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Рис. 2. А — Озеро Арейское, ЯбΛоновый хребет; Б — ГΛавный воΑоразΑеΛ ЗемΛи у озера Арейского Fig. 2. А — Lake Areyskoye and Yablonovy Ridge; Б — The Main divide of the Earth near the Lake Areyskoye in macroinvertebrates of the Lake Areyskoye
Рис. 2. А — Озеро Арейское, ЯбΛоновый хребет; Б — ГΛавный воΑоразΑеΛ ЗемΛи у озера Арейского Fig. 2. А — Lake Areyskoye and Yablonovy Ridge; Б — The Main divide of the Earth near the Lake Areyskoye
Data for: TOI-1685 b is a Hot Rocky Super-Earth (Burt et al. 2024)
<p>Machine Readable Tables containing the time series photometry and radial velocity data used in Burt et al. 2024 paper 'TOI-1685 b is a Hot Rocky Super-Earth: Updates to the Stellar and Planet Parameters of a Popular JWST Cycle 2 Target'</p> <p>Notes (also in ReadMe Document) </p> <p>TOI1685_Full_TimeSeries_Photometry_DataSet.dat : the juliet light curve input file for the transit fit presented in the paper.</p> <p>The nine columns in the file correspond to, Time (BJD_TDB), Normalized flux, Normalized flux error, Dataset label, Airmass, Centroid X offset, Centroid Y offset, PSF full-width half-maximum (FWHM) and PSF peak brightness. As these are not used to model the TESS light curves, these values are blank for the TESS data. For the MuSCAT2 data, entropy (a proxy for the PSF FWHM) is used in place of PSF FWHM, and PSF peak brightness is not used at set to 0. For the OMM data, total background across the target and comparison stars is used in place of PSF peak brightness. All values in the final five columns are normalized between -1 and 1 for any given dataset. </p> <p>~~~~~~~~~~~~~</p> <p>TOI1685_Full_RV_DataSet.csv : The full data sets from the MAROON-X data presented in this paper, as well as the CARMENES data from Bluhm et al. 2021 and the IRD data from Hirano et al. 2021. </p> <p>The fit presented in the paper only makes use of the RV time series from each instrument, but the SERVAL results for MAROON-X and CARMENES also provide a variety of activity indicators which we include as additional columns here for completeness.</p>
Figure 2. Screenshot of the Google Earth web-site's prototype on the visualization of heat/cold waves-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology-
<p>A non-anticipative analog method consists of four main steps:<br> 1. Generation of the prediction rules.<br> 2. Analysis of the prediction rules. The rules with time slots, which are not concentrated at<br> the same frame, are excluded.<br> 3. Generation of possible extremes.<br> 4. Analysis of the generated possible extremes. The extremes with time slots, which do not<br> correspond to the time slots of the appropriate rules, are excluded.<br> The results of the heat/cold waves’ prediction from 2011 to 2014 at different locations<br> (places are selected randomly) are presented in Table 2.</p>
Data of the publication: Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing
<p>Data corresponding to the figures of the publication "Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing" by N. Kunkel and Ph. Goldner (https://doi.org/10.1088/1361-648X/aa529a). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Orbital dynamic admittance and earth shadow
<p>The effects of orbital dynamics on a velocity distribution from a point source --- for example, a fragmentation --- may be studied by Housen's method. This technique permits the computation of a spatial density due to the velocity distribution as the sum over all routes between the source point and any point in space of the velocity density divided by the absolute value of the Jacobian determinant of the propagation map. The determination of all routes constitute finding all Lambert (orbital two point boundary value problem) solutions for the two points over a given elapsed time. In order to understand the observed density structures better, the dynamic admittance is introduced. It is the sum of the reciprocal absolute Jacobian determinants spanning all possible routes and is independent of the initial velocity distribution. The bands, pinch point and other features seen in the dynamic admittance plots are analyzed. The effects of the earth in reducing the number of routes, thereby casting a dynamic shadow, are demonstrated.</p>
Formation of super-Earths in a protoplanetary disk
<p>This is a simulation of the process of formation for a planetary system with close-in super-Earths.</p> <p>The simulation begins with 125 planetary embryos, each with 0.4 times the mass of the Earth. The gray region shows the height of the protoplanetary disk. At the beginning of the simulation the embryos have strong gravitational encounters that increase their orbital inclinations (y-axis) and leads to collisions. After the embryos merge into larger bodies, interactions with the disk cause the eccentricities and inclinations to drop and the planets form a chain of mean motion resonances. Planets also experience torques from the disk that remove orbital angular momentum which causes the planets to drop to lower orbits. While the planets migrate through the disk, they accrete gas from the disk. The color of each body shows the fraction of their mass that is in the gaseous envelope.</p> <p>The disk dissipates after 5 Myr. With the disk gone, the planetary system becomes dynamically unstable and two new waves of giant impacts occur. This leaves behind a planetary system with larger planet masses, smaller gas envelopes, and higher mutual inclinations.</p>
EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 1
<p>EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters.</p> <p>The current data set is a sample of the data generated within the first project year (2017), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area.<br> Data sets of the second (2018) and third (2019) year of EOMORES will also be submitted.</p> <p>The following is included:<br> - Estonia lakes and coast: in situ data 2017<br> - Finland: links to repositories of EO data<br> - Italy Trasimeno: sample of EO data 2017<br> - Lithuania Curonian Lagoon: sample of EO data 2017<br> - Netherlands Lake Markermeer: sample of EO data 2017<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: in situ data Loch Leven and Loch Lomond 2017<br> - UK WCO Sentinel2A match ups: Western Channel Observatory match ups with Sentinel-2 satellite 2016, 2017</p> <p>http://eomores-h2020.eu</p>
Idealized orbital extreme GCM simulations with EC-Earth-2-2
<p>This repository contains monthly mean output from the EC-Earth-2-2 orbital extreme simulations. The experiment details are described in the papers given as references. Daily or 12-hourly output (too large to place in this repository) can be obtained from Joyce Bosmans. For the PMIP3 simulations daily output is included, see below.</p> <p>*** Orbital extreme precession and obliquity experiments ***<br> Four simulations were performed: precession minimum and maximum (Pmin and Pmax) as well as obliquity maximum and minimum (Omax and Omin). The numbers in the file names each represent a variable according to the ECMWF 128 grib table, see e.g. https://apps.ecmwf.int/codes/grib/param-db. The qv, qdv, dqv and dqdv files represent the (anomalous) moisture transport as described in Bosmans et al. 2015, Climate dynamics.</p> <p>References:<br> Bosmans, J. H. C. (2014). A model perspective on orbital forcing of monsoons and Mediterranean climate using EC-Earth. UU Depts. of Physical Geography and Earth Sciences.<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., & Lourens, L. J. (2015). Response of the North African summer monsoon to precession and obliquity forcings in the EC-Earth GCM. Climate dynamics, 44(1-2), 279-297.<br> Bosmans, J. H. C., Hilgen, F. J., Tuenter, E., & Lourens, L. J. (2015). Obliquity forcing of low-latitude climate. Climate of the Past, 11(10), 1335-1346.<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., Lourens, L. J., & Rohling, E. J. (2015). Precession and obliquity forcing of the freshwater budget over the Mediterranean. Quaternary Science Reviews, 123, 16-30.<br> Bosmans, J. H. C., Erb, M. P., Dolan, A. M., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., Edge, D., Pope, J.O. & Lourens, L. J. (2018). Response of the Asian summer monsoons to idealized precession and obliquity forcing in a set of GCMs. Quaternary Science Reviews, 188, 121-135.</p> <p><br> *** Mid-Holocene and Control PMIP3 experiments ***<br> Output from the Mid-Holocene and the control (pre-industrial) simulation is available through the CMIP5 / PMIP3 database. See for instance https://wiki.lsce.ipsl.fr/pmip3/doku.php/pmip3:database:access. If you use the IPSL ESGF node you should get to the following website (it may require signing up for a log-in but it should be easy and free): https://esgf-node.ipsl.upmc.fr/projects/esgf-ipsl/.<br> On that website, click CMIP5 under 'Search Data'. Then on the left, under 'Model', select 'EC-Earth-2-2'. If you then click 'Search' in the upper middle of the page, you can further select the PI and MH simulations ('Experiment') and variables ('variable long name' is rather useful).</p> <p>Reference:<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Lourens, L. J., Hilgen, F. J., & Weber, S. L. (2012). Monsoonal response to mid-holocene orbital forcing in a high resolution GCM. Climate of the Past, 8(2), 723-740.</p> <p>Contact:<br> Joyce.Bosmans@ru.nl</p> <p>Acknowledgements:<br> These model simulations were performed during Joyce Bosmans' PhD research, funded by an Utrecht University `Focus en Massa' grant and partly performed at the Royal Netherlands Meteorological Institute (KNMI), with support from ECMWF.</p>
Data for the publication "Rare Earth Elements in oyster shells: provenance discrimination and potential vital effects"
<p>Rare Earth Elements (REE) and yttrium measurements from modern and archaeological oyster shells collected by LA-ICP-MS. This dataset is used in the publication 'Rare Earth Elements in oyster shells: provenance discrimination and potential vital effects'.</p>
IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations: Boundary conditions and outputs.
<p>Inputs, boundary conditions, and ouputs files of the experiments described in Sepulchre et al. manuscript "<em>IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations</em>" submitted for publication to Geoscientific Model Development:</p> <p><a href="https://www.geosci-model-dev-discuss.net/gmd-2019-332"><strong>https://www.geosci-model-dev-discuss.net/gmd-2019-332</strong></a></p> <p>The 90Ma_IPSLCM5A2_inputs.tar tarball contains the input and boundary files used to run the 3,000-year Cretaceous experiment.</p> <p>The output_files.tar tarball contains the netcdf output files of the preindustrial, historical and Cretaceous simulations analyzed in the manuscript. Diagnoses are presented through a Jupyter notebook that can be retrieved and played interactively <strong><a href="https://doi.org/10.5281/zenodo.3549652"><strong>here</strong></a>.</strong></p> <p><strong> </strong></p>
Buoyancy Waves in Earth's Nightside Magnetosphere: Normal-Mode Oscillations of Thin Filaments
<ul> <li>A theory has been developed for small oscillations of a thin filament in the magnetosphere.</li> <li>For the lowest-frequency even modes, the eigenfunctions are essentially buoyancy waves in the plasma sheet, but they are more like slow modes in the inner magnetosphere.</li> <li>For the lowest-frequency even modes, the eigenfrequencies (radians/s) have peak values of approximately 0.07 s<sup>-1</sup> between the inner plasma sheet and plasmapause, for an average magnetospheric field. </li> <li>This includes software and model data used in this paper.</li> <li>Submitted to JGR for review</li> </ul>
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017]. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017].
Figure 3 in Insecticidal effect of diatomaceous earth and dolomite powder against Corn weevil Sitophilus zeamais Motschulsky, 1855 (Coleoptera: Curculionidae)
Figure 3. Pictures of Sitophilus zeamais control taken by scanning electron microscopy (SEM). A. Dorsal view: trichoid sensilla (Se), sensilla (S), antenna (A), rostrum (R), elytrum (E). Bar = 500 µm. B. Rostrum and antenna: trichoid sensilla (Se), sensilla (S). Bar = 100 µm. C. Antenna: trichoid sensilla (Se). Bar = 20 µm. D. Elytrum: sensilla (S), suture (Su). Bar = 20 µm. E. Elytrum: sensilla (S), suture (Su). Bar = 50 µm. F. Abdomen, ventral view: sensilla (S). Bar = 10 µm.
Figure 2 in Insecticidal effect of diatomaceous earth and dolomite powder against Corn weevil Sitophilus zeamais Motschulsky, 1855 (Coleoptera: Curculionidae)
Figure 2. Mortality at different concentrations (mg) of diatomaceous earth and dolomite powder used for control of Sitophilus zeamais Motschulsky, 1855 (Coleoptera: Curculionidae), after different exposure times. DE:Diatomaceous earth and DOL: Dolomite powder.
Figure 5 in Insecticidal effect of diatomaceous earth and dolomite powder against Corn weevil Sitophilus zeamais Motschulsky, 1855 (Coleoptera: Curculionidae)
Figure 5. Pictures of Sitophilus zeamais exposed to inert dusts taken by scanning electron microscopy (SEM). A. Elytrum (E) of insect exposed to diatomaceous earth: sensilla (S), suture (Su). Bar = 200 µm. B. Elytrum (E) of insect exposed to dolomite powder: sensilla (S), suture (Su). Barra = 20 µm. C. Leg of insect exposed to dolomite powder: sensilla (S). Bar = 100 µm. D. Claw of insect exposed to dolomite powder. Bar = 50 µm.
High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data
<p>We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m<sup>2</sup> for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.</p> <p>For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40<a> European countries, </a>forming a continuous coverage of the western part of the European continent.</p> <p>A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to <a>73 % </a>the South-Eastern area.</p> <p>The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.</p> <p>The author list is sorted by last name except for the first and last authors who also serve as corresponding authors.</p> <p>Corresponding authors: <a href="mailto:Jukka.Miettinen@vtt.fi">Jukka.Miettinen@vtt.fi</a>, <a href="mailto:Johannes.Breidenbach@nibio.no">Johannes.Breidenbach@nibio.no</a></p>
Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database
<p>The database of organizational actors, "Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database" analyzed in "<span>Whose Priorities? Examining Inequities in Earth </span><span>Observation Advancements Across Africa" </span>this study, is available on Zenodo, an open-access repository developed under the European OpenAIRE program. The dataset comprises information on 310 space-centric earth observation organizations, including headquarters locations. For the 31 organizations in our sample, we provide additional details including the African countries where their projects are active, the type of initiative or program, other focus areas, organizational classification (commercial, government, or nongovernmental), funding source (public or private), organizational type (research, startup, or established industry), capabilities (data analysis, data storage, image labeling, competition platforms), involvement in early warning systems, data accessibility, availability of global products, and whether they build commercial satellites.</p> <p>This open sharing of the compiled organizational data aims to promote transparency, reproducibility, and additional investigations into the evolving landscape of earth observation activities globally and across Africa. Analyses of this dataset's relationships, funding flows, and priorities can provide further insights to guide equitable advancement of earth observation capabilities.</p>
Figure 6 in Can humans share spaceship earth?
Figure 6. Number of species of living varanids traded over the 30 year period from 1975 to 2005, based on CITES data (from Pernetta 2009).
Figure 4. A in Can humans share spaceship earth?
Figure 4. A prime candidate for imminent extinction, the very rare Guatemalan Abronia frosti. Photo courtesy of Jonathan Campbell.
Figure 2 in Can humans share spaceship earth?
Figure 2. Trends in annual total rainfall in Australia over the past four decades (Reprinted with permission from the Australian Bureau of Meteorology).
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.