Here are the data set and source code related to the paper: "A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms"1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actorsand https://malpedia.caad.fkie.fraunhofer.de/actors3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections4- countries.zip : contains country name list. 5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip. 10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use_neo4j_config=true --env=NEO4J_AUTH=none neo4j:5.13.0web interface: http://localhost:7474username: neo4jpassword: neo4j

(a) Graph representing fold change in mRNA levels in cells treated with Shh against untreated cells. Each circle represents an autophagy gene; those with largest fold-changes are indicated in red. The central line represents no changes in expression; above the central line, genes whose expression is increased: below, those with reduced levels. Grey lines indicate 2-fold increase or decrease. (b) Table showing the 10 genes with higher fold changes in expression and the values obtained.. List of tagged entities: AKT1 (ncbigene:207), APP (ncbigene:351), ARSA (ncbigene:410), ATG9A (ncbigene:79065), EIF2AK3 (ncbigene:9451), EIF4G1 (ncbigene:1981), GABARAPL1 (ncbigene:23710), HGS (ncbigene:9146), TGM2 (ncbigene:7052), UVRAG (ncbigene:7405), SHH (uniprot:Q15465), polymerase chain reaction (bao:BAO_0002080)

This feature layer shows the local relative sea level trend at more than 100 of NOAA’s tide-monitoring stations in the United States and Pacific Islands over their period of operation, which varies from station to station. Colored arrows indicate the direction of any trend: blue, upward arrows indicate rising sea level and brown, downward arrows indicate falling sea level. Local, or relative, sea level change may be more or less than the global average sea level trend due to ocean currents, natural climate variability, or changes occurring on land, such as sediment compaction. The map includes a pop-up window for each station that shows the station name, its period of operation, its highest and lowest monthly sea level, and an animated gif of line graphs of monthly sea level for the station’s period of operation. Darker lines indicate older years, and lighter lines indicate more recent years. If the lines start high on the graph and move down, local sea level at that location is falling. If the lines start lower on the graph and move up, local sea level is rising.The layer's source data can be found at https://tidesandcurrents.noaa.gov/sltrends/.

This data set consists of a geo-referenced digital map and attribute data derived from the publication 'Permafrost map of Alaska'. The map is presented at a scale of 1 to 2,500,000 and shows the correlation of physiographic province to presence of permafrost across the state of Alaska. The digital data were prepared under the U.S. Geological Survey Global Change Program, Land Data Systems - Arctic Land Processes Studies for display and analysis of terrain. The line work was captured by hand digitizing the source map, Ferrians, O.J., 1965, Permafrost map of Alaska - U.S. Geological Survey Miscellaneous Geologic Investigations Map I-445. Scale 1 to 2,500,000. The digital map was assembled and edited in ARC/INFO. The source map projection is polyconic. It is based on the Clarke 1866 ellipsoid with a central meridian of 150 W longitude. The data were geo-referenced from digitizer coordinates to the polyconic projection and then projected into an Albers Equal Area projection. The coastline was taken from the U.S Geological Survey, 1 to 2,000,000 scale Digital Line Graph data (U.S. Geological Survey, 1987). Attributes for the permafrost map were assigned. Metadata documentation was completed in 1996. The map units are closed polygons that are generalized in shape and size. They are defined in terms of their physiographic characteristics and association with permafrost. Each unit differs with respect to all other units and is uniquely identified as follows.11 Mountainous Area underlain by continuous permafrost12 Mountainous Area underlain by discontinuous permafrost13 Mountainous Area underlain by isolated masses of permafrost21 Lowland and Upland Area underlain by thick permafrost22 Lowland and Upland Area underlain by moderately thick to thin permafrost23 Lowland and Upland Area underlain by discontinuous permafrost24 Lowland and Upland Area underlain by numerous isolated masses of permafrost25 Lowland and Upland Area underlain by isolated masses of permafrost26 Lowland and Upland Area generally free of permafrostUse constraints - The U.S. Geological Survey should be acknowledged as the data source in products derived from these data. The data are general in nature and should not be used at a scale larger than 1 to 2,500,000, that of the original map. Users must assume responsibility to determine the usability of this data for their purposes. The use of these data is not restricted and may be interpreted by organizations, agencies, units of government or others; however, they are responsible for its appropriate application. Digital data files are periodically updated. Files are dated and users are responsible for obtaining the latest revisions of the data. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made by the agency regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty. A copy of this map is presented on the CAPS Version 1.0 CD-ROM, June 1998.

Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence graph. The edge-vertex incidence matrix and the normalized edge Laplacian matrix are factors in the covariance matrix for the pair-wise data. Using empirical measures of residual scatter in the pair-wise observations, we estimate the variance at each epoch by inverting the covariance of the pair-wise data. We evaluate the rank deficiency of the corresponding least-squares problem via the edge-vertex incidence matrix. We implement our method in a MATLAB software package called GraphTreeTA available on GitHub (https://github.com/feigl/gipht). We apply temporal adjustment to the data set described in Lu et al. (2005) at Okmok volcano, Alaska, which erupted most recently in 1997 and 2008. The data set contains 44 differential volumetric changes and uncertainties estimated from interferograms between 1997 and 2004. Estimates show that approximately half of the magma volume lost during the 1997 eruption was recovered by the summer of 2003. Between June 2002 and September 2003, the estimated rate of volumetric increase is (6.2 +/- 0.6) x 10^6 m^3/yr. Our preferred model provides a reasonable fit that is compatible with viscoelastic relaxation in the five years following the 1997 eruption. Although we demonstrate the approach using volumetric rates of change, our formulation in terms of incidence graphs applies to any quantity derived from pair-wise differences, such as wrapped phase or wrapped residuals.

Date of final oral examination: 05/19/2016 This thesis is approved by the following members of the Final Oral Committee: Kurt L. Feigl, Professor, Geoscience Michael Cardiff, Assistant Professor, Geoscience Clifford H. Thurber, Vilas Distinguished Professor, Geoscience

The Alaska Department of Fish and Game's (ADF&G) Anadromous water bodies data is derived from the ADF&G's GIS shape files for the "Catalog of Waters Important for Spawning, Rearing or Migration of Anadromous Fishes" (referred to as the "Catalog") and the "Atlas to the Catalog of Waters Important for Spawning, Rearing or Migration of Anadromous Fishes" (referred to as the "Atlas"). It is produced for general visual reference and to aid users in generating various natural resource analyses and products. The shape files depict the known anadromous fish bearing lakes and streams within Alaska (from the mouth to the known upper extent of species usage). It incorporates data from a variety of sources including: USGS Digital Line Graph (DLG) and National Hydrography Dataset (NHD) hydrography data; Alaska Department of Natural Resources hydrography layer; and ADF&G shape files for the "Atlas" and "Catalog". ADF&G updates the Anadromous Streams data regularly. Note that stream numbers, locations, extent of cataloged habitat or species utilization of a given stream may change from year to year. Data for the shape files are current as of the 2014 revision of the "Atlas to the Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes" and the "Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes" effective June 1, 2014.

The main stock market index of United States, the US500, rose to 6201 points on June 30, 2025, gaining 0.44% from the previous session. Over the past month, the index has climbed 4.46% and is up 13.25% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

"Please if you use this datasets we appreciated that you reference this repository and cite the works related that made possible the generation of this dataset." This change detection datastet has different events, satellites, resolutions and includes both homogeneous/heterogeneous cases. The main idea of the dataset is to bring a benchmark on semantic change detection in remote sensing field.This dataset is the outcome of the following publications:

@article{ JimenezSierra2022graph,author={Jimenez-Sierra, David Alejandro and Quintero-Olaya, David Alfredo and Alvear-Mu{~n}oz, Juan Carlos and Ben{\'i}tez-Restrepo, Hern{\'a}n Dar{\'i}o and Florez-Ospina, Juan Felipe and Chanussot, Jocelyn},journal={IEEE Transactions on Geoscience and Remote Sensing},title={Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection},year={2022},volume={60},number={},pages={1-16},doi={10.1109/TGRS.2022.3168126}} @article{ JimenezSierra2020graph,title={Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops},author={Jimenez-Sierra, David Alejandro and Ben{\'i}tez-Restrepo, Hern{\'a}n Dar{\'i}o and Vargas-Cardona, Hern{\'a}n Dar{\'i}o and Chanussot, Jocelyn},journal={Remote Sensing},volume={12},number={17},pages={2683},year={2020},publisher={Multidisciplinary Digital Publishing Institute},doi={10.3390/rs12172683}} @inproceedings{jimenez2021blue,title={Blue noise sampling and Nystrom extension for graph based change detection},author={Jimenez-Sierra, David Alejandro and Ben{\'\i}tez-Restrepo, Hern{\'a}n Dar{\'\i}o and Arce, Gonzalo R and Florez-Ospina, Juan F},booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},ages={2895--2898},year={2021},organization={IEEE},doi={10.1109/IGARSS47720.2021.9555107}} @article{florez2023exploiting,title={Exploiting variational inequalities for generalized change detection on graphs},author={Florez-Ospina, Juan F and Jimenez Sierra, David A and Benitez-Restrepo, Hernan D and Arce, Gonzalo},journal={IEEE Transactions on Geoscience and Remote Sensing}, year={2023},volume={61},number={},pages={1-16},doi={10.1109/TGRS.2023.3322377}} @article{florez2023exploitingxiv,title={Exploiting variational inequalities for generalized change detection on graphs},author={Florez-Ospina, Juan F. and Jimenez-Sierra, David A. and Benitez-Restrepo, Hernan D. and Arce, Gonzalo R},year={2023},publisher={TechRxiv},doi={10.36227/techrxiv.23295866.v1}} In the table on the html file (dataset_table.html) are tabulated all the metadata and details related to each case within the dasetet. The cases with a link, were gathered from those sources and authors, therefore you should refer to their work as well. The rest of the cases or events (without a link), were obtained through the use of open sources such as:

Copernicus European Space Agency Alaska Satellite Facility (Vertex) Earth Data In addition, we carried out all the processing of the images by using the SNAP toolbox from the European Space Agency. This proccessing involves the following:

Data co-registration Cropping Apply Orbit (for SAR data) Calibration (for SAR data) Speckle Filter (for SAR data) Terrain Correction (for SAR data) Lastly, the ground truth was obtained from homogeneous images for pre/post events by drawing polygons to highlight the areas where a visible change was present. The images where layout and synchorized to be zoomed over the same are to have a better view of changes. This was an exhaustive work in order to be precise as possible.Feel free to improve and contribute to this dataset.

This map shows the local relative sea level trend at more than 100 of NOAA’s tide-monitoring stations in the United States and Pacific Islands over their period of operation, which varies from station to station. Colored arrows indicate the direction of any trend: blue, upward arrows indicate rising sea level and brown, downward arrows indicate falling sea level. Local, or relative, sea level change may be more or less than the global average sea level trend due to ocean currents, natural climate variability, or changes occurring on land, such as sediment compaction. The map includes a pop-up window for each station that shows the station name, its period of operation, its highest and lowest monthly sea level, and an animated gif of line graphs of monthly sea level for the station’s period of operation. Darker lines indicate older years, and lighter lines indicate more recent years. If the lines start high on the graph and move down, local sea level at that location is falling. If the lines start lower on the graph and move up, local sea level is rising.The map's source data can be found at https://tidesandcurrents.noaa.gov/sltrends/.

This dataset contains the 2012 version of the anadromous fish streams (polylines) for Southeast Alaska and is pull from the Anadromous Waters Catalog. The Alaska Department of Fish and Game's (ADF&G) Anadromous water bodies data is derived from the ADF&G's GIS shape files for the "Catalog of Waters Important for Spawning, Rearing or Migration of Anadromous Fishes" (referred to as the "Catalog") and the "Atlas to the Catalog of Waters Important for Spawning, Rearing or Migration of Anadromous Fishes" (referred to as the "Atlas"). It is produced for general visual reference and to aid users in generating various natural resource analyses and products. The shape files depict the known anadromous fish bearing lakes and streams within Alaska (from the mouth to the known upper extent of species usage). It incorporates data from a variety of sources including: USGS Digital Line Graph (DLG) and National Hydrography Dataset (NHD) hydrography data; Alaska Department of Natural Resources hydrography layer; and ADF&G shape files for the "Atlas" and "Catalog". ADF&G updates the Anadromous Streams data regularly. Note that stream numbers, locations, extent of cataloged habitat or species utilization of a given stream may change from year to year. Data for the shape files are current as of the 2012 revision of the "Atlas to the Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes" and the "Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes" effective June 1, 2012. This particular data layer is for the Southeastern Region of Alaska.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q1 2025 about sales, median, housing, and USA.

The net job and business growth indicator measures the annual change in both the number of firms and the number of employees between 1978 and 2022. The data is categorized by the size of the firm: those with 1-19 employees, those with between 20 and 499 employees, and those with more than 500 employees.

This data contributes to the big picture of economic conditions in Champaign County. More firms and larger employment numbers are generally positive economic indicators, but any strictly economic indicator should be considered in the context of other factors.

The number of firms and number of employees show very different trends.

Historically, there have been significantly more firms with 1-19 employees than firms in the larger two size categories. The number of firms with 1-19 employees has also been relatively consistent until 2021: there were 95 fewer such firms in 2021 than 1978, and the largest year-to-year change in that 43-year period of analysis was a -3.2% decrease between 1979 and 1980. However, there were 437 fewer such firms in 2022 than 1978. There was a decrease in these firms of 12.5% from 2021 to 2022, the only double-digit year-to-year change and the largest year-to-year change over 44 years.

The larger two size categories have shown an increasing trend over the period of analysis. There were 43 more firms with 20-499 employees in 2022 than 1978, a total increase of 9%. The number of firms with more than 500 employees almost doubled, increasing by 206 firms from 212 in 1978 to 418 in 2022, a total increase of 97.2%.

The trends of employment also vary based on firm size. Firms with 1-19 employees have consistently, and unsurprisingly, accounted for less of the total employment than the larger two categories. Employment in firms with 1-19 employees has also remained relatively consistent over the period of analysis. Employment in firms with more than 500 employees saw an overall trend of growth, interrupted by brief and intermittent decreases, between 1978 and 2022. Employment in the middle category (firms with between 20 and 499 employees) was also greater in 2022 than in 1978.

This data is from the U.S. Census Bureau’s Business Dynamics Statistics Data Tables. This data is at the geographic scale of the Champaign-Urbana Metropolitan Statistical Area (MSA), which is comprised of Champaign and Piatt Counties, or a larger area than the cities or Champaign County.

Source: U.S. Census Bureau; 2022 Business Dynamics Statistics Data Tables; "BDSFSIZE - Business Dynamics Statistics: Firm Size: 1978-2022"; retrieved 21 October 2024.

This collection contains the source data for the graph of Figure 3.

[1] The Progress by Population Group analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included subsets of the 1,111 measurable HP2020 objectives that have data available for any of six broad population characteristics: sex, race and ethnicity, educational attainment, family income, disability status, and geographic location. Progress toward meeting HP2020 targets is presented for up to 24 population groups within these characteristics, based on objective data aggregated across HP2020 topic areas. The Progress by Population Group data are also available at the individual objective level in the downloadable data set. [2] The final value was generally based on data available on the HP2020 website as of January 2020. For objectives that are continuing into HP2030, more recent data will be included on the HP2030 website as it becomes available: https://health.gov/healthypeople. [3] For more information on the HP2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf. [4] Status for objectives included in the HP2020 Progress by Population Group analysis was determined using the baseline, final, and target value. The progress status categories used in HP2020 were: a. Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target (the percentage of targeted change achieved was equal to or greater than 100%); (ii) The baseline and most recent values were equal to or exceeded the target (the percentage of targeted change achieved was not assessed). b. Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change. c. Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point. d. Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline. NOTE: Measurable objectives had baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 Progress by Population Group database.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

PCE Price Index Annual Change in the United States increased to 2.30 percent in May from 2.20 percent in April of 2025. This dataset includes a chart with historical data for the United States PCE Price Index Annual Change.

Stay informed with real-time charts of international precious metal prices. Monitor spot prices for Silver in USD, GBP, and EUR. Access live updates here >>

This dataset represents points from the Alaska Department of Fish and Game "Catalog of Waters Important for Spawning, Rearing, or Migration of Anadromous Fishes." This dataset represents the combination of several regional datasets produced by the Alaska Department of Fish and Game. The Conservation Biology Institute used information in these datasets to derive additional attributes describing the life stages recorded for each species, and summary metrics across all species. The individual shapefiles from which this dataset was compiled were produced for general visual reference and to aid users in generating various natural resource analyses and products. The shapefiles depict the known anadromous fish bearing lakes and streams within Alaska (from the mouth to the known upper extent of species usage). It incorporates data from a variety of sources including: USGS Digital Line Graph (DLG) and National Hydrography Dataset (NHD) hydrography data; Alaska Department of Natural Resources hydrography layer; and Alaska Department of Fish and Game shapefiles for the "Atlas" and "Catalog". Alaska Department of Fish and Game updates the Anadromous Streams data regularly. Note that stream numbers, locations, extent of cataloged habitat or species utilization of a given stream may change from year to year. Data for the shapefiles are current as of the 2007 revision of the "Atlas to the Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes" and the "Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes" effective June 1, 2007.

Private businesses in the United States hired 37 thousand workers in May of 2025 compared to 60 thousand in April of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.