Source data table containing raw data.

Raw data was used to make table 1 and table 2

The data tables contain results from laboratory experiments published in the citation below.

File List data.txt Description The "data.txt" file is a tab-delimited text file containing the raw data used in this meta-analysis. Column definitions: interaction ID: unique numeric identification number for each pairwise resource-consumer interaction system: the location of study latitude: latitude of system in degree-minute-seconds longitude: longitude of system in degree-minute-seconds ecosystem type: the broad habitat category (i.e. aquatic, including marine and freshwater subtypes, or terrestrial) ecosystem subtype: the specific habitat category (e.g. temperate forest or freshwater) study type: observational or experimental event: the specific occurrence of a primary resource pulse in time pulse duration (d): the length of time that resource availability was more than 10% greater than the baseline condition in days response duration (d): the length of time that consumer densities or recruitment were more than 10% greater than the baseline condition in days resource: short description of the resource identity consumer: short description of the consumer identity trophic level of resource: the integer trophic level of the dominant pulsed resource (as described in text) consumer trophic level: the integer trophic level of the consumer (as described in text) consumer trophic position: autotrophy or heterotrophy consumer trophic distance: the minimum number of trophic levels between the focal consumer and the primary pulsed resource R baseline, Rb: resource availability in the baseline state R pulse, Rp: maximum resource availability in the pulsed state R units: units of resource availability R ratio: Rp/Rb ln (R ratio): ln(Rp/Rb), resource pulse magnitude C baseline, Cb: consumer density or recruitment in the baseline state C pulse, Cp: maximum consumer density or recruitment in the pulsed state C units: units of consumer density C ratio: Cp/Cb ln(C ratio): ln(Cp/Cb), consumer response magnitude ln(C ratio/R ratio): ln[(Cp/Cb)/(Rp/Rb)], relative response magnitude, measures the magnitude of consumer responses relative to their resource pulses estimated consumer body mass (g): the average mass of the consumer at reproduction estimated consumer generation time (d): the average interval of time between the birth, germination or division of the consumer and the birth, germination or division of their offspring in days response lag (d): the length of time between the observed peak of resource availability and the observed peak consumer density in days consumer response mechanism: the primary mode of numerical response (i.e. reproductive, aggregative, or combined reproductive and aggregative) reference(s): key literature citations, see manuscript notes: additional notes

Raw data used in the submitted paper Nativ and Turowski. See Table 2 therein.

This layer shows education level for adults (25+) by race by sex. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent of adults age 25+ who have a bachelor's degree or higher as their highest education level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B15002, C15002B, C15002C, C15002D, C15002E, C15002F, C15002G, C15002H, C15002I (Not all lines of these ACS tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Contains the plain csv files of the ID tables and link tables as well as a geopackage of the base geometries from which most spatial datasets of HGIS de las Indias (especially doi.org/10.7910/DVN/JSL0ND) are processed, using an automated workflow (doi:10.7910/DVN/FIK7RH). Also includes three intermediate dump tables with a crucial function between raw data and output. Does not include "aggregated data". Raw tables for aggregated data are stored along with the respective resulting geodata. Raw tables: -gz_entidades (place entities) -gz_cabildo (municipal institutions of places, over time) -gz_categoria (settlement type of places, over time) -gz_iglesia (church institutions of places over time) -gz_nombres (main names of places, over time) -entidades (territorial entities) -infotable (instances of territorial entities, over time) -LCG_All_levels (relates LCG features with territorial entities over time) -entidades_fuentes (relates used sources to territorial entities) -entidades_wiki (descriptive comments on territorial entities, in wiki format) Geopackage: -the_geom.gpkg -LCG (polygon layer). "Least common geometries" (areas that share the same information across the database) -gz_the_geom (point layer). Coordinates of the place entities, over time Intermediate dump files: -gz_info_1 ("places" - a computed aggregate of the tables with prefix gz_ with a unified chronology) -Niveles (relates corresponding instances of territorial entities to LCG_All_levels, over time) -cabeceras (capital cities, relates gz_info_1 and Niveles over time)

Raw data table

BackgroundShigella sonnei is a pathogen of growing global importance as a cause of diarrhoeal illness in childhood, particularly in transitional low-middle income countries (LMICs). Here, we sought to determine the incidence of childhood exposure to S. sonnei infection in a contemporary transitional LMIC population, where it represents the dominant Shigella species.MethodsParticipants were enrolled between the age of 12–36 months between June and December 2014. Baseline characteristics were obtained through standardized electronic questionnaires, and serum samples were collected at 6-month intervals over two years of follow-up. IgG antibody against S. sonnei O-antigen (anti-O) was measured using an enzyme-linked immunosorbent assay (ELISA). A four-fold increase in ELISA units (EU) with convalescent IgG titre >10.3 EU was taken as evidence of seroconversion between timepoints.ResultsA total of 3,498 serum samples were collected from 748 participants; 3,170 from the 634 participants that completed follow-up. Measures of anti-O IgG varied significantly by calendar month (p = 0.03). Estimated S. sonnei seroincidence was 21,451 infections per 100,000 population per year (95% CI 19,307–23,834), with peak incidence occurring at 12–18 months of age. Three baseline factors were independently associated with the likelihood of seroconversion; ever having breastfed (aOR 2.54, CI 1.22–5.26), history of prior hospital admission (aOR 0.57, CI 0.34–0.95), and use of a toilet spray-wash in the household (aOR 0.42, CI 0.20–0.89).ConclusionsIncidence of S. sonnei exposure in Ho Chi Minh City is substantial, with significant reduction in the likelihood of exposure as age increases beyond 2 years.

This is a raw data dataset from my family's grocery store in Mexico, the biggest file is the raw database with over 100 tables, the largest being over 5 million rows. Information in this dataset starts in 2014 when we installed the sales software. Latest data is Oct/2022, which is when I pulled the data to explore and practice with it. Includes sales, item description, Id's, dates, etc. With it you can do whatever you want, from weekly, monthly and yearly sales, to finding what's the most selling product during the weekend or on a Tuesday.

The excel file is cleaned data that I pulled from the raw data, includes some charts and filtered and sorted information. Some tables and column names might be in Spanish, hopefully that is not a big problem for you to explore the data!

This data set contains the raw strain data output from the 3D DIC process.

These are the raw data files to the NQR measurements presented in https://doi.org/10.1039/C8CP03848A TABLE 1. T2 measurements were performed using spin echo sequence with variable echo time as explained section 3, Experimental details. T2 has been found by fitting a monoexponential decay.

The best-fit parameters for R2 = 1/T2 and FWHM can be found Table 1 in the column "experimental relaxation rate"

The names of the files follow the labelling: SampleName_transitionNr_temperture_sequence type.txt

the organization of the txt-files is as follows:

header: file source, number of frequency-points (Starting Point, End Point) and number of records (Starting 2D Record, Ending Record) = number of parameter sweeps (i.e. echo time)

Data set: records stacke on top of each other:

real part [a.u.] / imaginary part [a.u.] / frequency shift [kHz]

the frequency is given in difference from the central frequency (Obeserve Freq.), which can be found in the footer

footer: contains information on the used sequence and its paramteres. eg.g the "Obeserve Freq" of the spectrometer during the sequence, (excitiation and detection frequency)

also: filter bandwidth, dwell time, delay time, pulse length ....

the paramter "step size" of the sweep parameter (echo time or inversion time, respectively) is available on request: christian.goesweiner@tugraz.at

The Galileo Probe Energetic Particles Investigation (EPI) Raw Data Set contains two tables of the EPI raw data values sorted by sampling time. The counter table contains the raw counter values as measured and the countrate table contains the countrates as derived from counter values, but without any correction. The tables are split into omnidirectional and sectorized data. The distance to Jupiter is given in Jupiter radii, Rj, and was derived from the Probe trajectory data. The time of probe entry is taken to be 1995-12-07T22:04:44Z when the probe reached an altitude of 450 km above the 1 bar pressure level.

Surface elevation table (SET) measurements (raw data) from 6 marsh sites along the Rowley River, Rowley, MA. SET measurements are useful for determining the relative elevation change of marsh sediments. Precise measurements of sediment elevation in marshes is useful for determining rates of elevation change in response to changes in sea level.

Raw data of expression of lipid metabolism related proteins in paired primary and metastatic breast cancer (Table B). (DOCX)

Information on data sources for field analyzer manuscript calculations. This dataset is not publicly accessible because: This data was not generated by EPA, but rather used by EPA researchers to calculate basic statistics (R square and slope), as part of this literature review. It can be accessed through the following means: These two old conference proceedings are available in book volumes that can be found in libraries, with page numbers as specified below: - Argent, V.A., Southall, J.M. and D'Costa, E. (1994) Analysis of water for lead and copper using disposable sensor technology. American Water Works Association – Annual Conference, pp. 43-54, New York, New York. - Wiese, P.M. (1989) Monitoring method for lead in first-draw drinking water samples. American Water Works Association - Annual Conference and Exposition, pp. 1309-1313, Los Angeles, California. Format: Data from three tables in two old conference proceedings were used to calculate basic statistics (R square and slope): - Table 2 and 4 in Proceeding "Argent, V.A., Southall, J.M. and D'Costa, E. (1994) Analysis of water for lead and copper using disposable sensor technology. American Water Works Association – Annual Conference, pp. 43-54, New York, New York." - Table 2 in Proceeding "Wiese, P.M. (1989) Monitoring method for lead in first-draw drinking water samples. American Water Works Association - Annual Conference and Exposition, pp. 1309-1313, Los Angeles, California.". This dataset is associated with the following publication: Dore, E., D. Lytle, L. Wasserstrom, J. Swertfeger, and S. Triantafyllidou. Field Analyzers for Lead Quantification in Drinking Water Samples. CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY. CRC Press LLC, Boca Raton, FL, USA, 50(20): 999-999, (2020).

Data package purpose: This data package was created to contain all data used in an upcoming article, "Canopy Cover and Microtopography Control Precipitation-Enhanced Thaw of Ecosystem-Protected Permafrost." In review.This data package includes: Raw output from 19 distributed temperature profilers with a thermistor every 10 cm along a 160 cm length at a measurement interval of 15 minutes (.CSV). Raw output from two soil moisture and temperature profilers (90 cm length and 120 cm length) that took composite soil moisture readings every 15 cm along the sensor length at a measurement interval of 30 minutes (.CSV). Permafrost depths were measured annually in mid-September at DTP sensor locations (.CSV) and along an across-site transect (.CSV). Environmental variables (snow depth, canopy closure, moss depth, and elevation) for all sensor locations. Real-time kinetic (RTK) GPS points showing site microtopography (.CSV).Analysis software: Our analysis was done in Matlab. File types can be used with any software.

44 sheets of data, each sheet representing a table from the database that stores the information. The order of the sheets is based on the sequence of reporting forms for the Financial Transactions Report.

Raw data describing the samples distributions with different methodologies.

The Galileo Probe Energetic Particles Investigation (EPI) Raw Data Set contains two tables of the EPI raw data values sorted by sampling time. The counter table contains the raw counter values as measured and the countrate table contains the countrates as derived from counter values, but without any correction. The tables are split into omnidirectional and sectorized data. The distance to Jupiter is given in Jupiter radii, Rj, and was derived from the Probe trajectory data. The time of probe entry is taken to be 1995-12-07T22:04:44Z when the probe reached an altitude of 450 km above the 1 bar pressure level.