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10,016 results for “Observation”
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-03]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-05]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
Data for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"
<p>Data for the main figures for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"</p>
Data underlying Frailty in people with rheumatoid arthritis – A systematic review of observational studies
<p>Abstract screening results, full text screening results, and data extraction documents for Frailty in people with rheumatoid arthritis – A systematic review of observational studies</p>
Observational Astrophysics II at Stockholm University
<p>Observing with the Nordic Optical Telescope has, for many years, been an integral part of the course Observational Astrophysics II (ObsII) offered to masters and Ph.D. students at Stockholm University. I will review the activities related to the course in Stockholm, which also includes observations with the 20m radio telescope at the Onsala Space Observatory.</p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-04]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
Observation‐based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models
<p><span>Soil heterotrophic respiration (R<sub>h</sub>) refers to the flux of CO2 released from soil to atmosphere as a result of organic matter decomposition by soil microbes and fauna. As one of the major fluxes in the global carbon cycle, the estimation of global R<sub>h</sub> still exists large uncertainties, which further limited our current understanding of the carbon accumulation in soils. Here, we applied a Random Forest algorithm to create a global dataset of soil R<sub>h</sub>, by linking 761 field observations with both abiotic and biotic predictors. We estimated that the global R<sub>h</sub> was 48.8 ± 0.9 Pg C yr<sup>-1</sup> for 1982–2018, which was 16% less than the ensemble mean (58.6 ± 9.9 Pg C yr<sup>-1</sup>) of 16 terrestrial ecosystem models. By integrating our observational R<sub>h</sub> with independent soil carbon stock datasets, we obtained a global mean soil carbon turnover time of 38.3 ± 11 yr. Using observation-based turnover times as a constraint, we found that terrestrial ecosystem models simulated faster carbon turnovers, leading to a 30% (74 Pg C) underestimation of terrestrial ecosystem carbon accumulation for the past century, which was especially pronounced at high latitudes. This underestimation is equivalent to 45% of the total carbon emissions (164 Pg C) caused by global land use change at the same time. Our analyses highlight the need to constrain ecosystem models using observation-based and locally adapted R<sub>h</sub> values to obtain reliable predictions of the carbon sink capacity of terrestrial ecosystems. </span></p>
Observation of non-Hermitian topology in a multi-terminal quantum Hall device
<p>This repository contains our measurements and the codes used to produce the figures of the manuscript/supplementary information.</p>
FIGURE 5 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)
FIGURE 5. Graphical representation of the most important allometric growth changes in morphometric parameters as measured for the three subject specimens of Nothobranchius ocellatus, through the sub-adult to young sexually mature phases, at 37, 56 and 84 days after hatching. Data from Table 2.
FIGURE 6 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)
FIGURE 6. Comparative morphometry of growth in selected specimens of Nothobranchius ocellatus; a, Score plot of principal component analysis (PCA) on morphometric characters; first vs. second principal components: female #1 (diamond), male #2 (circle), male #4 (inverted triangle); b, loading plot of PCA for female #1; c, loading plot of PCA for male #2; d, loading plot of PCA for male #4. Most important loadings with absolute magnitude greater than 0.4 appear in bold. Key to character abbreviations: TL, Total length; BD, Body depth at pelvic–fin origin; HL, Head length; PA, Preanal length; PD, Predorsal length; PV, Prepelvic length; PP, Prepectoral length; CPL, Caudal peduncle length; CPD, Caudal peduncle depth; DFB, Dorsal-fin base length; AFB, Anal-fin base length; CF, Caudal-fin length; HD, Head depth; PO, Postorbital length; SD, Suborbital depth; ED, Eye diameter; SEL, Snout to eye end length; SL, Snout length.
FIGURE 4 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)
FIGURE 4. Sets of photographs (A, B and C) showing the development of sex distinction as reflected in changes in colour pattern, and some general morphological features, in the study specimens of Nothobranchius ocellatus at 37, 58 and 84 days age.
FIGURE 2 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)
FIGURE 2. Illustration of the method used for measuring the total length (TL) of Nothobranchius ocellatus specimens on a weekly basis. Specimens were placed in very shallow water in a clear, flat-bottomed glass dish (of a size suited to the size of the specimen) over graph paper and photographed from above. A straight line was then digitally drawn from the terminus of the upper jaw to the posterior margin of the caudal fin. The line was then rotated into alignment with the grid of the graph paper in order to closely approximate TL.
FIGURE 1 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)
FIGURE 1. Nothobranchius ocellatus: upper, wild-caught male from neotype locality (field code: Kikongono TAN 95-9); lower, wild-caught female (field code: Kitonga south TAN 97-36); Rufiji River drainage; central coastal region, Tanzania.
Dataset of "Telemedicine and its acceptance by patients with type 2 diabetes mellitus at a single care center during the COVID-19 emergency: A prospective observational study"
<p>Dataset of the article titled "Telemedicine and its acceptance by patients with type 2 diabetes mellitus at a single care center during the COVID-19 emergency: A prospective observational study”.</p> <p> </p> <p> </p>
Code and Data for "Bridging Gaps in the Climate Observation Network"
<p>The code and data used in the paper "Bridging Gaps in the Climate Observation Network: A Physics-based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Model"</p>
FIGURE 77 in Observations on the Biology of Afrotropical Hesperiidae (Lepidoptera). Part 5. Hesperiinae incertae sedis: Dicotyledon Feeders
FIGURE 77. Pupa of Platylesches moritili reared from a form gamma caterpillar collected 20 Mar 1988 on Parinari curatellifolia, Mukuvisi Woodland, Harare, Zimbabwe; 19mm; died; MJWC 88/24(1).
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-08]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-07]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
Observations of Jordanita species in Central Europe from the BioOffice database, Tyolean state museum
<p><span>The decline of biodiversity in general and of insect diversity in particular has been recognized as a major environmental problem in recent years. </span><span>The study of Markl et al., in which this dataset was used, reports the distribution and the decline of populations of forester moths of the genus Jordanita in Central Europe since 1950 as a type example of the loss of grassland biodiversity, and discusses the reasons for this decline. </span><span>Based on extensive work in museums and private collections, a literature review and own observations, and including data as far back as 1834, this genus helps to understand the deeper reasons of insect population and biodiversity decline, as the well investigated six Central European species cover a broad range of extensive grassland habitats (fens to low-production grassland and xerothermic steppes) from low altitudes to high alpine meadows. Therefore, they monitor processes relevant also to other, less investigated grassland species. </span></p>
Development of chronic pain after posterolateral and axillary approaches for thoracic surgery: A prospective monocentric observational study
<p class="MDPI17abstract"><span>Mini-invasive thoracotomies may reduce chronic</span><span> pain and pain-related functional impairment. The aims of part 2 of the study were to assess their incidence after posterolateral and axillary thoracotomies and to identify potential factors of chronic pain. Patients undergoing a posterolateral (79 patients) or axillary (79 patients) thoracotomy between July 2014 and November 2015 were analyzed in this prospective monocentric cohort study. At the 4-month assessment, more patients had chronic pain in the posterolateral group (60.8% vs 40.5%; p=0.017). There was a significant difference between groups favoring the axillary group: less level of catastrophism (p=0.021) and less impact of pain on daily activities (globally, p=0.009; routine activities subscore, p=0.010, chosen activity subscore, p=0.030). According to a multinomial logistic model multivariable, three factors are associated with the presence of chronic pain: the presence of pain before surgery (OR=2.36, CI 95% [1.03-5.39]), a large posterolateral incision (OR=4.14 [1.25-13.72]) and the intensity of pain during mobilization of the ipsilateral shoulder at postoperative day 6 (OR=1.37 [1.10-1.70]). In conclusion, chronic pain is less frequent after an axillary approach than after a posterolateral one. Identified risk factors for chronic pain should lead to more aggressive management of preoperative and early postoperative pain and favor the mini-invasive approach.</span></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.