The Maine Geological Survey and the USGS coordinate the colletction of snow measurements each winter for the Maine River Flow Advisory Commission's flood prediction report. These measurements are sent to MGS monthly in January and February and weekly in March, April and May as long as there is snow on the ground. The dataset contains all the raw snow survey measurements (depth, water content, density), their locations, data quality and other qualitative comments or observations. These measurements are used to create the snow survey site summary graphs. These graphs show the water content measurements by defined date range for the current year and the complete historical mean, minimum, maximum, and percentiles

Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe navigation and to provide background data for engineers, scientific, and other commercial and industrial activities. Hydrographic survey data primarily consist of water depths, but may also include features (e.g. rocks, wrecks), navigation aids, shoreline identification, and bottom type information. NOAA is responsible for archiving and distributing the source data as described in this metadata record.

This document contains graphs summarizing the waterbird surveys conducted on St. Vincent National Wildlife Refuge between 1995 and 2000.

The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe navigation and to provide background data for engineers, scientific, and other commercial and industrial activities. Hydrographic survey data primarily consist of water depths, but may also include features (e.g. rocks, wrecks), navigation aids, shoreline identification, and bottom type information. NOAA is responsible for archiving and distributing the source data as described in this metadata record.

Task scheduler performance survey

This dataset contains results of task graph scheduler performance survey. The results are stored in the following files, which correspond to simulations performed on the elementary, irw and pegasus task graph datasets published at https://doi.org/10.5281/zenodo.2630384.

elementary-result.zip

irw-result.zip

pegasus-result.zip

The files contain compressed pandas dataframes in CSV format, it can be read with the following Python code: python import pandas as pd frame = pd.read_csv("elementary-result.zip")

Each row in the frame corresponds to a single instance of a task graph that was simulated with a specific configuration (network model, scheduler etc.). The list below summarizes the meaning of the individual columns.

graph_name - name of the benchmarked task graph

graph_set - name of the task graph dataset from which the graph originates

graph_id - unique ID of the graph

cluster_name - type of cluster used in this instance the format is x; 32x16 means 32 workers, each with 16 cores

bandwidth - network bandwidth [MiB]

netmodel - network model (simple or maxmin)

scheduler_name - name of the scheduler

imode - information mode

min_sched_interval - minimal scheduling delay [s]

sched_time - duration of each scheduler invocation [s]

time - simulated makespan of the task graph execution [s]

execution_time - real duration of all scheduler invocations [s]

total_transfer - amount of data transferred amongst workers [MiB]

The file charts.zip contains charts obtained by processing the datasets. On the X axis there is always bandwidth in [MiB/s]. There are the following files:

[DATASET]-schedulers-time - Absolute makespan produced by schedulers [seconds]

[DATASET]-schedulers-score - The same as above but normalized with respect to the best schedule (shortest makespan) for the given configuration.

[DATASET]-schedulers-transfer - Sums of transfers between all workers for a given configuration [MiB]

[DATASET]-[CLUSTER]-netmodel-time - Comparison of netmodels, absolute times [seconds]

[DATASET]-[CLUSTER]-netmodel-score - Comparison of netmodels, normalized to the average of model "simple"

[DATASET]-[CLUSTER]-netmodel-transfer - Comparison of netmodels, sum of transfered data between all workers [MiB]

[DATASET]-[CLUSTER]-schedtime-time - Comparison of MSD, absolute times [seconds]

[DATASET]-[CLUSTER]-schedtime-score - Comparison of MSD, normalized to the average of "MSD=0.0" case

[DATASET]-[CLUSTER]-imode-time - Comparison of Imodes, absolute times [seconds]

[DATASET]-[CLUSTER]-imode-score - Comparison of Imodes, normalized to the average of "exact" imode

Reproducing the results

1. Download and install Estee (https://github.com/It4innovations/estee)

$ git clone https://github.com/It4innovations/estee $ cd estee $ pip install .

1. Generate task graphs You can either use the provided script benchmarks/generate.py to generate graphs from three categories (elementary, irw and pegasus):

$ cd benchmarks $ python generate.py elementary.zip elementary $ python generate.py irw.zip irw $ python generate.py pegasus.zip pegasus

or use our task graph dataset that is provided at https://doi.org/10.5281/zenodo.2630384.

1. Run benchmarks To run a benchmark suite, you should prepare a JSON file describing the benchmark. The file that was used to run experiments from the paper is provided in benchmark.json. Then you can run the benchmark using this command:

$ python pbs.py compute benchmark.json

The benchmark script can be interrupted at any time (for example using Ctrl+C). When interrupted, it will store the computed results to the result file and restore the computation when launched again.

1. Visualizing results

$ python view.py --all

The resulting plots will appear in a folder called outputs.

Graph and download economic data for All Employees, Home Health Care Services (CEU6562160001) from Jan 1985 to Jun 2025 about health, establishment survey, education, services, employment, and USA.

Consumer confidence survey in the USA, June, 2025 The most recent value is 60.5 points as of June 2025, an increase compared to the previous value of 52.2 points. Historically, the average for the USA from January 1978 to June 2025 is 84.43 points. The minimum of 50 points was recorded in June 2022, while the maximum of 112 points was reached in January 2000. | TheGlobalEconomy.com

NOAA, National Ocean Service, Office of Coast Survey is responsible to build and maintain a suite of more than 1000 nautical charts that are used by commercial and recreational mariners to safely navigate the United States and the U.S. territory waters.A Nautical Chart is a graphic portrayal of the marine environment. They are used to lay out courses and navigate ships by the shortest and most...

Graph and download economic data for Indexes of Aggregate Weekly Payrolls of Production and Nonsupervisory Employees, Goods-Producing (CEU0600000035) from Jan 1947 to May 2025 about nonsupervisory, payrolls, establishment survey, production, goods, employment, indexes, and USA.

The Historical Map and Chart Collection of the Office of Coast Survey contains over 35000 historical maps and charts from the mid 1700s up through the 2020s, including the final cancelled editions of NOAA's raster charts. These images are available for viewing or download through the image catalog at https://historicalcharts.noaa.gov/. The Collection includes some of the nation's earliest nauti...

Digital line graph (DLG) data are digital representations of cartographic information. DLGs of map features are converted to digital form from maps and related sources. Large-scale DLG data are derived from USGS 1: 20,000-, 1: 24,000-, and 1: 25,000-scale 7.5-minute topographic quadrangle maps and are available in nine categories: (1) hypsography, (2) hydrography, (3) vegetative surface cover, (4) non-vegetative features, (5) boundaries, (6) survey control and markers, (7) transportation, (8) manmade features, and (9) Public Land Survey System. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

United States - Chicago Fed Survey of Business Conditions: Manufacturing Activity in Federal Reserve District 7: Chicago was -21.21212 Index in May of 2025, according to the United States Federal Reserve. Historically, United States - Chicago Fed Survey of Business Conditions: Manufacturing Activity in Federal Reserve District 7: Chicago reached a record high of 69.58949 in January of 2018 and a record low of -94.89796 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Chicago Fed Survey of Business Conditions: Manufacturing Activity in Federal Reserve District 7: Chicago - last updated from the United States Federal Reserve on July of 2025.

2011 to present. BRFSS combined land line and cell phone prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct

This is the reproducibility material for the following manuscript:

Bastian Rieck and Corinna Coupette. Evaluating the "Learning on Graphs" Conference Experience. 2023. arXiv: 2306.00586 [cs.LG].

United States - Labour Force Survey - quarterly levels: Active population: Aged 15-64: Males for OECD - Total was 356411500.00000 Persons in October of 2024, according to the United States Federal Reserve. Historically, United States - Labour Force Survey - quarterly levels: Active population: Aged 15-64: Males for OECD - Total reached a record high of 356497800.00000 in July of 2024 and a record low of 310326692.39828 in January of 2005. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Labour Force Survey - quarterly levels: Active population: Aged 15-64: Males for OECD - Total - last updated from the United States Federal Reserve on July of 2025.

2011 to present. BRFSS combined land line and cell phone prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct

United States - Chicago Fed Survey of Business Conditions: Nonmanufacturing Activity in Federal Reserve District 7: Chicago was 10.75269 Index in May of 2025, according to the United States Federal Reserve. Historically, United States - Chicago Fed Survey of Business Conditions: Nonmanufacturing Activity in Federal Reserve District 7: Chicago reached a record high of 71.64179 in August of 2014 and a record low of -52.70270 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Chicago Fed Survey of Business Conditions: Nonmanufacturing Activity in Federal Reserve District 7: Chicago - last updated from the United States Federal Reserve on July of 2025.

The GESIS Knowledge Graph (GESIS KG) represents metadata of all scientific resources available in the GESIS Search (https://search.gesis.org/) and its semantic relationships in an integrated and consistent form and makes them accessible for integration and reuse. Understanding relations and dependencies between scientific resources is crucial to capture provenance, ensure reproducibility of research and facilitate informed search across resources. Hence, the GESIS KG captures links between different scientific resources, e.g., links between data, publications, survey instruments, survey variables, and links between entities like authors and social science concepts. The GESIS KG is geared towards interoperability and uses established W3C standards and widely accepted vocabularies, such as schema.org, DDI, the NFDIcore Ontology among others to increase interoperability and reusability of data on the Web for both humans and machines, e.g., through APIs. On instance-level, we address interoperability by reusing PIDs from commonly used PID systems, interlinking the GESIS KG with other KG provided by GESIS as well within the NFDI.

Find more information at https://data.gesis.org/gesiskg/

Detailed description of the files can be found in GESISKG_readme.txt

Keywords: knowledge graph, semantic web, scholarly resource metadata, social sciences