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Figure 5 in Variations in trophic niches of generalist predators with plant community composition as indicated by stable isotopes and fatty acids

Figure 5. Principal components analysis of the relative abundance (mol %, logit- transformed) of individual NLFAs of Trochosa ruricola using body size (small, large), flooding index (FI), plant species richness (SR), plant functional group richness (FG), presence of grasses (Gr), legumes (Leg), small herbs (SH) and tall herbs (TH) as supplementary variables.

opencc-by-4.0Jul 2019View details →
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Figure 4 in Variations in trophic niches of generalist predators with plant community composition as indicated by stable isotopes and fatty acids

Figure 4. Variations in δ15N signatures of Trochosa ruricola as affected by flooding index (P = 0.04, R2 = 0.12) and body size (small, large; P <0.01).

opencc-by-4.0Jul 2019View details →
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Figure 1 in Variations in trophic niches of generalist predators with plant community composition as indicated by stable isotopes and fatty acids

Figure 1. Variations in δ15N and δ13C signatures of Harpalus rufipes (black) and Trochosa ruricola (pink) across the study site of the Jena Experiment.

opencc-by-4.0Jul 2019View details →
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Figure 3 in Variations in trophic niches of generalist predators with plant community composition as indicated by stable isotopes and fatty acids

Figure 3. Principal components analysis of the relative abundance (mol %, logit-transformed) of individual NLFAs of Harpalus rufipes using flooding index (FI), plant species richness (SR), plant functional group richness (FG), presence of grasses (Gr), legumes (Leg), small herbs (SH) and tall herbs (TH) as supplementary variables.

opencc-by-4.0Jul 2019View details →
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Figure 6 in Variations in trophic niches of generalist predators with plant community composition as indicated by stable isotopes and fatty acids

Figure 6. Principal components analysis of the relative abundance (mol%, logit- transformed) of individual PLFAs of soil microorganisms using flooding index (FI), plant species richness (SR), plant functional group richness (FG), presence of grasses (Gr), legumes (Leg), small herbs (SH) and tall herbs (TH) as supplementary variables.

opencc-by-4.0Jul 2019View details →
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Figure 2 in Variations in trophic niches of generalist predators with plant community composition as indicated by stable isotopes and fatty acids

Figure 2. Variations in δ15N signatures of Harpalus rufipes (P <0.01, R2 = 0.11) as affected by plant species richness (log-transformed).

opencc-by-4.0Jul 2019View details →
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Fig. 3 in Impact Of Coastal Wetland Restoration Strategies In The Chongming Dongtan Wetlands, China: Waterbird Community Composition As An Indicator

Fig. 3. Densities of Charadriidae (a), Anatidae (b), Ardeidae (c), and Laridae (d) among autumn, winter and spring in four sites. Error bars represent ±1 SE.

opencc-by-4.0Dec 2014View details →
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Рис. 2. Река БоΛьшая Пёра, ниже устья реки Юхта Fig. 2. The Bolshaya Pyora River, below the mouth of the Yukhta River in The Taxonomic Composition And Quantitative Indicators Of Zoobenthos In The Downstream Of The Bolshaya Pyora River (Zeya River Basin, Amur Region)

Рис. 2. Река БоΛьшая Пёра, ниже устья реки Юхта Fig. 2. The Bolshaya Pyora River, below the mouth of the Yukhta River

opencc-by-4.0Dec 2020View details →
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Рис. 1. Карто-схема реки БоΛьшая Пёра с указанием мест отбора проб (обозначены кружками) Fig. 1. Maps of the Bolshaya Pyora River with sampling locations (marked by circles) in The Taxonomic Composition And Quantitative Indicators Of Zoobenthos In The Downstream Of The Bolshaya Pyora River (Zeya River Basin, Amur Region)

Рис. 1. Карто-схема реки БоΛьшая Пёра с указанием мест отбора проб (обозначены кружками) Fig. 1. Maps of the Bolshaya Pyora River with sampling locations (marked by circles)

opencc-by-4.0Dec 2020View details →
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SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.

opencc-by-4.0Mar 2023View details →
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SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P&lt;0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.

opencc-by-4.0Mar 2023View details →
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Рис. 1. Карта-схема распоΛожения станций отбора проб на р. Амазар Fig.1. Location of sampling stations on the Amazar River in Species Composition And Quantitative Indicators Of Rotifers And Crustaceans In The Middle And Lower Streams Of The Amazar River (Zabaikalskiy Kray)

Рис. 1. Карта-схема распоΛожения станций отбора проб на р. Амазар Fig.1. Location of sampling stations on the Amazar River

opencc-by-4.0Sep 2019View details →
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Рис. 1. Карта-схема заповеΑника «КомсомоΛьский» с указанием станций отбора проб зообентоса (по: http://www.zapovedamur.ru) Fig. 1. Map of the Komsomolsky Reserve with indication of zoobenthos sampling stations (after: http://www.zapovedamur.ru) in Taxonomic composition of benthic invertebrates of the Komsomolsky Nature Reserve watercourses (Khabarovsky Region)

Рис. 1. Карта-схема заповеΑника «КомсомоΛьский» с указанием станций отбора проб зообентоса (по: http://www.zapovedamur.ru) Fig. 1. Map of the Komsomolsky Reserve with indication of zoobenthos sampling stations (after: http://www.zapovedamur.ru)

opencc-by-4.0Dec 2023View details →
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Fig. 3 in Call survey indicates rainbow trout farming alters glassfrog community composition in the Andes of Ecuador

Fig. 3. Map of the study area. Inset: Pichincha Province, Ecuador. Main: Blue points indicate non-trout farm sites whereas red points indicate trout farm sites. Yellow line represents the equator (latitude 0).

opencc-by-4.0Apr 2020View details →
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Fig. 2 in Call survey indicates rainbow trout farming alters glassfrog community composition in the Andes of Ecuador

Fig. 2. Glassfrog species found during surveys. (A) Centrolene heloderma, (B) Centrolene ballux, (C) Esparana prosoblepon, (D) Nymphargus lasgralarias, (E) Centrolene peristictum, (F) Nymphargus grandisonae, (G) Centrolene lynchi, (H) Egg mass from C. ballux. Nymphargus griffithsi was not encountered during the 2017 survey but has been documented at Kathy's creek in 2012 and 2013 (by Jane A. Lyons). Photographs by Dana G. Wessels (A–F) and Timothy J. Krynak (G–H).

opencc-by-4.0Apr 2020View details →
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Fig. 1 in Call survey indicates rainbow trout farming alters glassfrog community composition in the Andes of Ecuador

Fig. 1. Trout farming in the Mindo region of Ecuador utilizes a flow-through aquaculture technique. Stream water is diverted into tandem raceways/holding reservoirs and then flows through these reservoirs back into the natural stream system. This figure displays a panoramic view of Finca de Jaime's (FJ) set-up. Photograph by Katherine L. Krynak.

opencc-by-4.0Apr 2020View details →
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Fig. 4 in Call survey indicates rainbow trout farming alters glassfrog community composition in the Andes of Ecuador

Fig. 4. Two-dimensional NMDS ordination of survey sites and glassfrog species based upon presence of frogs audibly documented in 2017 survey conducted in the Mindo region of Ecuador (Stress = 3%). Red points and labels represent glassfrog species; grey points represent trout farms; and black points represent non-trout farms. RSR = Río Santa Rosa, LC = Lucy's Creek, Bcrk = Ballux Creek, KC = Kathy's Creek, M = Michelle's, C = tributary of the Chalguayacu Grande River, 5F = Five Frog Creek, LRSR = Lower Río Santa Rosa, ST = Santa Teresita, FJ = Finca de Jaime, LS = La Sierra, VC = Verdecocha, EP = El Paraíso del Pescador. Trout farms EP, LS, and VC are not included in the analysis because glassfrogs were not observed at these sites. A significant difference in glassfrog community composition between trout farm and non-trout farm sites was indicated by MRPP (delta = 0.59, A = 0.11, P = 0.03). NMDS1 correlated with elevation; NMDS2 correlated with: percent canopy openness, dissolved oxygen (mg/L), total dissolved solids (mg/L), and conductivity (µS).

opencc-by-4.0Apr 2020View details →
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Social vulnerability to flooding in Ecuador : input variables, PCA vs Expert composite indices

<p><strong>Social vulnerability indices are used to better understand and predict the consequences of disasters, and support the development of improved disaster management policies. This research specifically supports the Ecuadorian Red Cross in generating a flood-specific social vulnerability index to inform flash flood early action protocol.</strong></p> <p>The&nbsp;dataset presents the results from the analysis of the&nbsp;social vulnerability to flooding in Ecuador, from individual input variables to&nbsp;the composite indices&nbsp;outputs. The results are available at the Parroquia level in Ecuador (admin level 3),&nbsp;for 1032 Parroquia excluding the Galapagos Islands.</p> <ul> <li>The dataset comprises, for each Parroquia, the&nbsp;estimation&nbsp;of <strong>15 variables characterizing the social vulnerability to flooding specific to Ecuador context</strong>. The variables are selected from literature review and consultation with Ecuadorian&nbsp;Red Cross disaster practitioners : <em>Disability, Poverty incidence, Gini Index, Agricultural labor share, Vectorborne disease incidence, Waterborne disease incidence, Social Security affiliation, Education level, Sanitation, Driking water access, Power access, Road travel time, Wall structure, Mobile access and Internet access.</em> All variables are normalized from 0 to 1,&nbsp;directed toward increasing vulnerability, and renamed accordingly.</li> <li>In addition, the <strong>Administrative level names, PCODE, calculated Area, population density,</strong> as well as related&nbsp;<strong>sub-regions</strong> are also referenced.</li> <li>Individual variables are integrated into <strong>composite vulnerability indices</strong>, using two different approaches:&nbsp; i) the Principal Component Analysis approach, using the first component <strong>PCA(n=1)&nbsp;</strong>and the first 5 components <strong>PCA(n=5)</strong> separately ; ii) the <strong>expert judgement weighting</strong> of the variables. The output composite indices, normalized from 0 to 1&nbsp;are presented in 3 separated columns.</li> </ul> <p>&nbsp;</p>

opencc-by-nc-nd-4.0Sep 2021View details →
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Fig. 1 in Impact Of Coastal Wetland Restoration Strategies In The Chongming Dongtan Wetlands, China: Waterbird Community Composition As An Indicator

Fig. 1. Locationofthestudysites (A–D).

opencc-by-4.0Dec 2014View details →
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AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites

<p>AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites</p> <p>The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. &nbsp;</p> <p>Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.&nbsp;</p> <p>All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. &nbsp;</p> <p>Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., &amp; Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). &nbsp;</p> <p>More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:&nbsp;</p> <p>Vegetation Indices&nbsp;</p> <p>(Atmospherically Resistant Vegetation Index, Kaufman 1972)&nbsp;</p> <p>(Enhanced Vegetation Index, Huete 2002):&nbsp;</p> <p>(Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)&nbsp;</p> <p>(Normalized Difference Chlorophyll Index, Mishra &amp; Mishra, 2012)&nbsp;</p> <p>(Normalised Difference Moisture Index, Gao 1996)&nbsp;</p> <p>(Normalized Difference Vegetation Index, Rouse 1973)&nbsp;</p> <p>(Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)&nbsp;</p> <p>(Simple Ratio Vegetation Index Jordan, C.F.1 969)&nbsp;</p> <p>(Soil Adjusted Vegetation Index, Huete 1988)&nbsp;</p> <p>(Visible Atmospherically Resistant Index, Gittleson 2002)&nbsp;</p> <p>&nbsp;&nbsp;</p> <p>Built-up Indexes&nbsp;</p> <p>(Band Ration For Built-up Area, Waqar 2012)&nbsp;</p> <p>(Built-up Area Extraction Index, Bouzekri 2015)&nbsp;</p> <p>(Built-up Index, He Et Al. 2010)&nbsp;</p> <p>(Index-based Built-up Index, Xu 2008)&nbsp;</p> <p>(New Built-up Index, Jieli Et Al. 2010)&nbsp;</p> <p>(Normalized Difference Built-up Index, Zha 2003)&nbsp;</p> <p>(Normalized Built-up Area Index, Waqar 2012)&nbsp;</p> <p>(Urban Index, Kawamura 1996)&nbsp;</p> <p>&nbsp;&nbsp;</p> <p>Water Indices&nbsp;</p> <p>(Modified Normalized Difference Water Index, Xu 1996)&nbsp;</p> <p>(Normalized Difference Water Index, Mcfeeters 1996)&nbsp;</p> <p>(Water Index, Fisher 2016)&nbsp;</p> <p>&nbsp;&nbsp;</p> <p>Other Indices&nbsp;</p> <p>(Bare Soil Index, Rikimaru Et Al. 2002)&nbsp;</p> <p>(Bare Soil Index, Wanhui 2004)&nbsp;</p> <p>(Burn Area Index, Martin 1998)&nbsp;</p> <p>(Clay Minerals Ratio, Drury 1987)&nbsp;</p> <p>(Ferrous Minerals Ratio, Segal 1982)&nbsp;</p> <p>(Iron Oxide Ratio, Segal 1982)&nbsp;</p> <p>(Normalized Burn Ratio, Lopez Garcia 1991)&nbsp;</p> <p>(Normalised Difference Snow Index, Hall 1995).&nbsp;</p>

opencc-by-4.0Jul 2022View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record