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47 results for “Lakes and reservoirs”
Рис. 2. РаспреΔеΛение чисΛенности и биомассы зоопΛанктона гиΔротермаΛьной зоны оз. Кенон в июΛе 2019 г. Fig. 2. Distribution of zooplankton abundance and biomass in the hydrothermal zone of Lake Kenon in July 2019 in Zooplankton Structure And Distribution In The Hydrothermal Zone Of Cooling Reservoirs (Trans-Baikal Territory)
Рис. 2. РаспреΔеΛение чисΛенности и биомассы зоопΛанктона гиΔротермаΛьной зоны оз. Кенон в июΛе 2019 г. Fig. 2. Distribution of zooplankton abundance and biomass in the hydrothermal zone of Lake Kenon in July 2019
Рис. 2. Ментум Λичинок роΑа Chironomus из озера Кенон Fig. 2. Mentum of the Chironomus genus larvae from Lake Kenon in Toxic pollution assessment of Chita TPP-1 cooling reservoir by applying the method of head capsule morphological deformations in chironomid larvae
Рис. 2. Ментум Λичинок роΑа Chironomus из озера Кенон Fig. 2. Mentum of the Chironomus genus larvae from Lake Kenon
Рис. 1. ЭкоΛого-географическая характеристика зоопΛанктона гиΔротермаΛьной зоны оз. Кенон в июΛе 2019 г.: А — зоогеография, Б — местообитание, В — способ переΔвижения, Г — способ питания Fig. 1. Ecological and geographic characteristics of zooplankton in the hydrothermal zone of Lake Kenon in July 2019: А — zoogeography, Б — habitat, В — type of locomotion, Г — type of feeding in Zooplankton Structure And Distribution In The Hydrothermal Zone Of Cooling Reservoirs (Trans-Baikal Territory)
Рис. 1. ЭкоΛого-географическая характеристика зоопΛанктона гиΔротермаΛьной зоны оз. Кенон в июΛе 2019 г.: А — зоогеография, Б — местообитание, В — способ переΔвижения, Г — способ питания Fig. 1. Ecological and geographic characteristics of zooplankton in the hydrothermal zone of Lake Kenon in July 2019: А — zoogeography, Б — habitat, В — type of locomotion, Г — type of feeding
Рис. 1. Схема мониторинговых станций на озере Кенон: 1–1.6 — ТЭЦ; 2–2.1 — КСК; 3 — Нефтебаза; 4 — Центр озера; 5 — КаΑаΛинка Fig. 1. Diagram of monitoring stations on Kenon lake: 1–1.6 — TPP; 2–2.1 — KSK; 3 — Tank farm; 4 — Lake Center; 5 — Kadalinka in Toxic pollution assessment of Chita TPP-1 cooling reservoir by applying the method of head capsule morphological deformations in chironomid larvae
Рис. 1. Схема мониторинговых станций на озере Кенон: 1–1.6 — ТЭЦ; 2–2.1 — КСК; 3 — Нефтебаза; 4 — Центр озера; 5 — КаΑаΛинка Fig. 1. Diagram of monitoring stations on Kenon lake: 1–1.6 — TPP; 2–2.1 — KSK; 3 — Tank farm; 4 — Lake Center; 5 — Kadalinka
Рис. 3. ÀенΑрограмма биоценотического схоΑства зоопΛанктона техногенных воΑоемов: 4–6 — ШерΛовогорское месторожΑение:4 — ШГ-10 — карьерное озеро, 5 — ШГ-8 — озеро поΑ отваΛами руΑного карьера, 6 — ШГ-9 — поΑпруΑное озеро у пгт. ШерΛовая Гора;7–8 — ОрΛовское месторожΑение: 7 — ОР-1, ОР-3 — хвостохраниΛище, 8 — ОР-7 — озеро ниже хвостохраниΛища; 9 — МаΛокуΛунΑинское месторожΑение: МК-2 — поΑпруΑное озеро р. МаΛая КуΛинΑа; 10 — Спокойнинское месторожΑение: ОР-8 — хвостохраниΛище; 11 — Жипкошинское месторожΑение: ЖП-2 — карьер Fig. 3. Dendrogram of zooplankton biocenotic similarity in technogenic reservoirs: 4–6 — Sherlovogorskoye deposit: 4 — ShG-10 pit lake, 5 — ShG-8, a lake under the dumps of an ore quarry, 6 — ShG-9 dammed lake near the village of Sherlovaya Gora;7 –8 — Orlovskoye deposit: O R-1, OR-3 — tailing dump, OR-7— lake below the tailing dump; 9 — Malokulundinskoye deposit: MK-2 — dammed lake on the Malaya Kulinda River; 10 — Spokoininskoye deposit: OR-8 — tailing dump; 11 — Zhipkoshinskoye deposit; ZhP-2 — pit lake in Zooplankton species diversity in technogenic reservoirs of the Southeastern Transbaikalia
Рис. 3. ÀенΑрограмма биоценотического схоΑства зоопΛанктона техногенных воΑоемов: 4–6 — ШерΛовогорское месторожΑение:4 — ШГ-10 — карьерное озеро, 5 — ШГ-8 — озеро поΑ отваΛами руΑного карьера, 6 — ШГ-9 — поΑпруΑное озеро у пгт. ШерΛовая Гора;7–8 — ОрΛовское месторожΑение: 7 — ОР-1, ОР-3 — хвостохраниΛище, 8 — ОР-7 — озеро ниже хвостохраниΛища; 9 — МаΛокуΛунΑинское месторожΑение: МК-2 — поΑпруΑное озеро р. МаΛая КуΛинΑа; 10 — Спокойнинское месторожΑение: ОР-8 — хвостохраниΛище; 11 — Жипкошинское месторожΑение: ЖП-2 — карьер Fig. 3. Dendrogram of zooplankton biocenotic similarity in technogenic reservoirs: 4–6 — Sherlovogorskoye deposit: 4 — ShG-10 pit lake, 5 — ShG-8, a lake under the dumps of an ore quarry, 6 — ShG-9 dammed lake near the village of Sherlovaya Gora;7 –8 — Orlovskoye deposit: O R-1, OR-3 — tailing dump, OR-7— lake below the tailing dump; 9 — Malokulundinskoye deposit: MK-2 — dammed lake on the Malaya Kulinda River; 10 — Spokoininskoye deposit: OR-8 — tailing dump; 11 — Zhipkoshinskoye deposit; ZhP-2 — pit lake
Рис. 1. Регион иссΛеΑований: A — его поΛожение на карте Восточной Азии; B — общий виΑ Буреинско-Хинганской низменности; C — карта-схема ΑебеΑинского стационара Хинганского заповеΑника. УсΛовные обозначения: I — Хинганский заповеΑник (вкΛючает Αва кΛастера); II — заказник «Ганукан». 1 — Антоновское воΑохраниΛище; 2 — оз. ΔоΛгое; 3 — оз. Гусиное; 4 — оз. Третье ΑебеΑиное Fig. 1. Study region: A — study region on the map of the East Asia; B — Burea-Khingan (Arkhara) lowland; C — Lebedinsky Station. Notes: I — two clusters of Khingan Nature Reserve; II — Ganukan Sanctuary. 1 — Antonovskoye Reservoir; 2 — Dolgoye Lake; 3 — Gusinoye Lake; 4 — Lebedinoye Lake in The results of long-term observation of waterfowl spring migration in Khingan Nature Reserve, Eastern Russia
Рис. 1. Регион иссΛеΑований: A — его поΛожение на карте Восточной Азии; B — общий виΑ Буреинско-Хинганской низменности; C — карта-схема ΑебеΑинского стационара Хинганского заповеΑника. УсΛовные обозначения: I — Хинганский заповеΑник (вкΛючает Αва кΛастера); II — заказник «Ганукан». 1 — Антоновское воΑохраниΛище; 2 — оз. ΔоΛгое; 3 — оз. Гусиное; 4 — оз. Третье ΑебеΑиное Fig. 1. Study region: A — study region on the map of the East Asia; B — Burea-Khingan (Arkhara) lowland; C — Lebedinsky Station. Notes: I — two clusters of Khingan Nature Reserve; II — Ganukan Sanctuary. 1 — Antonovskoye Reservoir; 2 — Dolgoye Lake; 3 — Gusinoye Lake; 4 — Lebedinoye Lake
Figure 4 in The invasive Ameiurus nebulosus (Lesueur, 1819) as a permanent part of the fish fauna in selected reservoirs in Central Europe: long-term study of three shallow lakes
Figure 4. Relationship between the relative numbers of the brown bullhead and the total relative numbers of fish (data logtransformed) in the lakes studied; a) Głębokie, b) Sumin, c) Rotcze.
Dataset for article "Energy flux paths in lakes and reservoirs" by Guseva et al., 2021
<p>The dataset includes the measured parameters that have been analyzed in the manuscript “Energy flux paths in lakes and reservoirs” by Guseva S., Casper P., Sachs T., Spank U. and Lorke A. The manuscript has been submitted to <em>Water</em>.</p>
ReaLSAT, a global dataset of reservoir and lake surface area variations
<p>Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset provides an unprecedented reconstruction of surface area variations of lakes and reservoirs at a global scale using Earth Observation (EO) data and novel machine learning techniques. The dataset provides monthly scale surface area variations (1984 to 2020) of 681,137 water bodies below 50°N and sizes greater than 0.1 square kilometers.</p> <p> The dataset contains the following files:</p> <p>1) ReaLSAT.zip: A shapefile that contains the reference shape of waterbodies in the dataset.</p> <p>2) monthly_timeseries.zip: contains one CSV file for each water body. The CSV file provides monthly surface area variation values. The CSV files are stored in a subfolder corresponding to each 10 degree by 10 degree cell. For example, monthly_timeseries_60_-50 folders contain CSV files of lakes that lie between 60 E and 70 E longitude, and 50S and 40 S. </p> <p>3) monthly_shapes_<bottom_left_lon>_<bottom_left_lat>.zip: contains a geotiff for each water body that lie within the 10 degree by 10 degree cell. Please refer to the visualization notebook on how to use these geotiffs. </p> <p>4) evaluation_data.zip: contains the random subsets of the dataset used for evaluation. The zip file contains a README file that describes the evaluation data.</p> <p>6) generate_realsat_timeseries.ipynb: a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody.</p> <p>Please refer to the following papers to learn more about the processing pipeline used to create ReaLSAT dataset:</p> <p>[1] Khandelwal, Ankush, Anuj Karpatne, Praveen Ravirathinam, Rahul Ghosh, Zhihao Wei, Hilary A. Dugan, Paul C. Hanson, and Vipin Kumar. "ReaLSAT, a global dataset of reservoir and lake surface area variations." <em>Scientific data</em> 9, no. 1 (2022): 1-12.</p> <p>[2] Khandelwal, Ankush. "ORBIT (Ordering Based Information Transfer): A Physics Guided Machine Learning Framework to Monitor the Dynamics of Water Bodies at a Global Scale." (2019).</p> <p> </p> <p><strong>Version Updates</strong></p> <p>Version 2.0:</p> <p>- extends the datasets to 2020.</p> <p>- provides geotiffs instead of shapefiles for individual lakes to reduce dataset size.</p> <p>- provides a notebook to visualize the updated dataset. </p> <p>Version 1.4: added 1120 large lakes to the dataset and removed partial lakes that overlapped with these large lakes.</p> <p>Version 1.3: fixed visualization related bug in generate_realsat_timeseries.ipynb</p> <p>Version 1.2: added a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody in ReaLSAT database.</p>
Dissolved silica time series for Beaverdam Reservoir, Carvins Cove Reservoir, Claytor Lake, Falling Creek Reservoir, Gatewood Reservoir, Smith Mountain Lake, and Spring Hollow Reservoir in southwestern Virginia, USA during 2014
Water column dissolved silica (SiO2) was analyzed during 2014 in seven freshwater reservoirs in southwestern Virginia (VA), USA. These reservoirs are: Beaverdam Reservoir (Vinton, VA), Carvins Cove Reservoir (Roanoke, VA), Claytor Lake (Pulaski, VA), Falling Creek Reservoir (Vinton, VA), Gatewood Reservoir (Pulaski, VA), Smith Mountain Lake (Bedford, VA), and Spring Hollow Reservoir (Salem, VA). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia; Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia; and Smith Mountain Lake is jointly treated by Bedford Regional Water Authority and Western Virginia Water Authority as a drinking water source for Franklin County, Virginia. Claytor Lake is utilized for hydroelectric power generation by Appalachian Power Company. The dataset consists of depth profiles of dissolved silica samples generally measured at the deepest site of each reservoir adjacent to the dam and an inflow stream into Falling Creek Reservoir. The water column samples were collected approximately fortnightly from April-June, weekly from June-July and sporadically from July-October at Beaverdam Reservoir; weekly from April-July and fortnightly from July-November at Carvins Cove Reservoir; sporadically from April-August at Claytor Lake; weekly from April-November at Falling Creek Reservoir; fortnightly from April-October at Gatewood Reservoir; fortnightly from May-November at Spring Hollow Reservoir; and fortnightly from May-October at Smith Mountain Lake.
Global nitrous oxide emissions from rivers, lakes, and reservoirs during 1850-2019
<p>Data for paper "Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era", accepted at Nature Communications 2023.</p><p>Data are in ascii-format at a spatial resolution of 30 arcmin. Header for ascii-format files - ncols: 720 - nrows: 354 - xllcorner: -180 - yllcorner: -88.5 - cellsize: 0.5 - NODATA_value: -9999. The unit of the datasets is g N /yr.</p>
Field investigations of salt partitioning and aqueous chemistry of freezing closed-basin lakes in Mongolia as terrestrial analogs of subsurface brine reservoirs on icy bodies [Data set]
<p>All measurement and calculation data</p> <p>Measurement and calculation data in Version 4 were uploaded in 2021-11-2</p>
FIGURES 10–13 in A new genus and new species of Baetidae (Ephemeroptera) from lakes and reservoirs in eastern North America
FIGURES 10–13. Waynokiops dentatogriphus, new genus, new species. 10. Mesothoracic leg. 11. Tarsal claw; note double row of denticles restricted to proximal quarter of claw. 12. Gill VI. 13. Mesothoracic leg, patellotibial region.
FIGURES 7–9 in A new genus and new species of Baetidae (Ephemeroptera) from lakes and reservoirs in eastern North America
FIGURES 7–9. Waynokiops dentatogriphus, new genus, new species. 7. Labial palp. 8. Dorsal view, whole. 9. Lateral view, whole.
FIGURES 1–6 in A new genus and new species of Baetidae (Ephemeroptera) from lakes and reservoirs in eastern North America
FIGURES 1–6. Waynokiops dentatogriphus, new genus, new species. 1. Head, anterior view, cleared and with mouthparts removed. 2. Labrum. 3. Right (planate) mandible. 4. Left (angulate) mandible. 5. Maxilla. 6. Labium.
Carbon dioxide emissions from temperate reservoirs and pit lakes of different trophic states - Data
<p>This dataset contains results from the associated revised manuscript, as well as the data that has been used to obtain the results. </p> <p>The long-term data (monitoring data) was provided by two waterbody management agencies: a federal reservoir agency (Landestalsperrenverwaltung Sachsen – LTV, https://www.wasserwirtschaft.sachsen.de/) and a private company (Lausitzer und Mitteldeutsche Bergbau-Verwaltungsgesellschaft mbH – LMBV, https://www.lmbv.de/). </p> <p>This data can only be used with explicit approval of the respective management organization. </p>
FIGURE 4 in Biogeography and co-occurrence of 16 planktonic species of Keratella Bory de St. Vincent, 1822 (Rotifera, Ploima, Brachionidae) in lakes and reservoirs of the United States
FIGURE 4. Co-occurrence matrix for ten Keratella species. All other taxon pair combinations showed no significant negative or positive co-occurrence correlations. (Abbreviations: Amer = K. americana, Tect = K. tecta, Quad = K. quadrata, Taur = K. taurocephala, Trop = K. tropica, Cras = K. crassa, Disp = K. quadrata dispersa, Earl = K. earlinae, Test = K. testudo, Coch = K. cochlearis)
FIGURE 2 in Biogeography and co-occurrence of 16 planktonic species of Keratella Bory de St. Vincent, 1822 (Rotifera, Ploima, Brachionidae) in lakes and reservoirs of the United States
FIGURE 2. Distributions of 15 species of Keratella within nine agglomerated ecoregions of the continental U.S. Each circle represents one sample; circle size represents total biomass (µg dry weight L-1) for that species in a sample. Biomass values for each species are set to individual scales.
FIGURE 1 in Biogeography and co-occurrence of 16 planktonic species of Keratella Bory de St. Vincent, 1822 (Rotifera, Ploima, Brachionidae) in lakes and reservoirs of the United States
FIGURE 1. Dorsal views of the loricas of 15 species of Keratella. a) Keratella americana, b) Keratella cochlearis, c) Keratella crassa, d) Keratella earlinae, e) Keratella mixta, f) Keratella testudo, g) Keratella hiemalis, h) Keratella quadrata, i) Keratella quadrata dispersa, j) Keratella serrulata, k) Keratella taurocephala, l) Keratella tecta, m) Keratella ticinensis, n) Keratella tropica, o) Keratella valga. Scale bar represents 50 µm.
Data associated with article "Bulk Transfer Coefficients Estimated from Eddy-Covariance Measurements Over Lakes and Reservoirs" by Guseva et al., 2022
<p>The data includes <strong>(a)</strong> the general information about the lakes and reservoirs under study (e.g., lake surface area, lake mean and maximum depth); <strong>(b)</strong> the publications and data repository references for each individual lake or reservoir where we took the original datasets from (for details, see the article); <strong>(c)</strong> the number of data points (for the estimated bulk transfer coefficients) and filters applied to each dataset. ('<em>Table_Data_Bulk_Transfer_Coeff.docx</em>')</p> <p>In addition, we attach the derived quantities for each lake and reservoir that we analyzed in our manuscript: the neutral bulk transfer coefficients of <strong>(a)</strong> momentum (the drag coefficient); <strong>(b)</strong> heat (the Stanton number); <strong>(c)</strong> water vapor (the Dalton number). ('<em>Data_Bulk_Transfer_Coeff.xlsx</em>')</p> <p><strong><em>Update 22.11.2022</em></strong>: After the first round of revisions we upload the new version of the data since we had to recalculate the transfer coefficients. <strong>(1)</strong> We added the median values of the transfer coefficients; <strong>(2)</strong> we added the transfer coefficients accounting for gustiness. ('<em>Data_Bulk_Transfer_Coeff.xlsx</em>')</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.