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6,569 results for “Long-term”
Long-term (2001-2020) global full-coverage daytime and nighttime carbon monoxide profile and total column
<p>Validation results are in preparation. The full dataset will be uploaded after the submission/acceptance of our paper.</p>
Long-Term, Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores
<p>Supporting data for paper "Long-Term, Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores"</p>
CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing
<p>Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. <em>Sci Data</em> 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x</p> <p> </p> <p>The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m<sup>3</sup>/m<sup>3</sup> to 0.014 m<sup>3</sup>/m<sup>3</sup> and on average is close to a desired SM product stability threshold of 0.01 m<sup>3</sup>/m<sup>3</sup> per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m<sup>3</sup>/m<sup>3</sup>. </p>
Global and regional long-term M4.95+ seismicity forecasts undergoing prospective evaluation
<p>Contains a stationary M5.95+ seismicity forecast derived from the Global Earthquake Activity Rate (GEAR1) model of Bird et al. (2015) and nineteen time-invariant M4.95+ earthquake forecasts participating in forecast experiments conducted by the Collaboratory for the Study of Earthquake Predictability (CSEP) in California, New Zealand, and Italy. Ten additional forecast files are included to properly perform comparative tests.</p> <p>Earthquake rates are expressed as number of M4.95+ earthquakes per 0.1<sup>o</sup> cell per year. Forecasts are stored in tab separated values files with the following fields (the first row is shown as an example):</p> <table> <tbody> <tr> <td>lon_min</td> <td>lon_max</td> <td>lat_min</td> <td>lat_1</td> <td>depth_0</td> <td>depth_1</td> <td>mag_0</td> <td>mag_1</td> <td>rate</td> <td>flag</td> </tr> <tr> <td>-125.4</td> <td>-125.3</td> <td>40.1</td> <td>40.2</td> <td>0.0</td> <td>30.0</td> <td>4.95</td> <td>5.05</td> <td>5.8499e-04</td> <td>1</td> </tr> </tbody> </table> <p>The data, forecasts, and tests are described in detail in the following publications and the references contained therein:</p> <p>Bayona, J.A., Savran, W.H., Iturrieta, P., Gerstenberger, M.C., Marzocchi, W., Schorlemmer, D., and Werner, M.J., Are Regionally Calibrated Seismicity Models more Informative than Global Models? Insights from California, New Zealand, and Italy. <em>in review</em>.</p> <p>Bayona, J.A., Savran, W.H., Rhoades, D.A. and Werner, M.J., 2022. Prospective evaluation of multiplicative hybrid earthquake forecasting models in California. <em>Geophysical Journal International</em>, <em>229</em>(3), pp.1736-1753.</p> <p>Bird, P., Jackson, D.D., Kagan, Y.Y., Kreemer, C. and Stein, R.S., 2015. GEAR1: A Global Earthquake Activity Rate Model Constructed from Geodetic Strain Rates and Smoothed SeismicityGEAR1: A Global Earthquake Activity Rate Model Constructed from Geodetic Strain Rates and Smoothed Seismicity. <em>Bulletin of the Seismological Society of America</em>, <em>105</em>(5), pp.2538-2554.</p> <p>Marzocchi W, Schorlemmer D, Wiemer S. Preface. Ann. Geophys. [Internet]. 2010Nov.5 [cited 2022Sep.16];53(3):III-VIII. Available from: https://www.annalsofgeophysics.eu/index.php/annals/article/view/4851</p> <p>Rhoades, D.A., Christophersen, A., Gerstenberger, M.C., Liukis, M., Silva, F., Marzocchi, W., Werner, M.J. and Jordan, T.H., 2018. Highlights from the first ten years of the New Zealand earthquake forecast testing center. <em>Seismological Research Letters</em>, <em>89</em>(4), pp.1229-1237.</p> <p>Savran, W.H., Bayona, J.A., Iturrieta, P., Asim, K.M., Bao, H., Bayliss, K., Herrmann, M., Schorlemmer, D., Maechling, P.J. and Werner, M.J., 2022. pycsep: A python toolkit for earthquake forecast developers. <em>Seismological Society of America</em>, <em>93</em>(5), pp.2858-2870.</p> <p>Schorlemmer, D., Gerstenberger, M.C., Wiemer, S., Jackson, D.D. and Rhoades, D.A., 2007. Earthquake likelihood model testing. <em>Seismological Research Letters</em>, <em>78</em>(1), pp.17-29.</p> <p>Werner, M.J., Zechar, J.D., Marzocchi, W. and Wiemer, S., 2010. Retrospective evaluation of the five-year and ten-year CSEP-Italy earthquake forecasts. <em>arXiv preprint arXiv:1003.1092</em>.</p> <p>Zechar, J.D., Gerstenberger, M.C. and Rhoades, D.A., 2010. Likelihood-based tests for evaluating space–rate–magnitude earthquake forecasts. <em>Bulletin of the Seismological Society of America</em>, <em>100</em>(3), pp.1184-1195.</p> <p>Zechar, J.D., Schorlemmer, D., Werner, M.J., Gerstenberger, M.C., Rhoades, D.A. and Jordan, T.H., 2013. Regional earthquake likelihood models I: First‐order results. <em>Bulletin of the Seismological Society of America</em>, <em>103</em>(2A), pp.787-798.</p>
Code and data used in "A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies"
<p>Data and code for Tool for Air Pollution Scenarios (TAPS v1.0) as submitted to Geoscientific Model Development for publication. See the enclosed README and full user manual (https://github.com/watkin-mit/TAPS/wiki) for more information. </p>
data sets of "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"
<p>data sets from "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"</p> <p>Monthly means ozone profiles data sets of MCH homogenized Dobson D051 and of Brewer B040 used in the article entitled: "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland" by Eliane Maillard Barras, Alexander Haefele, René Stübi, Achille Jouberton, Herbert Schill, Irina Petropavlovskikh, Koji Miyagawa, Martin Stanek, and Lucien Froidevaux.</p> <p><a href="https://doi.org/10.5194/acp-2022-344">https://doi.org/10.5194/acp-2022-344</a></p>
Data for manuscript "Long-term variation in the quasi-five-day wave in the top layer of Venus clouds"
<p>The dataset contain the derived cloud motions from Akatsuki LIR level3c and the corrected LIR level3c brightness temperature. The data of each figure used for drawing are also contained.</p>
Long-term variations of daily insolation and Quaternary climatic changes
<p>Supplementary material to:</p> <p>Berger A. L. (1978), Long-term variations of daily insolation and Quaternary climatic changes, Journal of the Atmospheric Sciences, (35) 2362-2367 doi:10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2</p> <p>Tables and FORTRAN computer code for the computation of daily mean insolation, according to the solution BER78.</p> <p>This solution is commonly referred to as BER78.</p> <p>Material uploaded to Zenodo by Michel Crucifix on 2022-10-14.</p>
Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies (datasets and R scripts)
<p>This digital archive is an outcome of the paper Kolář J., Macek M., Tkáč P., Novák D. & V.Abraham: Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies. Quaternary Science Reviews, 2022</p>
Fig. 1 in Long-term exposure of Aedes aegypti to Bacillus thuringiensis svar. israelensis did not involve altered susceptibility to this microbial larvicide or to other control agents
Fig. 1 Resistance ratios (RR) betseen the lethal concentrations of Bti and its toxins (Cru11Aa, Cru4Ba), temephos (Tem) and diflubenzuron (Dif) for third-instar Ae. aegypti larvae from the RecBti strain compared to that of the reference strain. a RR at LC50. b RR at LC90
Рис. 11. Гидрометеоролого-технологическая блок-схема хоЗяйственных решений (di) и гидрометеорологических долгосрочных прогноЗов (Pi), необходимых для их принятия. Fig. 11. Hydrometeorological-technological block diagram of economic decisions (di) and hydrometeorological long-term forecasts (Pi) necessary for their adoption. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 11. Гидрометеоролого-технологическая блок-схема хоЗяйственных решений (di) и гидрометеорологических долгосрочных прогноЗов (Pi), необходимых для их принятия. Fig. 11. Hydrometeorological-technological block diagram of economic decisions (di) and hydrometeorological long-term forecasts (Pi) necessary for their adoption.
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E). in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E).
Data from: Local environment and coral composition affect recovery and determine long-term coral responses to recurrent mass mortalities in the Lakshadweep Archipelago
<p>A quarter century after the first global coral bleaching event in 1998, reports differ on the relative importance of anthropogenic influences, local environment and bleaching recurrence in determining the resilience of coral reefs. While life history traits largely determine how corals respond to temperature anomalies, it is unclear if these traits also determine how corals fare over time. From 1998 to 2022, we tracked compositional changes in reefs across the Lakshadweep Archipelago to explore how global El Niño events, and local environment (wave climate and depth) influenced coral responses to repeated mass bleaching. From the 1998 to the 2016 bleaching event, the magnitude of coral mortality reduced overall, particularly at deeper reefs (shallow: -38% to -3%; deep: -18% to -0.45%). Post-bleaching recovery correlated positively with higher wave exposure, linked to the creation of stable structures for coral settlement and survival. Across bleaching phases, recovery was initially slow (6-7 years post-mortality), but, given time, showed a much steeper increase, led by space-occupying genera like <em>Acropora</em>. However, recurring mass bleaching maintained coral cover low (~15% across all sites). These broad trends mask dynamic compositional patterns. Genera such as <em>Porites</em>, <em>Pocillopora</em>, and <em>Favia</em> declined less through time compared to <em>Acanthastrea</em>, <em>Turbinaria</em>, <em>Psammocora</em> and <em>Plesiastrea </em>among others. We identified six community clusters that describe contrasting long-term responses to local and global factors, mediated by depth and wave exposure. Interestingly, genera with different functional traits cluster together indicating that bleaching susceptibility interacts with depth and exposure, creating a spatial mosaic of coral assemblages. These clusters serve as a predictive, site-specific framework to understand the dynamically shifting but declining assemblage of Lakshadweep reefs. While local management could help maintain this changing composition, urgent global action is needed to secure the long-term ecological integrity of tropical reefs.</p>
Comparing long-term power sector pathways in Viet Nam: a simple cost-optimization approach with OSeMOSYS
<p>Six clicSAND files for OSeMOSYS Modelling in Viet Nam. The six files depict six different scenarios:</p> <p>1. Power Development Plan 7-based scenario</p> <p>2. Power Development Plan 8-based scenario</p> <p>3. Renewable Energy Development Strategy-based scenario</p> <p>4. Renewables-Led Pathway-based scenario</p> <p>5. Net Zero</p> <p>6. Clean Efficient Transition</p> <p>The Reference Energy System (RES) is also included to represent the relationship between estimated energy demands, energy conversion technologies, fuel mixes, and the resources required to satisfy the demands in the study.</p>
Dataset: BlackRock Long-Term U.S. Equity ETF (BELT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vanguard Long-Term Treasury Index Fund ETF Shares (VGLT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vanguard Long-Term Corporate Bond Index Fund ETF Shares (VCLT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 2 in Long-Term Light Trap Study On The Macro-Moth (Lepidoptera: Macroheterocera) Fauna Of The Aggtelek National Park
Fig. 2. Activity curves. (A) Changes of the number of species, and (B) specimens in 2003. Catches were summed up on every second day, and box diagrams were based on 5 data items. The curves
Fig. 1 in Long-Term Light Trap Study On The Macro-Moth (Lepidoptera: Macroheterocera) Fauna Of The Aggtelek National Park
Fig. 1. Species constancy of the macro-moth's (Lepidoptera: Macroheterocera) at the Aggtelek National Park based on a light trap operated at the southern slope of the Tohonya-valley. The graph
Рис. 3. Спектры многоΛетней цикΛичности в разΛичных попуΛяциях Λесного Λемминга (а — ЗапаΔная ФинΛянΔия, б — Восточная ФинΛянΔия, в — АΛтай, оз. ТеΛецкое) in Long-Term Variability In Forest Lemming Population Numbers ( Liljeborg, 1844): Cyclicity
Рис. 3. Спектры многоΛетней цикΛичности в разΛичных попуΛяциях Λесного Λемминга (а — ЗапаΔная ФинΛянΔия, б — Восточная ФинΛянΔия, в — АΛтай, оз. ТеΛецкое)
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.