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2,516 results for “Seri”
Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs (repeat)
<p>The dataset contains microscopy image data of DNA-nanomotifs (color channel 2) that can form liquid-like condensates, especially in association with target DNA strands that can act as condensation surfaces (color channel 1). The data were recorded on a VisiTech iSIM microscopy with a 40X water immersion objective (Apo LWD 40x WI λS DIC N2, NA 1.15). Images were recorded using dual ORCA-Flash4.0 V3 cameras with simultaneous two-channel acquisition to avoid displacement of objects due to consecutive acquisition of color channels. The file names contain the flow channel number, the presence of condensation surfaces (SX4), and the amount of added nanomotif solution in microliters (NX in µl).</p> <p>The image files are in the .nd2 Nikon proprietary format, but can be read with the OME bioformats libraries.</p> <p>Scripts for the analysis of these data are available via a GitHub repository: <a href="https://github.com/lhilbert/SurfacesAndCondensates" target="_blank" rel="noopener">https://github.com/lhilbert/SurfacesAndCondensates</a></p> <p>This repository contains data recorded on 10 March, 2023</p> <p>The file size of data were too large, so additional repositories were opened. The complete date need to be also downloaded from these additional repositories:</p> <p>EXTRA DATA 1/2: 10.5281/zenodo.13788977</p> <pre><a href="https://doi.org/10.5281/zenodo.13788977" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13788977</a></pre> <p>EXTRA DATA 2/2: 10.5281/zenodo.13789555</p> <pre><a href="https://doi.org/10.5281/zenodo.13789555" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13789555</a></pre> <pre> </pre> <p>There is another data set that contains a repeat of the same conditions, recorded on 13 March, 2023, under the following link</p> <pre><a href="https://doi.org/10.5281/zenodo.13788484" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13788484</a></pre> <p><br>From these data sets, two concentration conditions were excluded from further analysis, as described in the file <a href="https://zenodo.org/api/records/15231478/draft/files/Zenodo_Titration_Exclusions.pdf/content" target="_blank" rel="noopener noreferrer">Zenodo_Titration_Exclusions.pdf</a></p>
Geospatial and time series dataset for hydrologic analyses within South Asia (GHSA)
<p>Summary of changes:</p> <ul> <li>v2504, GHSA <ul> <li>1,702 stations from 5 countries in South Asia (Bhutan, China, India, Nepal and Pakistan);</li> <li>districts and basin states from 7 countries in South Asia (Afghanistan, Bangladesh, Bhutan, China, India, Nepal and Pakistan)</li> <li>land use change, vegetation index (NDVI, NDVI-crop), precipitation, evaporation (E-total, E-crop and E-irrigation), streamflow, surface and root zone soil moisture, terrestrial water storage change, snow water equivalent and snow cover fraction; 1950-2023</li> <li>publication:</li> </ul> </li> <li>v2409 <ul> <li>test version</li> </ul> </li> <li>v2301, GHI <ul> <li>645 stations, limited to Peninsular India</li> <li>precipitation, evaporation and streamflow; 1950-2020</li> <li>publication: Goteti (2023), Earth Syst. Sci. Data, https://doi.org/10.5194/essd-15-4389-2023</li> </ul> </li> </ul> <p> </p>
Daily activity time series of ants, Camponotus japonicus
<p>Social insects often share tasks among individuals. In this study, we analyzed the foraging activity of ants (<em>Camponotus japonicus</em>) and recorded the daily passage event counts of individual workers between a nest chamber and a foraging arena using five monodomous colonies. We proposed two hypotheses on the time series of foraging frequency by individual worker ants:</p> <p>(i) Regarding the time series of foraging frequency by individual worker ants, the foraging frequency on a certain day could be expressed by the product of the foraging frequency on the previous day and the exponential of a random number.<br>(ii) The random numbers are correlated between some pairs of worker ants.</p> <p>The results for the five tested ant colonies showed that the probability of total daily passage counts (the sum of an individual's passage count) were characterized by a log-normal distribution. The worker ants behaved differently in terms of active days and foraging frequency. However, for >54% of the worker ants, the probability of the daily passage count was characterized by a log-normal distribution, and these worker ants performed >72% of the tasks in each colony. Furthermore, for >73% of the worker ants, the time development of passage count was confirmed to be mathematically modelled; the logarithmic first difference between the passage counts on a certain day and those on the previous day was a random normal variable. These results support hypothesis (i). Additionally, the random numbers, that were equivalent to the logarithmic first difference, were correlated for some pairs of worker ants. These results support hypothesis (ii).</p>
GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County
<p>Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see <a href="https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf">https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf</a>. For the balancing authority level timeseries, please see <a href="https://doi.org/10.5281/zenodo.7888568">10.5281/zenodo.7888568</a>. The code used to produce this data is available at <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>. Note that fleet sizes at the state scale are derived from the GCAM-USA scenario output. Downscaling to the county scale uses the electric vehicle penetration rates found in the appendix of <a href="https://www.pnnl.gov/sites/default/files/media/file/EV-AT-SCALE_1_IMPACTS_final.pdf">M. Kintner-Meyer, S. Davis, S. Sridhar, D. Bhatnagar, S. Mahserejian and M. Ghosal, "Electric vehicles at scale-phase I analysis: High EV adoption impacts on the western US power grid", Tech. Rep., 2020</a>. To harmonize the state scale LDV energy use from the GCAM-USA scenarios with the LDV load calculated with EV-Pro Lite, a scale factor was applied to the county level loads, so note that if the reported scale factor is much different than 1.0 there is potentially some disagreement between the load and the fleet size. This scale factor has already been applied to the loads reported in these files (but has NOT been applied to the fleet sizes).</p><h4><strong>GCAM-USA scenarios</strong></h4><p>See <a href="https://doi.org/10.5281/zenodo.7838871">10.5281/zenodo.7838871</a> and <a href="https://doi.org/10.5281/zenodo.8377778">10.5281/zenodo.8377778</a> for more details</p><ul><li>BAU_Climate - a business-as-usual scenario without IRA incentives</li><li>business_as_usual_ira_ccs_climate - a business-as-usual scenario with IRA incentives for CCS technology</li><li>NetZeroNoCCS_Climate - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology</li><li>net_zero_ira_ccs_climate - scenario targeting net-zero by 2050 with IRA incentives for CCS technology</li></ul><h4><strong>Climate pathways</strong></h4><p>See <a href="https://doi.org/10.1038/s41597-023-02485-5">10.1038/s41597-023-02485-5</a> for more details</p><ul><li>rcp45cooler - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models</li><li>rcp85hotter - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models</li></ul><h4><strong>Fields in the data files:</strong></h4><ul><li>time - hourly timestamp in UTC representing the preceding hour of data</li><li>county - the county name</li><li>State - the state abbreviation for this county</li><li>FIPS - FIPS code for the county</li><li>balancing_authority - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority</li><li>load_MWh - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours</li><li>temperature_celsius - mean temperature within this county and balancing authority in degrees Celsius</li><li>fleet_size - number of electrified LDV cars within this county and balancing authority</li><li>daily_miles - average number of miles traveled per day per LDV within this county and balancing authority in miles/day</li><li>scale_factor - the values in the load_MWh field have been scaled by this multiplier in order to harmonize the state scale LDV loads with the GCAM-USA scenarios</li></ul><h4><strong>Changelog</strong></h4><ul><li>v1.0.1 - added fleet_size, daily_miles, and scale_factor to the output, and updated the README accordingly</li></ul><h4><strong>Acknowledgements</strong></h4><p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
National-scale tree species/genera map for Poland from Sentinel-2 time series
<p>Map of 16 dominant tree species/genera in Poland based on classification of time series of Sentinel-2 imagery. This dataset is associated with the article by Grabska-Szwagrzyk et al. (2024)<em>: <a href="https://essd.copernicus.org/articles/16/2877/2024/">Map of forest tree species for Poland based on Sentinel-2 data.</a></em></p> <p>The map is provided as GeoTiff file. In addition, training and test data is provided in shapefile format. The map can be explored online in a <a href="https://ee-aweaksbarg.projects.earthengine.app/view/speciesmappl">webviewer</a>.</p> <p> </p>
Bermuda Atlantic Time-Series Study (BATS) Zooplankton Biomass
<p>The BATS (Bermuda Atlantic Time-series Study) zooplankton biomass dataset is time-series spanning from 1994 to 2022. The dataset contains zooplankton biomass measurements.</p><p>Due to an ambiguity with depth a subset of the dataset been removed as part of the Simons CMAP curation process. The original dataset is also included here:</p><ul><li>Curated by Simons CMAP: BATS_Zooplankton_Biomass_CMAP.xlsx </li><li>Original version as text file: BATS_zooplankton.xlsx</li><li>Original version as excel file: BATS_zooplankton.xlsx </li></ul><p>The following dates were impacted by the depth ambiguity: 1994 (4/6 – 12/12); 1995 (1/11 – 4/27, 8/22); 2000 (2/28); 2001 (1/30, 8/7-8/8, 9/12, 10/16); 2004 (2/14, 3/23, 4/7, 7/14-15, 8/16-17); 2005 (1/27); 2006 (5/11, 6/26, 9/4-9/5); 2007 (7/18, 8/9, 10/6); 2008 (6/22); 2009 (2/10, 4/1, 4/15, 5/16, 5/19, 10/10); 2017 (5/9); 2018: (8/13); 2022: (6/29).</p><p>This description has been reproduced using https://www.dropbox.com/sh/xo6c72qaeznyv05/AACCiijqHcd2chjbwrqNcxica?dl=0&preview=BATS_zooplankton.txt</p>
The reconstructed surface water area time series (2000-2019) dataset for lakes >1 km2 in China
<p>This repository contains the <strong>revised version</strong> of the supplementary data for the paper: <strong>Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China </strong>(https://www.sciencedirect.com/science/article/pii/S0034425723003267?via%3Dihub). </p> <p>Specifically, this dataset documents the reconstructed surface water area time series for all studied lakes in China during the period of 2000-2019. In the prior version of the dataset, there was an erroneous assignment of IDs to each lake. This issue has been rectified in the revised version, ensuring that the updated IDs now accurately correspond to the actual GLAKES_ID for each of the GLAKES lake polygons.</p> <p>For more detailed information of the dataset, please refer to the README file.</p>
MINI-EX version 2.2 input files (MiMB, lab protocol series book chapter)
<p>Wendrich et al. (2020) Arabidopsis root scRNA-seq dataset, processed for MiMB (lab protocol series) book chapter: "MINI-EX version 2: cell-type-specific gene regulatory network inference using an integrative single-cell transcriptomics approach"</p> <p>Contains:</p> <ul> <li>Seurat object of single-cell dataset</li> <li>Extracted input files for MINI-EX</li> </ul>
jcpenney-mcs-series-vtr-component-tv
jcpenney-mcs-series-vtr-component-tv This model is hand-made using parametric techniques, image networks and some ray-traced label making HDA fun. #houdini #procedural Only the decals have UV's and textures, the rest is just form and function. This model is inspired greatly by a physical object that can be seen in a youtube video located here: https://www.youtube.com/watch?v=KPneS9ZdUB8 (We thank TechMoan for his wonderful coverage of #vintage and #retro electronics gear!! [disclaimer: not affiliated with TechMoan, just a fan]) please enjoy this lightweight, render-ready model - 100% made using parametric and procedural techniques in #sidefxhoudini [renders with #blender #cycles #b279] <a href="https://imgur.com/BF6kcxW"></a> <a href="https://imgur.com/ELGv3JS"></a> If you are interested in custom graphics work, please get in touch Thank you, see you soon, -Alan/Organic https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
PhasAGE Ask The Expert Series | Silvio Tosatto
<p>A collection of captivating interviews with senior researchers from the PhasAGE consortium. Meet each core member while they foster new scientific horizons and cultivate collaborations, through clear, engaging dialogue that connects science with society. This initiative was organized by the early-stage researchers together with the Research and Communication Manager.</p> <p>This interview is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952334 – PhasAGE, and was co-produced with MSCA-RISE IDPfun (H2020 EU GA778247), MSCA-RISE REFRACT (H2020 EU GA823886) and ML4NGP COST Action (CA21160).</p>
Displacement time series from GNSS stations in the Alto Tiberina Fault area (Central Italy)
<p>The files report the position time-series of GNSS stations deployed in the Alto Tiberina Fault area (Central Italy). <br>Columns are: Time, E, N, Se, Sn, Ren, U, Su, Reu, Rnu, site, long, lati, representing, respectively, epoch (in decimal years), displacement in the East component (in mm), displacement in the North component (in mm), uncertainty (one standard deviation) of the East component (in mm), uncertainty (one standard deviation) of the North component (in mm), correlation between the East and North components, displacement in the Up component (in mm), uncertainty (one standard deviation) of the Up component (in mm), correlation between the East and Up components, correlation between the North and Up components, Station ID (four letters), Longitude of the station (°), Latitude of the station (°).</p>
Daily GNSS time series of La Palma 2021 eruption
<p>Time series form GNSS station in La Palma (Canary Island, Spain) during 2021 eruption (19/09/2021-21/01/2022). The daily neu time series have been computed in a Double Diference method using Bernese v.5.2 software as describe in the references.</p> <p> </p>
AutoML Applied to Time Series Analysis Tasks in Production Engineering
<p>The dataset is accompanying the paper "AutoML Applied to Time Series Analysis Tasks in Production<br>Engineering" (<a href="https://doi.org/10.1016/j.procs.2024.01.085" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2024.01.085</a>). It contains the experimental data referred to in the paper as "KIOptiPack".</p>
Dataset for: A 31-year time series of at-sea counts shows a non-significant decline of Marbled Murrelets at Laskeek Bay, Haida Gwaii, 1990-2020
<p>This dataset includes counts of Marbled Murrelets (<em>Brachyramphus marmoratus</em>) conducted from a small boat near Laskeek Bay on the east coast of Louise Island, and north of Lyell Island in Haida Gwaii, from 1990 to 2020. The dataset includes counts of Marbled Murrelets observed on the water or flying by. Information is also included on the date, time of day, survey width, and spatial coordinates of the transects where the observation took place.</p>
High-Resolution Beach Profile Time Series: Morecambe Bay, 2007-2022
<p><strong>Description:</strong> This dataset provides a comprehensive collection of beach profile measurements for Morecambe Bay, covering a 16-year period from 2007 to 2022. The data was collected and shared by Sefton Council (Sefton MBC), UK, as part of the coastal monitoring programme. Captured biannually during spring and autumn, the data offers valuable insights into the bay's coastal morphology and dynamics.</p> <p><strong>Data Description:</strong></p> <ul> <li><strong>Beach Transects:</strong> Detailed profiles are provided for various locations along the Morecambe Bay coastline. Station numbers are used to identify each measurement point on a dedicated map (included within the data).</li> <li><strong>Temporal Coverage:</strong> The dataset encompasses measurements conducted biannually, capturing seasonal variations in beach profiles.</li> </ul>
Data from: Decadal-scale time series highlight the role of chronic disturbances in driving ecosystem collapse in the Anthropocene
<p>These data support a publication in the Journal Ecology that describes 37 years of change on the coral reefs of St. John, US Virgin Islands. In this paper, four decades of surveys from two coral reefs (9 and 14 m depth) off St. John, US Virgin Islands, are used to quantify the associations of acute and chronic events with the changes in benthic community structure. These reefs profoundly changed over 36 years, with coral death altering species assemblages to depress abundances of the ecologically important coral <em>Orbicella</em> spp. and elevating the coverage of macroalgae and crustose coralline algae/turf/bare space (CTB). Linear mixed models revealed the prominent role of chronic variation in temperature in accounting for changes in coverage of corals, macroalgae, and CTB, with rising temperature associated with increases in coral cover on the deep reef, and declines on the shallow reef. Hurricanes were also associated with declines in coral cover on the shallow reef, and increases on the deep reef. Multivariate analyses revealed strong associations between community structure and temperature, but weaker associations with hurricanes, bleaching, and diseases. These results highlight the overwhelming importance of chronically increasing temperature in altering the benthic community structure of Caribbean reefs.</p>
The raw GNSS position time series (raw_time_series.rar) and TDEFNODE models (TDEFNODE_models.rar) related to the manuscript authored by Rui Xu, D. S. Stamps and C. A. Williams
<p>This repository saves the raw GNSS position time series (raw_time_series.rar) and TDEFNODE models (TDEFNODE_models.rar) related to the manuscript authored by Rui Xu, D. S. Stamps and C. A. Williams. For more details, please refer to the NOTES files in each .rar archive.</p>
"Moira knows what you did": Glasgow Disability Alliance Podcast Series Episode 1 for Our Heritage, Our Stories
<p>https://open.spotify.com/episode/0mwaMu6cqqbgx9RoFR6EFf</p>
Acute pseudo-landmarking and Constellation homologies: A generalized workflow to identify and track segmented structures in plant time series images
<p>Assessing plant phenotypes throughout the lifecycle is integral to exploring the development, genetics, and evolution of morphology, and can be critical for agronomic and basic research studies. Although various automated or semi-automated phenomic approaches have been developed, it has been challenging to analyze differential growth because of difficulties in segmenting and annotating specific structures or positions in the plant body and maintaining their identities throughout time-series data. To address this gap, we have developed a generalized workflow linking our previously published function, <i>Acute</i>, with a companion homology workflow, <i>Constellation</i>, in the PlantCV environment. <i>Acute</i> identifies acute shapes (pseudo-landmarks) in the plant body, most often corresponding to leaf tips and ligular regions. <i>Constellation</i> uses a strategy of dimensionality reduction via <i>starscape</i> followed by hierarchical clustering through <i>constella </i>to identify 'constellations' of segments in eigenspace that represent the same landmark in consecutive images of a time-series. We devised a quality control function, <i>constellaQC</i>, to test the accuracy of the clustering approach, and use it to show that the approach appropriately clusters the pseudo-landmarks derived from <i>Acute</i>, with 80-90% accuracy. We discuss the reasons for and consequences of this lack of 100% accuracy in automated workflows and suggest how to develop these functions for other phenomics datasets that may vary in dimensional complexity.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 12
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 12 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 and C07 which were acquired by scanning electron microscopy. The images show alveolae with various degree of epithelial damage.</p>
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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.