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18 results for “Historical networks”
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.
Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
Source Code Accompanying the Paper "More on network approaches in Historical Chinese Phonology (音韻學)"
<p>First version of the source code and data accompanying the paper "More on Network Approaches in Historical Chinese Phonology".</p> <p>This paper is available here:</p> <ul> <li>List, Johann-Mattis (2018): <strong>More on network approaches in Historical Chinese Phonology (音韻學)</strong>. Paper prepared for the <em>LFK Society Young Scholars Symposium</em>. Taibei: Li Fang-Kuei Society ofr Chinese Linguistics. URL: <a href="https://hal.archives-ouvertes.fr/hal-01706927">https://hal.archives-ouvertes.fr/hal-01706927</a>.</li> </ul> <pre><code>@InProceedings{List2018a, author = {List, Johann-Mattis}, title = {{More on Network Approaches in Historical Chinese Phonology (音韻學)}}, booktitle = {{LFK Society Young Scholars Symposium}}, year = {2018}, publisher = {Li Fang-Kuei Society for Chinese Linguistics}, pdf = {https://hal.archives-ouvertes.fr/hal-01706927/file/main.pdf}, url = {https://hal.archives-ouvertes.fr/hal-01706927}, address = {Taipei}, hal_id = {hal-01706927}, } </code></pre> <p>See the README.md for mor information.</p> <ul> <li> </li> </ul>
Code for: A century of wild bee sampling: historical data and neural network analysis reveal ecological traits associated with species loss
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Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
Fig. 3. Haplotype network for 419 in Across mountains and ocean: species delimitation and historical connectivity in Holarctic and Arctic-Alpine wolf spiders (Lycosidae, Pardosa)
Fig. 3. Haplotype network for 419 specimens of P. saltuaria species group based on mitochondrial COI data. Haplotypes are colored based on morphological identification. Each line is 1 mutation step.
DIVINE: Andalusia Agroclimatic Information Network Historic Datasets - CADIZ - (June 23 - May 24)
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DIVINE: Andalusia Agroclimatic Information Network Historic Datasets - HUELVA - (June 23 - May 24)
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DIVINE: Andalusia Agroclimatic Information Network Historic Datasets - CORDOBA - (June 23 - May 24)
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DIVINE: Andalusia Agroclimatic Information Network Historic Datasets - SEVILLA - (June 23 - May 24)
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DIVINE: Andalusia Agroclimatic Information Network Historic Datasets - GRANADA - (June 23 - May 24)
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DIVINE: Andalusia Agroclimatic Information Network Historic Datasets - ALMERIA - (June 23 - May 24)
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DIVINE: Andalusia Agroclimatic Information Network Historic Datasets - JAEN - (June 23 - May 24)
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Historic Caddo Network Analysis Data
<p>Ceramic and lithic types and counts used in the Historic Caddo network analysis for northeast Texas. This dataset is incomplete, and does not include the site locations used to plot the network.</p>
Data for articles in "Historical Network Analysis in the Study of Chinese Religion" (Special issue Religions 2023)
<p>This is the data for the seven articles collected in the special issue of <i>Religions</i> (2023) "Historical Network Analysis in the Study of Chinese Religion":</p><p> - Bingenheimer, Marcus. 2023. "Miyun Yuanwu 密雲圓悟 (1567–1642) and His Impact on 17th-Century Buddhism" Religions 14, no. 2: 248. https://doi.org/10.3390/rel14020248</p><p>- Chen, Song. 2023. "Patterns of Integration: A Network Perspective on Popular Religious Connections in China's Lower Yangzi, 1150–1350" Religions 14, no. 5: 577. https://doi.org/10.3390/rel14050577</p><p>- Chu, Ming-Kin. 2023. "Realizing the "Outwardly Regal" Vision in the Midst of Political Inactivity: A Study of the Epistolary Networks of Li Gang 李綱 (1083–1140) and Sun Di 孫覿 (1081–1169)" Religions 14, no. 3: 389. https://doi.org/10.3390/rel14030389</p><p>- Goossaert, Vincent. 2023. "The Social Networks of Gods in Late Imperial Spirit-Writing Altars" Religions 14, no. 2: 217. https://doi.org/10.3390/rel14020217</p><p>- Nehrdich, Sebastian. 2023. "Observations on the Intertextuality of Selected Abhidharma Texts Preserved in Chinese Translation" Religions 14, no. 7: 911. https://doi.org/10.3390/rel14070911</p><p>- Sokolova, Anna. 2023. "Regional Buddhist Communities in Tang China and Their Social Networks: The Network of Master Fayun (?–766)" Religions 14, no. 3: 335. https://doi.org/10.3390/rel14030335</p><p>- Van Cutsem, Laurent. 2023. "Lineages as Network: A Study of Chan Genealogy in the Zutang ji 祖堂集 Using Social Network Analysis" Religions 14, no. 2: 205. https://doi.org/10.3390/rel14020205</p>
LBA Regional Global Historical Climatology Network, V. 1, 1832-1990
This data set consists of a subset of the Global Historical Climatology Network (GHCN) Version 1 database for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). There are three files available, one each for precipitation, temperature, and pressure data. Within this subset the oldest data date from 1832 and the most recent from 1990.The GHCN V1 database contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which was to produce, maintain and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea-level pressure stations, and 1800 station-pressure stations. Each station has at least 10 years of data; 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara desert, Greenland, and Antarctica. The earliest station data are from 1697; the most recent are from 1990. The database was created from 15 source data sets including:The National Climatic Data Center's (NCDC's) World Weather Records,CAC's Climate Anomaly Monitoring System (CAMS),NCAR's World Monthly Surface Station Climatology,CIRES' (Eischeid/Diaz) Global precipitation data set,P. Jones' Temperature data base for the world, andS. Nicholson's African precipitation database. Quality Control of the GHCN V1 database included visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests. This detailed analysis has revealed that most stations (95% for temperature and precipitation, 75% for pressure) contain high-quality data. However, gross data-processing errors (e.g., keypunch problems) and discontinuous inhomogeneities (e.g., station relocations and instrumentation changes) do characterize a small number of stations. All major data processing problems have been flagged (or corrected, when possible). Similarly, all major inhomogeneities have been flagged, although no homogeneity corrections were applied.LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. LBA was a cooperative international research initiative led by Brazil and NASA was a lead sponsor for several experiments. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.
SAFARI 2000 Global Historical Climatology Network, V. 1, 1874-1990
This data set consists of a southern African subset of the Global Historical Climatology Network (GHCN) Version 1 database. All stations with the following bounding coordinates are included in this subset: 5W - 60E and 5N - 35S. There are three files available, one each for precipitation, temperature, and pressure data. Within this subset the oldest data date from 1874 and the most recent from 1990. The GHCN V1 database contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which is to produce, maintain and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea-level pressure stations, and 1800 station-pressure stations. Each station has at least 10 years of data; 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara desert, Greenland, and Antarctica. The earliest station data are from 1697; the most recent are from 1990. The database was created from 15 source data sets including: The National Climatic Data Center's (NCDC's) World Weather Records, CAC's Climate Anomaly Monitoring System (CAMS), NCAR's World Monthly Surface Station Climatology, CIRES' (Eischeid/Diaz) Global precipitation data set, P. Jones' Temperature data base for the world, and S. Nicholson's African precipitation database. Quality Control of the database included visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests. This detailed analysis has revealed that most stations (95% for temperature and precipitation, 75% for pressure) contain high-quality data. However, gross data-processing errors (e.g., keypunch problems) and discontinuous inhomogeneities (e.g., station relocations and instrumentation changes) do characterize a small number of stations. All major data processing problems have been flagged (or corrected, when possible). Similarly, all major inhomogeneities have been flagged, although no homogeneity corrections were applied. More information can be found at: ftp://daac.ornl.gov/data/safari2k/climate_meteorology/ghcn/comp/ghcn_v1_readme.pdf.
Global Historical Climatology Network, 1753-1990
This data set contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which is to produce, maintain, and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea level pressure stations, and 1800 station pressure stations. Each station has at least 10 years of data, 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara Desert, Greenland, and Antarctica.
DIVINE: Andalusia Agroclimatic Information Network Historic Datasets - MALAGA - (June 23 - May 24)
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Allen Brain Atlas
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OpenNeuro
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