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10,016 results for “Observation”
Text-fig. 7. Occurrence of p1 in mandibles from Deninger bears and cave bears, data after Table 3 (presence = p1 or alveoli observed, absence = no p1 developed, broken = diastema fragmented). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 7. Occurrence of p1 in mandibles from Deninger bears and cave bears, data after Table 3 (presence = p1 or alveoli observed, absence = no p1 developed, broken = diastema fragmented).
Figs. 1–2. Lisposoma josehermana Lamoral, 1979 in New Records and Observations on the Natural History of Lisposoma elegans and L. josehermana (Scorpiones: Bothriuridae)
Figs. 1–2. Lisposoma josehermana Lamoral, 1979, adult?, in life. 1. Natural light. 2. Ultraviolet light. Scale bar 5 10 mm.
Fig. 9 in New Records and Observations on the Natural History of Lisposoma elegans and L. josehermana (Scorpiones: Bothriuridae)
Fig. 9. Map showing the known distributions of Lisposoma elegans Lawrence, 1928 (n) and Lisposoma josehermana Lamoral, 1979 (m) in Namibia (contour interval 600 m; major sand systems stippled).
Figure 1 Micrographs showing a in Morphological observations of fluorescence in different tick species (Family: Ixodidae)
Figure 1 Micrographs showing a dorsal view of Dermacentor variabilis (male) to compare each light source. White light (left), royal blue (RB, 440-460 nm) (middle) and ultraviolet (UV, 360-380 nm) (right). Variation in ornamentation illuminated by the different light sources can
FIG. 1 in Observations on Dactylocephala madagascariensis (Vayssière, 1892), a temnocephalan with twelve tentacles from Madagascar
FIG. 1. — Dactylocephala madagascariensis; A, whole cirrus; B, Introvert partly everted. Scale bars: A, 50 µm; B, 20 µm.
Fig. 4 in Intertidal life: field observations on the clingfish Gobiesox barbatulus in southeastern Brazil
Fig. 4. Percent composition of food items found in stomachs of 11 Gobiesox barbatulus individuals: (a) broad taxonomic categories; (b) only crustaceans and unidentified organic material.
Fig. 3 in Intertidal life: field observations on the clingfish Gobiesox barbatulus in southeastern Brazil
Fig. 3. Standard movements performed by Gobiesox barbatulus before leaving the "shelter-stone": (a) sequence 1-8 illustrates a round over the "shelter-stone"; (b) body rotation under the "shelter-stone".
Fig. 2 in Intertidal life: field observations on the clingfish Gobiesox barbatulus in southeastern Brazil
Fig. 2. Microhabitat use by Gobiesox barbatulus in the Baleeiro Isthmus (n = 51) vs. Saco Grande (n = 13). Size in cm.
Fig. 6. Courtship movements recorded for a in Intertidal life: field observations on the clingfish Gobiesox barbatulus in southeastern Brazil
Fig. 6. Courtship movements recorded for a pair of Gobiesox barbatulus in the wild. Each cycle did not exceed 30 min of duration, and always started with one of the three "pre-mating" positions [i.e., (a, e, and h)]. After mating the pair did not stay in contact (b or f), and the larger individual performed parental behavior (b). They restarted the cycle after moving to a spot without eggs, following new approximation (c, d, and g). It is important to note that there are many sequences possible based in such events, since they can alternate.
Fig. 1 in Intertidal life: field observations on the clingfish Gobiesox barbatulus in southeastern Brazil
Fig. 1. Map indicating the location of the study area (São Sebastião Channel) on the coast of São Paulo, southeastern Brazil, and the scanned regions into the marine protected area of CEBIMar-USP (the marine station of the University of São Paulo): (a) pebbles field at the Baleeiro Isthmus; (b) pebbles, cobblestones and boulder field of Saco Grande; (c, d) examples of other scanned regions at the continental margin of the São Sebastião Channel with pebbles and/or tide pools. The big black arrow shows a detail of the Gobiesox barbatulus habitat at the intertidal zone.
Zebra finch song and distance call amplitude measurements: A transmission experiment and observational transects in the natural environment
<p>Birdsong is typically seen as a long-range signal functioning in mate attraction and territory defense. Among birds, the zebra finch is the prime model organism in bioacoustics, yet almost exclusively studied in the lab. In the wild, however, zebra finch song differs strikingly from songbirds commonly studied in the wild as zebra finch males sing most after mating and in the absence of territoriality. Using data from the wild, we here provide an ecological context for a wealth of laboratory studies. By integrating calibrated sound recordings, sound transmission experiments and social ecology of zebra finches in the wild with insights from hearing physiology we show that wild zebra finch song is a very short-range signal with an audible range of about nine meters and that even the louder distance calls do not carry much farther (up to about fourteen meters). These integrated findings provide an ecological context for the interpretation of laboratory studies of this species and indicate that the vocal communication distance of the main laboratory species for avian acoustics contrasts strikingly with songbirds that use their song as a long-range advertisement signal.</p>
SASC: A Simple Approach to Synthetic Cohorts. Applying COVID-19 clinical data to generate longitudinal observational patient cohorts and comparison with alternative synthetic cohort approaches as well as real patient data
<p>Subset from COVID-19 Dataset from https://zenodo.org/record/3766350#.YVcfyTFBxgA. Used as reference for a publication dealing with synthetic patient cohort generation.</p>
MICA - Muskrat and coypu camera trap observations in Belgium, the Netherlands and Germany
<p><em>MICA - Muskrat and coypu camera trap observations in Belgium, the Netherlands and Germany</em> is a camera trap observations dataset published by the <a href="https://inbo.be">Research Institute of Nature and Forest (INBO)</a>. It is part of the <a href="https://lifemica.eu/">LIFE project MICA</a>, in which innovative techniques are tested for a more efficient control of muskrat and coypu populations, both invasive species. The dataset contains camera trap observations of muskrat and coypu, as well as many other observed species.</p> <p>Data in this package are exported from the camera trap management system Agouti (<a href="https://agouti.eu">https://agouti.eu</a>) and formatted as a <a href="https://tdwg.github.io/camtrap-dp/">Camera Trap Data Package (Camtrap DP)</a>.</p> <p><strong>Files</strong></p> <p>Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/record/5590881/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json</strong>: technical description of the data files.</li> <li><strong>deployments.csv</strong>: camera trap deployments. Includes <code>deploymentID</code>, start, end, location and camera setup information.</li> <li><strong>media.csv</strong>: media files (images/videos) captured by the camera traps. Associated with deployments (<code>deploymentID</code>) and organized in sequences (<code>sequenceID</code>). Includes timestamp and file path.</li> <li><strong>observations.csv</strong>: observations based on the media files. Associated with deployments (<code>deploymentID</code>) and sequences (<code>sequenceID</code>). Observations can mark non-animal events (camera setup, human, blank) or one or more animal observations (<code>observationType</code> = <code>animal</code>) of a certain taxon, count, age, sex, behaviour and/or individual.</li> </ul>
Tsunami Observation Points Database in Eastern Mediterranean
<p>Knowledge of the tsunami observation points of past tsunamis is of great importance since it may help in studies of several kinds, such as better understanding of the source type, determination of the inundation area, and verification of tsunami simulation results. </p> <p>Plenty of information is available about the Tsunami Observation Points (TOPs) of many historical and modern tsunamis observed in Greece and in other areas of the eastern Mediterranean region. The TOPs Database is a systematic compilation of such kind of information. For a specific tsunami event, the TOPs DB includes the source epicenter, the TOPs names and the corresponding geographical coordinates, the names of the localities, and a characterization of the tsunami intensity level, K, in each TOP. K has been estimated according to the 12-grade tsunami intensity scale by Papadopoulos and Imamura (2001). </p> <p>In its current form the TOPs DB provides information about 61 TOPs related to the 9 July 1956 large seismic tsunami generated in the south Aegean Sea. Information about the TOPs and the impact of the wave in each TOP can be found in the publications by Galanopoulos (1957), Ambraseys (1960), Papadopoulos et al. (2005) and Okal et al. (2009). The tsunami intensities have been estimated by Dr G.A. Papadopoulos and Dr I. Triantafyllou. Earthquake source parameters have been adopted from the ISC-GEM (2023) catalogue. </p> <p>The TOPs DB will be gradually expanded with the aim to finally include all the known tsunami events to have occurred in Greece and in other eastern Mediterranean regions in modern and historical times. </p>
Integrated Effect Database for Toxicological Observations (INTOB)
<p>This dataset contains morphological observations of zebrafish embryos from toxicological experiments from the department of Ecotoxicology at the Helmholtz-Center for Environmental Research (UFZ). Data was recorded via <a href="https://www.ufz.de/intob/index.php?en=51326">INTOB</a>, a software to collect and manage data and metadata from toxicological observations.</p> <p>The dataset contains two directories, <code>data/</code>which contains experimental data, and <code>scripts/</code> which contains all code needed to run the analysis described in the paper, as well as a compiled HTML file. <code>results/</code> contains a table with EC50 and LC50 values as visualised in Figure 3 of the paper.</p> <p><code>data/intob_data/</code> contains several csv files:</p> <ul> <li><code>effect_list_info.csv</code> contains all possible effects of the database and their relation to each other</li> <li><code>obs_phenotypes.csv</code> contains all individual observations</li> <li><code>metadata.csv</code> lists metadata about the experiments and substances</li> <li><code>plate_layout.csv</code> contains experimental setups, i.e. layouts of substances and concentrations in well-plates or vials</li> <li><code>phys_chem_prop.csv</code> contains the physico-chemical observations of the substances</li> </ul> <p><code>data/data_for_analysis/</code> contains files required in the script and <code>data/zotero_phenotype_library/</code> contains the results of the literature review described in the paper in JSON format.</p>
Training Deep Learning Models to Estimate SWAT Parameters using Streamflow Observations
<p>This folder provides the simulation and observational data</p> <p>Simulation data using SWAT (1000 realz)<br> Train, Val, and Test splits (80/10/10)<br> Observational data for ARW (WY2000-2016)</p>
PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTDOC Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTDOC instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTGJON Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTGJON instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
Raw Data and PCA Filtering of Apache Point Observatory NMSU 1m StellaCam Observations of LCROSS
<p>This archive contains the raw data and data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the StellaCam instrument on the Apache Point Observatory NMSU 1m telescope.</p> <p>Full details about the raw data are available in Chanover, N. J. et al. Results from the NMSU-NASA Marshall Space Flight Center LCROSS observational campaign. <em>J. Geophys. Res. (Planets)</em> <strong>116</strong>, E08003 (2011). <a href="https://doi.org/10.1029/2010JE003761">https://doi.org/10.1029/2010JE003761</a></p> <p>We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Apache Point Observatory 3.5m Agile Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Agile instrument on the Apache Point Observatory 3.5m telescope. We use principal component analysis (PCA) filtering both to improve the coregistration of the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
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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.
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.