Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time. Two tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.

mpb_outbreak_mapsThese files represent maps of mountain pine beetle infestation in Arapaho and Roosevelt National Forests, Colorado, for the years 2003, 2005, 2006, 2009, and 2010. They are identical in their format and methods of preparation, with the exception that they represent different time periods. They were developed from Landsat5 (2005, 2006, 2009, 2010) or a combination of Landsat5 and Landsat7 (pre-scan line corrector failure) imagery (2003) (path 32 row 34). All images were atmospherically corrected and converted to reflectance. Cloud cover was manually removed, with areas obscured by cloud cover in one year removed from all images. The study area was masked to include only forested areas in Arapaho and Roosevelt National Forests. The images were classified using a supervised maximum-likelihood algorithm in ENVI v.4.8 with training classes selected based on initial NDVI and change in NDMI between images. The training classes were hand selected so that Red Attack was ...

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

The STAFF (Spatio-Temporal Analysis of Field Fluctuations) experiment is one of the five experiments of the WEC. STAFF uses a three-axis search coil magnetometer to measure magnetic field fluctuations at frequencies up to 4 kHz. The waveform is digitised and telemetered to the ground at low frequencies, while at higher frequencies a digital spectrum analyser calculates the power spectrum and cross-spectrum in near-real time. The spectrum analyser also analyses the two spin-plane components of electric field as measured by the long dipole antennas of the EFW experiment. The three-axis search coil unit is mounted on a rigid boom with its three mutually orthogonal mechanical axes aligned respectively with the spin axis and the axes of the two EFW spin-plane wire antennas. Each sensor consists of a high permeability core embedded inside two solenoids. The main winding has a very large number of turns mounted in separate sections. The frequency response of the sensor is flattened in the frequency range 40--4000 Hz by a secondary winding used to introduce flux feedback. The secondary winding is also used as a calibration loop on which an external signal can be applied through a calibration network included in the preamplifiers. The measured sensitivity is 3 x10^-3 nT Hz^(-1/2) at 1 Hz and 3 x10^-5 nT Hz^(-1/2 )at 100 Hz. The search coils are designed so as to minimise their sensitivity to electric fields. The angles between each magnetic axis and the three mechanical axes have been carefully measured. These angles, at most a few degrees, are known with a precision of 0.1°. The three preamplifiers are mounted inside the spacecraft. The dynamic range of the preamplifiers is about 100 dB, to allow weak signals to be measured in the presence of the large signals induced by the rotation of the spacecraft in the ambient magnetic field. The magnetic preamplifier output is used by: - the STAFF magnetic waveform unit, - the STAFF spectrum analyser, - the WBD experiment, - the EFW experiment (for the fast event detector), and - the EDI experiment. The Magnetic Waveform Unit The three signals Bx, By, and Bz from the search coil preamplifier are passed through 7th order ant-aliasing filters (i.e., they have an attenuation of 42 dB per octave) with -3 dB cut-off at either 10 Hz or 180 Hz, depending upon the experiment operating mode. The signals are then applied to three sample and hold devices, and digitised by an ADC, with 16-bit precision to achieve the required dynamic range. The sampling is synchronised by the DWP experiment at either 25 or 450 Hz. This is 2.5 times the filter frequency, so that the rejection of aliased components is at least 40 dB. The output is sent to the DWP experiment. Note that, to facilitate ground data analysis, identical filters are used by the STAFF and the EFW experiments, and the same synchronisation signal is sent to both the STAFF and the EFW experiments. The dynamic range is reduced (by differencing) from 16 to 12 bits inside DWP. The Spectrum Analyser The frequency range of 8 to 4000 Hz is divided into three sub-bands, each of covering 3 octaves: - band A: 8--64 Hz - band B: 64--512 Hz - band C: 512--4000 Hz The ''front end'' of the analyser is analogue. For each of the three bands and five sensors there are nine automatic gain-controlled (AGC) amplifiers. The gain of these AGC amplifiers is a multiplying factor in the determination of the absolute measurement. The outputs from the 9 amplifiers are multiplexed to a single 8-bit ''flash'' analogue-to-digital converter, and sampled at 4 times the highest frequency in the band. The AGC gain-control signals are also digitised for inclusion in the telemetry. The digital processing is performed in three distinct steps: - De-spin of the spin-plane (By, Bz, and Ey, Ez) signals using the onboard sun reference pulse. - Determination of the complex Fourier coefficients, using an extension of the Remez exchange algorithm. - Calculation and integration of the correlation matrices. The resulting cross-spectral matrix has its diagonal elements logarithmically compressed into eight bits. The off-diagonal elements are normalised (by the diagonal elements), and coded using four bits (including the sign) for the real and four bits for the imaginary part. The spectrum analyser determines the complete 5x5 Hermitian cross-spectral matrix of the signals from five input channels, over the frequency range of 8 Hz to 4 kHz. The five auto-spectral power estimates are obtained with: - a dynamic range of approximately 100 dB, - an average amplitude resolution of 0.38 dB, The 10 cross-spectral power estimates are normalised to give the coherence, which is obtained with the following precision: - the magnitude falls into one of 8 bins with upper limits distributed approximately as 2-n, for n = 0 to 7 - the phase has a precision which depends upon the magnitude of the coherence: for a signal with magnitude in the highest bin, it is approximately 5° close to 0°, 180°, and +/-90°, increasing to about 10° midway between these angles. The spectral estimates are made at 27 frequencies distributed logarithmically over the range from 8 Hz to 4 kHz, with centre frequencies fmn = 2^(3m) × 2^((2n+1)/6) for 1 <= m <= 3 and 0 <= n <= 8 All channels are sampled quasi-simultaneously, and the integration time, normally the same for all auto and for all cross-spectral channels, can be commanded to values between 125 ms (except at the lowest frequencies) and 4 s. The cross-spectral matrix elements are generally have 4 times less time resolution than the auto-spectra. This description has been obtained from Section 3.5 of the ''Users Guide to the Cluster Science Data System'', DS-MPA-TN-0015.

Temporal analysis of the progression of cortical contusion in mice investigated via Magnetic Resonance Imaging (MRI).

Additional file 5.

Two data sets with transcript ratios associated with the manuscript:An Interferon Regulated MicroRNA Provides Broad Cell-Intrinsic Antiviral Immunity through Multihit Host-Directed Targeting of the Sterol Pathway. The full data set is on NCBI, gene expression omnibus (GEO) accession number GSE63290.

In this article, I explore Twitter data to analyze Gender Neutral Language (GNL) in (Greater) Buenos Aires, (Greater) La Plata, and Córdoba. The goal is to characterize the social context behind GNL. Social context analysis of social media data is challenging given that this data type does not contain the social characteristics of its users and the circumstances under which the tweets were written. In order to fill this gap, I will derive the social context information from textual and temporal features by analyzing the names of locations, companies, and people used in the text and relating these entities to the message of the tweet. The analysis of temporal features will give us insights into the correlation between language use and social events. Our results show that the general characterization of the social context behind GNL is associated with socio-economically rich areas in city centers. Users of GNL in the investigated areas address certain groups of people with words that express familiarity and close social relationships, such as those meaning “friends” and “neighbors” and that give them information about a political, cultural, or social event or concerning commercial products/services. The temporal analysis by month supports this characterization by showing that certain political and social events induce a higher frequency of GNL. This paper contributes to previous research on GNL in Argentina by testing existing hypotheses quantitatively. The new discovery presented here is that political activism is not the only language context in which GNL is used in social media and that GNL is not exclusively used in big cities of Argentina but also in smaller cities.

Bee microbiome paired-end, part AThe paired-end deep-sequence data from this paper was generated as a single lane of Illumina data, but was split up into 6 parts (A-F) for Dryad submission. This is part A. The file will unzip into two files containing the paired-end reads for the first 5.5M sequenced clusters. Phred+64 quality score encoding. Library prep details can be found in the Methods of the linked manuscript.s_4_partA.tar.gzBee microbiome paired-end, part BThe paired-end deep-sequence data from this paper was generated as a single lane of Illumina data, but was split up into 6 parts (A-F) for Dryad submission. This is part B. The file will unzip into two files containing the paired-end reads for the second 5.5M sequenced clusters. Phred+64 quality score encoding. Library prep details can be found in the Methods of the linked manuscript.s_4_partB.tar.gzBee microbiome paired-end, part CThe paired-end deep-sequence data from this paper was generated as a single lane of Illum...

DOI The objective of the study was to introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. The algorithm was designed to suppress signal patterns that are common in the central and surrounding pixels, utilizing spatial and temporal information at different scales. Twee folders ('Normalized_different_framesizes' en 'Retrieval_different_anomalies') zijn te groot voor upload en worden nagestuurd via SURF Filesender

Dataset Overview

  1. Dataset Details

This dataset contains 451 videos across two distinct categories:

201 videos focused on post-disaster scenarios 250 videos tailored for defense/security purposes

  Note:

Despite the restricted context of the dataset (post-disaster and defense/security), most questions are generic, requiring models to possess abilities for:

Temporal analysis Spatial analysis Motion comprehension Object recognition Event recognition

These… See the full description on the dataset page: https://huggingface.co/datasets/vbr48/qars48.

Dataset Construction

This dataset captures the temporal network of Bitcoin (BTC) flow exchanged between entities at the finest time resolution in UNIX timestamp. Its construction is based on the blockchain covering the period from January, 3rd of 2009 to January the 25th of 2021. The blockchain extraction has been made using bitcoin-etl (https://github.com/blockchain-etl/bitcoin-etl) Python package. The entity-entity network is built by aggregating Bitcoin addresses using the common-input heuristic [1] as well as popular Bitcoin users' addresses provided by https://www.walletexplorer.com/

[1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.
keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain},

Dataset Description

Bitcoin Activity Temporal Coverage: From 03 January 2009 to 25 January 2021

Overview:

This dataset provides a comprehensive representation of Bitcoin exchanges between entities over a significant temporal span, spanning from the inception of Bitcoin to recent years. It encompasses various temporal resolutions and representations to facilitate Bitcoin transaction network analysis in the context of temporal graphs.

Every dates have been retrieved from bloc UNIX timestamp and GMT timezone.

Contents:

The dataset is distributed across three compressed archives:

All data are stored in the Apache Parquet file format, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package.

1. orbitaal-stream_graph.tar.gz:

   • The root directory is STREAM_GRAPH/
   • Contains a stream graph representation of Bitcoin exchanges at the finest temporal scale, corresponding to the validation time of each block (averaging approximately 10 minutes).
   • The stream graph is divided into 13 files, one for each year
   • Files format is parquet
   • Name format is orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering
   • These files are in the subdirectory STREAM_GRAPH/EDGES/

2. orbitaal-snapshot-all.tar.gz:

   • The root directory is SNAPSHOT/
   • Contains the snapshot network representing all transactions aggregated over the whole dataset period (from Jan. 2009 to Jan. 2021).
   • Files format is parquet
   • Name format is orbitaal-snapshot-all.snappy.parquet.
   • These files are in the subdirectory SNAPSHOT/EDGES/ALL/

3. orbitaal-snapshot-year.tar.gz:

   • The root directory is SNAPSHOT/
   • Contains the yearly resolution of snapshot networks
   • Files format is parquet
   • Name format is orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering
   • These files are in the subdirectory SNAPSHOT/EDGES/year/

4. orbitaal-snapshot-month.tar.gz:

   • The root directory is SNAPSHOT/
   • Contains the monthly resoluted snapshot networks
   • Files format is parquet
   • Name format is orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet, where
   • [YYYY] and [MM] stands for the corresponding year and month, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year and month ordering
   • These files are in the subdirectory SNAPSHOT/EDGES/month/

5. orbitaal-snapshot-day.tar.gz:

   • The root directory is SNAPSHOT/
   • Contains the daily resoluted snapshot networks
   • Files format is parquet
   • Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet, where
   • [YYYY], [MM], and [DD] stand for the corresponding year, month, and day, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, and day ordering
   • These files are in the subdirectory SNAPSHOT/EDGES/day/

6. orbitaal-snapshot-hour.tar.gz:

   • The root directory is SNAPSHOT/
   • Contains the hourly resoluted snapshot networks
   • Files format is parquet
   • Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet, where
   • [YYYY], [MM], [DD], and [hh] stand for the corresponding year, month, day, and hour, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, day and hour ordering
   • These files are in the subdirectory SNAPSHOT/EDGES/hour/

7. orbitaal-nodetable.tar.gz:

   • The root directory is NODE_TABLE/
   • Contains two files in parquet format, the first one gives information related to nodes present in stream graphs and snapshots such as period of activity and associated global Bitcoin balance, and the other one contains the list of all associated Bitcoin addresses.

Small samples in CSV format

1. orbitaal-stream_graph-2016_07_08.csv and orbitaal-stream_graph-2016_07_09.csv

   • These two CSV files are related to stream graph representations of an halvening happening in 2016.

2. orbitaal-snapshot-2016_07_08.csv and orbitaal-snapshot-2016_07_09.csv

   • These two CSV files are related to daily snapshot representations of an halvening happening in 2016.

The data set contains the daily statistics for the TMP2m variable (2-m above ground temperature) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar day of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted normal distribution

Datasets for calibration (1–2) and evaluation (3–5) of CROPGRO-Canola model.

Site information and meteorological summary of the rapeseed growing season.

Analysis of ‘Global Landslide Catalog Export’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/31484040-b5ba-42a8-9ec1-1fe318127372 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag “GLC” in the landslide editor.

This dataset on data.nasa.gov was a one-time export from the Global Landslide Catalog maintained separately. It is current as of March 7, 2016. The original catalog is available here: http://www.arcgis.com/home/webmap/viewer.html?url=https%3A%2F%2Fmaps.nccs.nasa.gov%2Fserver%2Frest%2Fservices%2Fglobal_landslide_catalog%2Fglc_viewer_service%2FFeatureServer&source=sd

To export GLC data, you must agree to the “Terms and Conditions”. We request that anyone using the GLC cite the two sources of this database:

Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561–575. doi:10.1007/s11069-009-9401-4. [1] Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016. [2]

--- Original source retains full ownership of the source dataset ---

Benthic meiofauna samples from the northern Gulf of Mexico were collected with multi-corers on research cruises during 16 September - 30 October 2010, 23 May - 11 June 2011, and 29 May - 28 June 2014. Organisms were identified taxonomically and their abundance was determined. Taxonomic richness, diversity, and evenness of meiofauna, as well as nematode to copepod ratios were calculated at 34 stations for each of the three sampling times.

Toxic pollutants such as crude oil have direct negative effects for a wide array of marine life. While mortality from acute exposure to oil is obvious, sub-lethal consequences of exposure to petroleum derivatives for growth and reproduction are less evident and sub-lethal effects in fish populations are obscured by natural environmental variation, fishing, and measurement error. We use fisheries independent surveys in the Gulf of Alaska to examine the consequences of the 1989 Exxon Valdez oil spill (EVOS) for demersal fish. We delineate areas across a range of exposure to EVOS and use spatio-temporal models to quantify the abundance of 53 species-groups over 31 years. We compare multiple community metrics for demersal fish in EVOS and Control areas. We find that areas more exposed to EVOS have more negative trends in total groundfish biomass than non-EVOS areas, and that this change is driven primarily by reductions in the abundance of the apex predator guild. We show no signature of increased variability or increased levels of synchrony within EVOS areas. Our analysis supports mild consequences of EVOS for groundfish communities, but suggests that long time-series and assessments of changes at the community level may reveal sub-lethal effects in marine communities.

This submission contains 167 deviatoric moment tensor (MT) solutions for the seismicity observed two years prior and three years post start of injection activities at The Geysers Prati 32 EGS Demonstration. Also included is a statistical representation of the properties of 751 fractures derived from the analysis of seismicity observed two years prior and three years post start of injection activities at The Geysers Prati 32 EGS Demonstration Project. The locations of the fractures are taken from microseismic hypocenters, the fracture areas are derived from moment magnitudes via scaling relationships, and the azimuths (sigma 1) and dips (sigma 3) are derived from the results of stress analyses.

The market for Time Series Analysis Software is projected to reach $X million by 2033, growing at a CAGR of XX% from 2025 to 2033. Key drivers of this growth include the increasing adoption of IoT devices, the need for real-time data analysis, and the growing complexity of time series data. Additionally, the market is expected to benefit from advancements in artificial intelligence (AI) and machine learning (ML), which can be used to automate time series analysis tasks and improve the accuracy of predictions. The market for Time Series Analysis Software is segmented by application, type, and region. By application, the market is divided into large enterprises and SMEs. By type, the market is divided into cloud-based and on-premises solutions. By region, the market is divided into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. North America is expected to be the largest market for Time Series Analysis Software throughout the forecast period, followed by Europe and Asia Pacific. The growing adoption of IoT devices and the need for real-time data analysis are expected to be the key drivers of growth in these regions.