NOAA Ship Oregon II Underway Meteorological Data (Near Real Time, updated daily) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z." in your query. "=~" indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '' says to match the previous character 0 or more times. See the tutorial for regular expressions at https://res1wwwd-o-tvogellad-o-tcom.vcapture.xyz/tutorials/JavaRegularExpressions/article.html

Last Version: 2

Author: Francisco Rubio, Universitat Politècnia de València.

Date of data collection: 2020/06/23

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v2.xlsx: full list of 56 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v2.csv: full list of 56 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 2nd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Scimago Journal and Country Rank (SJR)

Total size: 32 KB

Version 1: Description

This dataset contains a list of journals that publish data articles, code, software articles and database articles.

The search strategy in DOAJ and Ulrichsweb was the search for the word data in the title of the journals.
Acknowledgements:
Xaquín Lores Torres for his invaluable help in preparing this dataset.

In the SANDBOX research project, we investigated the natural dynamics of the North Sea bed. As part of this research, we conducted multiple research cruises on the North Sea. The documents in this dataset explain which data was collected, when it was collected and the structure of the data repository (svn.citg.tudelft.nl/sandbox).

NOAA Ship Pisces Underway Meteorological Data (Near Real Time, updated daily) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~'ZZZ........Z.* 'in your query. '=~ 'indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. See the tutorial for regular expressions at http://www.vogella.com/tutorials/JavaRegularExpressions/article.htmlNOAA Ship Pisces Underway Meteorological Data (Near Real Time, updated daily) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~'ZZZ........Z.* 'in your query. '=~ 'indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. See the tutorial for regular expressions at http://www.vogella.com/tutorials/JavaRegularExpressions/article.htmlNOAA Ship Pisces Underway Meteorological Data (Near Real Time, updated daily) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~'ZZZ........Z.* 'in your query. '=~ 'indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. See the tutorial for regular expressions at http://www.vogella.com/tutorials/JavaRegularExpressions/article.htmlNOAA Ship Pisces Underway Meteorological Data (Near Real Time, updated daily) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~'ZZZ........Z.* 'in your query. '=~ 'indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. See the tutorial for regular expressions at http://www.vogella.com/tutorials/JavaRegularExpressions/article.html

NOAA Ship Ferdinand Hassler Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~'ZZZ........Z.* 'in your query. '=~ 'indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. See the tutorial for regular expressions at http://www.vogella.com/tutorials/JavaRegularExpressions/article.htmlNOAA Ship Ferdinand Hassler Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~'ZZZ........Z.* 'in your query. '=~ 'indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. See the tutorial for regular expressions at http://www.vogella.com/tutorials/JavaRegularExpressions/article.htmlNOAA Ship Ferdinand Hassler Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~'ZZZ........Z.* 'in your query. '=~ 'indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. See the tutorial for regular expressions at http://www.vogella.com/tutorials/JavaRegularExpressions/article.htmlNOAA Ship Ferdinand Hassler Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~'ZZZ........Z.* 'in your query. '=~ 'indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. See the tutorial for regular expressions at http://www.vogella.com/tutorials/JavaRegularExpressions/article.html

description: Research Ship Aurora Australis Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z.*" in your query. '=~' indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. (Don't include backslashes in your query.) See the tutorial for regular expressions at http://www.vogella.de/articles/JavaRegularExpressions/article.html; abstract: Research Ship Aurora Australis Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z.*" in your query. '=~' indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '*' says to match the previous character 0 or more times. (Don't include backslashes in your query.) See the tutorial for regular expressions at http://www.vogella.de/articles/JavaRegularExpressions/article.html

GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.Each data granule has an associated browse product.

Research Ship Laurence M. Gould Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z." in your query. '=~' indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '' says to match the previous character 0 or more times. (Don't include backslashes in your query.) See the tutorial for regular expressions at https://www.vogella.com/tutorials/JavaRegularExpressions/article.html

Supplementary material to DESI's publication "New measurements of the Lyman-alpha forest continuum and effective optical depth with LyCAN and DESI Y1 data" to comply with the data management plan. Data for each figure is provided. See the README file for information on which data file corresponds to each figure.

Abstract

The Global Historical Climatology Network daily (GHCNd) is an integrated database of daily climate summaries from land surface stations across the globe. GHCNd is made up of daily climate records from numerous sources that have been integrated and subjected to a common suite of quality assurance reviews.

GHCNd contains records from more than 100,000 stations in 180 countries and territories. NCEI provides numerous daily variables, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth. About half the stations only report precipitation. Both record length and period of record vary by station and cover intervals ranging from less than a year to more than 175 years.

Methodology

The process of integrating data from multiple sources into GHCNd takes place in three steps:

1. Screening the source data for stations whose identity is unknown or questionable
2. Classifying each station in a source dataset either as one that is already represented in GHCN-D or as a new site
3. Mingling the data from the different sources

%3C!-- --%3E

The process performs the first two of these steps whenever a new source dataset or additional stations become available, while the mingling of data is part of the automated processing that creates GHCNd on a regular basis.

Screening the Source Data

A station within a source dataset is considered for inclusion in GHCNd if it meets all of the following conditions:

• It can be identified with a name, latitude, and longitude contained in metadata provided as part of the source dataset or in standard station history information
• Its record contains 100 or more values for at least one of the GHCNd elements
• It does not fail the interstation duplicate check that compares records from all stations within a source dataset in order to identify cases in which more than 50% of a station's record is identical to the data from another station

%3C!-- --%3E

Classifying Stations

The next step is to determine for each station in the source dataset if data for the same location are already contained in GHCNd, or if the station represents a new site. Whenever possible, stations are matched on the basis of network affiliation and station identification number. If no match exists, then there is consultation from different networks for existing cross-referenced lists that identify the correspondence of station identification numbers.

For example, data for Alabaster Shelby County Airport, Alabama, USA, is stored under Cooperative station ID 010116 in NCEI's datasets 3200 and 3206 as well as in the data stream from the High Plains Regional Climate Center; they are combined into one GHCNd record based on the ID. In data set 3210 and the various sources for ASOS stations, however, the data for this location are stored under WBAN ID 53864 and must be matched with the corresponding Cooperative station ID using NCEI's Master Station History Record.

A third approach is to match stations on the basis of their names and location. This strategy is more difficult to automate than the other two approaches because identification of multiple stations within the same city or town, with the same name and small differences in coordinates, can be the result of either differences in accuracy or the existence of multiple stations in close proximity to each other. As a result, the employment of the third approach is used only when stations cannot be matched on the basis of station identification numbers or cross-reference information. This is the case, for example, when there is a need for matching stations outside the U.S. whose data originate from the Global Summary of the Day dataset and from the International Collection.

Mingling

The implementation of the above classification strategies yields a list of GHCNd stations and an inventory of the source datasets for integration of each station. This list forms the basis for integrating, or mingling, the data from the various sources to create GHCNd. Mingling takes place according to a hierarchy of data sources and in a manner that attempts to maximize the amount of data included while also minimizing the degree to which data from sources with different characteristics are mixed. While the mingling of precipitation, snowfall, and snow depth are separate, consideration of maximum and minimum temperatures is performed together in order to ensure the temperatures for a particular station and day always originate from the same source. Data from the Global Summary of the Day dataset are used only if no observations are available from any other source for that station, month, and element. Among the other sources, consideration of each day is made individually; if an observation for a particular station and day is available from more than one source, GHCNd uses the observation from the most preferred source available.

Several criteria are used for the hierarchy of data sources used in cases of overlap. In gener

NOAA Ship Fairweather Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable s metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag= Z ) and which data isn t. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are good data, include this constraint in your ERDDAP query: flag=~ ZZZ........Z.* in your query. =~ indicates this is a regular expression constraint. The Z s are literal characters. In this dataset, Z indicates good data. The . s say to match any character. The * says to match the previous character 0 or more times. See the tutorial for regular expressions at www.vogella.de/articles/JavaRegularExpressions/article.html

Research Ship Knorr Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z." in your query. '=~' indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '' says to match the previous character 0 or more times. (Don't include backslashes in your query.) See the tutorial for regular expressions at https://www.vogella.com/tutorials/JavaRegularExpressions/article.html

Vegetation and elevation data were collected by the Partnership for the Delaware Estuary. Topographic Change: Transects were spaced at 3m intervals creating a 3m2 survey grid covering~0.12 hectares (1,170m2). This area included the marsh platform and mudflat to ~5m waterward of the installed breakwaters. Fixed Monitoring Plots Analyses: Mean +/- SEM for elevation, vegetation robustness, bearing capacity, and mussel density were calculated as the average of three replicate plots per position per sub-area per year. Bearing Capacity (Substrate Firmness): Measured as the penetrative capacity of a slide hammer after 5 blows. Vegetation Robustness in Fixed Monitoring Plots: Vegetation robustness integrates the horizontal and vertical obstruction of a parcel of vegetation into an overall unit-less index between 0-1 and is reported as a percentage. A score of 0% indicates no robustness, and a score of 100% indicates full robustness. By integrating the horizontal and vertical obstruction through the marsh canopy, a full picture of the three-dimensional structure of the vegetation with in the parcel is obtained. The formula for calculation was: 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝑅𝑜𝑏𝑢𝑠𝑡𝑛𝑒𝑠𝑠 = 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝐷𝑒𝑛𝑠𝑖𝑡𝑦+𝑉𝑒𝑟𝑡𝑖𝑐𝑎𝑙 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝐷𝑒𝑛𝑠𝑖𝑡𝑦2 • Horizontal Vegetation Density: measured horizontal density by counting the number of bars visible (out of 10; 10cm width each) on a 1m obstruction board from 3m away within the same band of vegetation. The count was conducted at three heights: 0.25m; 0.50m; 0.75m. The height to which data was used for calculations (number of bars available; max=30, 10 at each height) was determined by the max vegetation height as measured by Blade Height below. Calculations were as follows: 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑎𝑟𝑠 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 − 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑎𝑟𝑠 𝑣𝑖𝑠𝑖𝑏𝑙𝑒 /𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓 𝑏𝑎𝑟𝑠 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 • Blade Height: Twenty-five stems were measured, moving from the waterward corner towards the interior. Max blade height was used for the Vegetation Robustness calculation. • Vertical Vegetation Density (Canopy Cover): Five measurements of ambient light were taken above each plot (corners and center) and at the ground level (penetrative light) beneath canopy using a light meter. Calculations were as follows: 𝑉𝑒𝑟𝑡𝑖𝑐𝑎𝑙 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 = 1 − (ratio of penetrative light: ambient light)

Invertebrate, fish, and habitat data were collected as part of a cooperative project that began in 2002 between the U.S. Geological Survey, Colorado Springs Utilities, and Colorado Springs Engineering. Other entities have contributed to the project over the years including Colorado Parks and Wildlife. Habitat samples were collected and analyzed at 27 sites – 25 in the Fountain Creek Basin as well as two sites on the Arkansas River, El Paso and Pueblo Counties, Colorado. The number of sites sampled each year as well as the number of annual visits during which data were collected, and sampling methods used during each visit vary. The habitat data were collected between 2002 and 2023 and is intended to be updated annually as new data are collected and available. Users of these data are advised to read all metadata and associated documentation thoroughly to understand appropriate use and data limitations. NOTE: Although previous versions are available from the author, all the records in previous versions can be found in version 4.0. First posted - January 7, 2022 (available from author) Revised - May, 2022 (version 2.0) Revised - March, 2023 (version 3.0) Revised - March 2024 (version 4.0)

NOAA Ship Henry B. Bigelow Underway Meteorological Data (Near Real Time, updated daily) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z." in your query. "=~" indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '' says to match the previous character 0 or more times. See the tutorial for regular expressions at https://www.vogella.com/tutorials/JavaRegularExpressions/article.html

Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 10.700 % in 2011. Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 10.700 % from Dec 2011 (Median) to 2011, with 1 observations. The data reached an all-time high of 10.700 % in 2011 and a record low of 10.700 % in 2011. Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lebanon – Table LB.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Data from: Data-driven analysis of oscillations in Hall thruster simulations

- Authors: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino

- Contact email: dmaddalo@ing.uc3m.es

- Date: 2022-03-24

- Keywords: higher order dynamic mode decomposition, hall effect thruster, breathing mode, ion transit time, data-driven analysis

- Version: 1.0.1

- Digital Object Identifier (DOI): 10.5281/zenodo.6359505

- License: This dataset is made available under the Open Data Commons Attribution License

Abstract

This dataset contains the outputs of the HODMD algorithm and the original simulations used in:

Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino, "Data-driven analysis of oscillations in Hall thruster simulations", Plasma Sources Science and Technology

Which is currently in the final stages of the review process.

Dataset description

The simulations from which data stems have been produced using the full 2D hybrid PIC/fluid code HYPHEN, while the HODMD results have been produced using an adaptation of the original HODMD algorithm with an improved amplitude calculation routine.

Please refer to the relative article for further details regarding any of the parameters and/or configurations.

Data files

The data files are in standard Matlab .mat format. A recent version of Matlab is recommended.

The HODMD outputs are collected within 18 different files, subdivided into three groups, each one referring to a different case. For the file names, "case1" refers to the nominal case, "case2" refers to the low voltage case and "case3" refers to the low mass flow rate case. Following, the variables are referred as:

• "n" for plasma density
• "Te" for electron temperature
• "phi" for plasma potential
• "ji" for ion current density (both single and double charged ones)
• "nn" for neutral density
• "Ez" for axial electric field
• "Si" for ionization production term
• "vi1" for single charged ions axial velocity

In particular, axial electric field, ionization production term and single charged ions axial velocity are available only for the first case. Such files have a cell structure: the first row contains the frequencies (in Hz), the second row contains the normalized modes (alongside their complex conjugates), the third row collects the growth rates (in 1/s) while the amplitudes (dimensionalized) are collected within the last row. Additionally, the time vector is simply given as "t", common to all cases and all variables.

Citation

Works using this dataset or any part of it in any form shall cite it as follows.

The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available.

Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.6359505.

Acknowledgments

This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (MARETERRA-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). F. Terragni was also supported by the Fondo Europeo de Desarrollo Regional, Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación, under grants MTM2017-84446-C2-2-R and PID2020-112796RB-C22.

This dataset contains code and data for our paper "Better by default: Strong pre-tuned MLPs and boosted trees on tabular data". The main code is provided in pytabkit_code.zip and contains further documentation in README.md and the docs folder. The main code is also provided on GitHub. Here, we additionally provide the data that is generated by the code as well as the plots. See the documentation in docs/source/bench/download_results.md in the main code for instructions on how/when to download which data. The code for the Grinsztajn et al. (2022) benchmark is provided in grinsztajn_benchmarking_code.zip and on GitHub.

These supplementary data files were created in 2021 as part of a research paper describing the results of a research study for which data was collected in 2020. It is made up of two documents: one PDF containing the calculation of the phantomless calibration factor, and one excel file in which the raw data is presented on multiple worksheets.

The Sloan Digital Sky Survey (SDSS) Moving Object Catalog lists astrometric and photometric data for moving objects detected in the SDSS. The catalog includes various identification parameters, SDSS astrometric measurements (five SDSS magnitudes and their errors), and orbital elements for previously cataloged asteroids. The data set also includes a list of the runs from which data are included, and filter response curves.