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Figure 3 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 3. Clustering heatmap of different expression phosphorylated peptides. Each row represents a phosphorylated peptide segment, each column represents a group of samples. The logarithmic value (logarithmic transformation based on 2) of the significantly differentially expressed phosphorylated peptides in different samples is displayed in the clustering heatmap in different colors. Red represents significant upregulation of phosphorylated peptides; blue represents significant down-regulation of phosphorylated peptides.
Figure 2 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 2. Volcano plots from different group comparisons. The abscissa indicates difference multiple (logarithmic transformation based on 2), the ordinate indicates the significant of difference (logarithmic transformation based on 10). The red point is significantly upregulated phosphorylated peptide segment, the blue point is significantly downregulated phosphorylated peptide segment and the gray point is a phosphorylated peptide segment with no significant difference.
Figure 2 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 2. The total number of non-native and cryptogenic species recorded at each monitoring location (2012–2017).
Fig. 1 in Fisheries monitoring in Babel: fish ethnotaxonomy in a hotspot of common names
Fig. 1. Map of the study region showing the location of Cumuruxatiba within the Marine Extractive Reserve of Corumbau, Bahia, Northeastern Brazil.
Fig. 7. Correspondence between the 89 in Fisheries monitoring in Babel: fish ethnotaxonomy in a hotspot of common names
Fig. 7. Correspondence between the 89 names given by 14 Cumuruxatiba fishers to fresh specimen photographs' shown six months later. Numbers represent the number of species.
Fig. 4 in Fisheries monitoring in Babel: fish ethnotaxonomy in a hotspot of common names
Fig. 4. Richness of common names by species for the main fishing gears utilized in Cumuruxatiba, Bahia.
Fig. 3 in Fisheries monitoring in Babel: fish ethnotaxonomy in a hotspot of common names
Fig. 3. Collector's curve: cumulative number of species according to the number of landings sampled in Cumuruxatiba, Bahia.
Fig. 5 in Fisheries monitoring in Babel: fish ethnotaxonomy in a hotspot of common names
Fig. 5. Number of fish species captured by each fishing gear used by Cumuruxatiba fisherfolks (n = 141 landings).
Fig. 6 in Fisheries monitoring in Babel: fish ethnotaxonomy in a hotspot of common names
Fig. 6. Richness of common names of marine species in Cumuruxatiba, Bahia. a) frequency of species with 0-5 common names, and b) frequency of common names matching 1-4 species.
Fig. 2 in Heracleum Sosnowskyi Manden. Monitoring In Protected Areas - A Case Study In Rēzekne Municipality, Latvia
Fig. 2. Two studied sites in Rāzna National Park in Rēzekne municipality, Mākoņkalna village, Jegorova. Polygon No. 14 - the largest increase of Heracleum sosnowskyi cover from 2012 to 2016 among the studied sites, Polygon No. 15 – the largest new site found in 2016 (Table 2). Yellow stripes - H. sosnowskyi cover in 2012, purple colour – H. sosnowskyi cover in 2016 (Author: Elīna Tripāne).
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
Biofouling sponges as natural eDNA samplers for marine vertebrate biodiversity monitoring
<p>These are the raw sequencing data and associated analysis codes for the study of "biofouling sponges as natural eDNA samplers for marine <span>vertebrate </span>biodiversity monitoring".</p>
FIGURE 7 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 7. Boxplot of yield difference over area index by hiatus class. Note the median for each hiatus class is close to zero. This indicates similar amounts of collection between the earlier year and the later year regardless of the number of years the area has been left to erode.
FIGURE 6. Map showing estimated collection area. Points are field localities and 15 meter buffer shows estimated prospecting areas for 2008 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 6. Map showing estimated collection area. Points are field localities and 15 meter buffer shows estimated prospecting areas for 2008 (yellow), 2009 (green), and 2010 (blue). Precise locality information is available to qualified researchers upon request from JODA's museum program.
FIGURE 9. All field collections from 2013 - early 2019 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 9. All field collections from 2013 - early 2019 to be used for tracking previous collection area. Points outside of JODA boundaries are BLM, USFS, or private localities. Precise locality information is available to qualified researchers upon request from JODA's museum program.
Figure 8 in Satellite Monitoring of the Black Sea Ecological Risk Areas
Figure 8. Areas of environmental risk in the Black Sea, identified on the basis of satellite remote sensing data: 1 - Istanbul-Varna shipping route; 2 - Istanbul-Constanta shipping route; 3 - Istanbul-Odessa shipping route; 4 - Istanbul - Kerch Strait and Istanbul – Novorossiysk shipping routes; 5 - shipping routes leading to the ports of Georgia; 6 - water area near the Danube Delta; 7 - area of intense algal bloom on the northwestern shelf; 8 - Sevastopol Bay; 9 - Kerch Strait; 10 - Tsemesskaya Bay; 11 - Gelendzhik Bay; 12 - the Port of Tuapse; 13 - water area near Sochi; 14 - area of intensive methane seeps; 15 - coastal zone of the extreme eastern part of the Black Sea.
Figure 5 in Satellite Monitoring of the Black Sea Ecological Risk Areas
Figure 5. Natural oil slicks in the southeastern Black Sea.Upper left: Envisat ASAR, 27.10.2010, 07:32 UTC; Upper right: Sentinel-1A SAR, 18.07.2016, 03:16 UTC; Bottom: a generalized scheme of oil pollution of the sea surface over the region of natural hydrocarbon seeps from the bottom in the southeastern part of the Black Sea.
Figure 3 in Satellite Monitoring of the Black Sea Ecological Risk Areas
Figure 3. Manifestation of oil contamination at the SAR images. Left: Oil pollution at the anchorage of the Port of Odessa. ASAR Envisat on 02.09.2011, 08:10 UTC; Right: Oil pollution on the navigable route, directed towards Sevastopol. ASAR Envisat on 17.07.2011, 19:50 UTC.
Figure 7 in Satellite Monitoring of the Black Sea Ecological Risk Areas
Figure 7. Distribution of polluted waters under the influence of anticyclonic vortices in the coastal zone of the Black Sea. The upper left: suspended matter concentration from Envisat MERIS data obtained on 07.10.2011; The upper right: pollution on the sea surface on 10.11.2013 (photo from shipboard); Bottom: the scheme of spread of pollution on 10.11.2013.
Figure 2 in Satellite Monitoring of the Black Sea Ecological Risk Areas
Figure 2. Oil pollution near the straits. Left: Oil pollution at the entrance to the Bosphorus; ASAR Envisat on 02.10. 2011, 08:11 UTC. The area of the spot is 3.2 km2. Right: Oil pollution in the Kerch Strait. SAR Sentinel-1A from 07.09.2016, 03:40 UTC.
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.