavanskaya River; 5 – sand dunes between Nikolskoe vill. and Lodyginskaya River; 6 – vicinity of Northwest rookery; 7 – North rookery; 8–9 – vicinity of Staraya Gavan' Bay; 10 –Buyan Bay and floodplain of Buyan River; 11 – Poludennaya Bay; 12 – Podutesnaya Bay; 13 – Toporkov Island; 14 – vicinity of airport and floodplain of Kamenka River; 15 – Commander Bay.
Figure 2 in Mosquito Surveillance Program Using Ovitraps Detected Aedes aegypti at the Honolulu International Airport in 2012
Figure 2. Mean number of eggs collected monthly using ovitraps from Honolulu International Airport from May 2010 to June 2012. Monthly values are the averages of collections for all weeks in the month, by collection site.
Figure 1 in Mosquito Surveillance Program Using Ovitraps Detected Aedes aegypti at the Honolulu International Airport in 2012
Figure 1. Map of the Hawaiian Islands. Markers of "X" represent each of this study's Aedes aegypti collection sites on Oahu and Hawaii islands.
Figure 4 in Mosquito Surveillance Program Using Ovitraps Detected Aedes aegypti at the Honolulu International Airport in 2012
Figure 4. Mean number of eggs collected per week using ovitraps and mean rainfall per week at Honolulu International Airport from May 2010 to June 2012. * shows the weeks (87 and 110) in which A. aegypti was collected.
Figure 5. Neighbor-joining tree for A in Mosquito Surveillance Program Using Ovitraps Detected Aedes aegypti at the Honolulu International Airport in 2012
Figure 5. Neighbor-joining tree for A. aegypti based on COI (450bp) and ND4 (322bp) sequences. Labels are Genbank accession numbers combined with country names.
Figure 3 in Mosquito Surveillance Program Using Ovitraps Detected Aedes aegypti at the Honolulu International Airport in 2012
Figure 3. Frequency distribution of egg collections for each ovitrap site at Honolulu International Airport
Runway sequencing benchmarks for Manchester Airport
<p>Runway sequencing benchmarks for Manchester Airport for various traffic densities</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.