The paper discussed sources of data. Data is a set of values of qualitative or quantitative variables. Data is facts or figures from which conclusions can be drawn. Before one can present and interpret information, there has to be a process of gathering and sorting data. Just as trees are the raw material from which paper is produced, so too, can data be viewed as the raw material from which information is obtained. It is evident from the above discussion that primary data is an original and unique data, which is directly collected by the researcher from a source such as observations, surveys, questionnaires, case studies and interviews according to his requirements

The present study investigates primary and secondary sources of organic carbon for Bakersfield, CA, USA as part of the 2010 CalNex study. The method used here involves integrated sampling that is designed to allow for detailed and specific chemical analysis of particulate matter (PM) in the Bakersfield airshed. To achieve this objective, filter samples were taken during thirty-four 23-hr periods between 19 May and 26 June 2010 and analyzed for organic tracers by gas chromatography – mass spectrometry (GC-MS). Contributions to organic carbon (OC) were determined by two organic tracer-based techniques: primary OC by chemical mass balance and secondary OC by a mass fraction method. Radiocarbon (14C) measurements of the total organic carbon were also made to determine the split between the modern and fossil carbon and thereby constrain unknown sources of OC not accounted for by either tracer-based attribution technique. From the analysis, OC contributions from four primary sources and four secondary sources were determined, which comprised three sources of modern carbon and five sources of fossil carbon. The major primary sources of OC were from vegetative detritus (9.8%), diesel (2.3%), gasoline (<1.0%), and lubricating oil impacted motor vehicle exhaust (30%); measured secondary sources resulted from isoprene (1.5%), α-pinene (<1.0%), toluene (<1.0%), and naphthalene (<1.0%, as an upper limit) contributions. The average observed organic carbon (OC) was 6.42 ± 2.33 μgC m-3. The 14C derived apportionment indicated that modern and fossil components were nearly equivalent on average; however, the fossil contribution ranged from 32-66% over the five week campaign. With the fossil primary and secondary sources aggregated, only 25% of the fossil organic carbon could not be attributed. Whereas, nearly 80% of the modern carbon could not be attributed to primary and secondary sources accessible to this analysis, which included tracers of biomass burning, vegetative detritus and secondary biogenic carbon. The results of the current study contributes source-based evaluation of the carbonaceous aerosol at CalNex Bakersfield. This dataset is associated with the following publication: Sheesley, R., P. Dev Nallathamby, J. Surratt, A. Lee, M. Lewandowski, J. Offenberg, M. Jaoui, and T. Kleindienst. Constraints on primary and secondary particulate carbon sources using chemical tracer and 14C methods during CalNex-Bakersfield. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 166: 204-214, (2017).

Gross enrolment ratio, primary and secondary, male (%) in Myanmar was reported at 87.07 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Myanmar - Gross enrolment ratio, primary and secondary, male - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Students can explore some of the Library of Virginia’s collections and learn how they are conserved! The Library of Virginia is the oldest cultural institution in the state and the official archive (a place where history is kept) and library of the Commonwealth. In the book To Collect, Protect, and Serve: Behind the Scenes at the Library of Virginia, Archie the Archivist, Libby the Librarian, and Connie the Conservator guide young readers through a visit to the Library of Virginia. Check out these To Collect, Protect, and Serve worksheet activities.

This is a conference presentation that later evolved into the manuscripts:Anthony, K. & Morgan, M. K. (2015). Ulysses S. Grant Manumits William Jones: An Example of America’s Entanglement with Slavery. Middle Level Learning, 54, pp. 2-5.

Morgan, M.K. & Anthony, K. (2015). Ulysses S. Grant Manumits William Jones: America’s Entanglement with Slavery, A Lesson for Grades 6-8. Middle Level Learning, 54, pp. 6-16.

Global Primary to Post-Secondary Non-Tertiary Education Expenditure from Private Sources by Country, 2023 Discover more data with ReportLinker!

The purpose of the collection of outpatient health statistics is to monitor, evaluate and plan curative and preventive health care at the primary and secondary level of health care system.

Data on outpatient statistics are an important source of information for population health monitoring indicators
and accessibility of outpatient health care activities in Slovenia. Health care providers collect data for each individual contact of the patients with the health service. It is reported by public and private healthcare providers.

Outpatient health statistics record contacts and services at general practicioners and specialist outpatient activities at the secondary level.

Gross enrolment ratio, primary and secondary, female (%) in Honduras was reported at 81.63 % in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. Honduras - Gross enrolment ratio, primary and secondary, female - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

This table contains some of the science results from the Nuclear Spectroscopic Telescope Array (NuSTAR) Serendipitous Survey. The catalog incorporates data taken during the first 40 months of NuSTAR operation, which provide ~20 Ms of effective exposure time over 331 fields, with an areal coverage of 13 deg². The primary catalog (available as the HEASARC NUSTARSSC table) contains 498 sources (the abstract of the reference paper states that there are 497 sources) detected in total over the 3-24 keV energy range. There are 276 sources with spectroscopic redshifts and classifications, largely resulting from the authors' extensive campaign of ground-based spectroscopic follow-up. The authors characterize the overall sample in terms of the X-ray, optical, and infrared source properties. The sample is primarily composed of active galactic nuclei (AGN), detected over a large range in redshift from z = 0.002 to 3.4 (median redshift z of 0.56), but also includes 16 spectroscopically confirmed Galactic sources. There is a large range in X-ray flux, from log (f_3-24_keV) ~ -14 to -11 (in units of erg s^-1 cm^-2), and in rest-frame 10-40 keV luminosity, from log (L_10-40keV) ~ 39 to 46 (in units of erg s^-1), with a median of 44.1. Approximately 79% of the NuSTAR sources have lower-energy (<10 keV) X-ray counterparts from XMM-Newton, Chandra, and Swift XRT observations. The mid-infrared (MIR) analysis, using WISE all-sky survey data, shows that MIR AGN color selections miss a large fraction of the NuSTAR-selected AGN population, from ~15% at the highest luminosities (L_X > 10⁴⁴ erg s^-1) to ~80% at the lowest luminosities (L_X < 10⁴³ erg s^-1). The authors' optical spectroscopic analysis finds that the observed fraction of optically obscured AGN (i.e., the type 2 fraction) is F_Type2 = 53 (+14, -15) per cent, for a well-defined subset of the 8-24 keV selected sample. This is higher, albeit at a low significance level, than the type 2 fraction measured for redshift- and luminosity-matched AGNs selected by < 10 keV X-ray missions. This table contains the Secondary NuSTAR Serendipitous Source Catalog of 64 sources found using wavdetect to search for significant emission peaks in the FPMA and FPMB data separately (see Section 2.1.1 of Alexander et al. 2013, ApJ, 773, 125) and in the combined A+B data. These sources are listed in Table 7 of the reference paper. This method was developed alongside the primary one (Section 2.3 of the reference paper) in order to investigate the optimum source detection methodologies for NuSTAR and to identify sources in regions of the NuSTAR coverage that are automatically excluded in the primary source detection. The authors emphasize that these secondary sources are not used in any of the science analyses presented in their paper. Nevertheless, these secondary sources are robust NuSTAR detections, some of which will be incorporated in future NuSTAR studies, and for many of them (35 out of the 43 sources with spectroscopic identifications) the authors have obtained new spectroscopic redshifts and classifications through their follow-up program. The X-ray photometric parameters for 4 sources are left blank as in these cases the A+B data prohibit reliable photometric constraints. Additional information on these Secondary Catalog sources that the authors obtained using optical spectroscopy is available in Table 8 of the reference paper (q.v.). This table does NOT contain the the 498 sources in the Primary NuSTAR Serendipitous Source Catalog that were found using the source detection procedure described in Section 2.3 of the reference paper, and that are listed in Table 5 (op. cit.). This table was created by the HEASARC in July 2017 based on the machine-readable version of Table 7 from the reference paper, the Secondary NuSTAR Serendipitous Source Catalog, that was obtained from the ApJ web site. This is a service provided by NASA HEASARC .

The dataset contains the data collected in the survey on the use of dictionaries and other lexicographic resources in Croatian primary and secondary education, which was conducted from 1 February to 17 February 2023.

The study used an explanatory sequential mixed method design. This method is appropriate for examining the employment status of STEM graduates in terms of gender as well as the time it takes for graduates to secure their first job after graduating. The method is also employed to look at how staff in higher education supports female graduates in their search for employment after graduation. By design, this study collects data in a sequential fashion, starting with quantitative data and moving on to qualitative data that provide context for the quantitative data.Both primary and secondary sources of data were employed in the study (See Figure A). While information from secondary sources was gathered using Eric, Scopus, and Google search engines, information from primary sources was gathered through questionnaires and interviews. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) was used to conduct the analysis. Using the keywords employment status, duration of job search, and gender-responsive support of higher education, the first 221 articles were collected. Only 15 articles were chosen when PRISMA used the inclusion and exclusion criteria to filter out publications gathered between 2012 and 2024. The information gathered from secondary sources was utilized to triangulate the findings of the primary data sources. The following figure shows the data sources.Figure A: Data sources for the study (see the Description Word Doc. in the dataset)Based on the explanatory sequential mixed method design, quantitative data analysis was first carried out. In order to determine whether there were statistical differences in the employment status and the time it took for male and female STEM engineering graduates to find jobs, the chi square test was employed. An analysis of the degree to which higher education institutions assist female graduates in their job search was also done using an independent samples t-test. The viewpoints of academics from these related universities and prospective employers of STEM graduates were captured through the use of qualitative data.

National Council for the Social Studies Conference Presentation based on our work helping elementary and middle school teachers and teacher candidates learn how to effectively use primary sources from the Library of Congress in the classroom.

Analysis of ‘BPA20 - Current Account: Primary and Secondary Income BPM6’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/82b0796b-9cb5-4fa5-8365-1ff329c51814 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Current Account: Primary and Secondary Income BPM6

--- Original source retains full ownership of the source dataset ---

Gross enrolment ratio, primary and secondary, both sexes (%) in Poland was reported at 104 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Poland - Gross enrolment ratio, primary and secondary, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Conference presentation with a goal to help inservice teachers use the Library of Congress and primary sources to engage their students in historical thinking.

Analysis of ‘BPA26 - Current Account: Primary and Secondary Income BPM6’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/42936777-71a1-4063-b308-7d0750a6b5d8 on 15 January 2022.

--- Dataset description provided by original source is as follows ---

Current Account: Primary and Secondary Income BPM6

--- Original source retains full ownership of the source dataset ---

Analysis of ‘BPA22 - Current Account: Primary and Secondary Income BPM6’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/627f6f1a-a2f1-49ce-a855-f82a4bc85962 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Current Account: Primary and Secondary Income BPM6

--- Original source retains full ownership of the source dataset ---

Primary and secondary energy by fuel type in terajoules (coal, natural gas, steam, etc.) and supply and demand characteristics (production, exports, imports, inter-regional transfers, etc.).

Given the considerable financial and logistical resources supporting long-term monitoring for air pollutants, and the use of these data for performance evaluation of mitigation measures, it is important to account for contributions from primary versus secondary sources. We demonstrate a simple approach for using open source Global land cover raster data from the National Mapping Organization from the Geospatial Information Authority of Japan to assess local source inputs for air measurements of legacy persistent organic pollutants (POPs)polychlorinated biphenyls (PCBs) and organochlorine pesticidesreported under the Global atmospheric passive sampling (GAPS) Network at 119 locations for the time period 2005–2014. The land cover composition within a 10 km radius around the GAPS sites was identified to create source impact indicator (SII) vectors to quantify and rank the remoteness of the sites from human infrastructure. Using principal component analysis, three SII vectors were established to rank sites by impact of (i) general infrastructure/remoteness, (ii) urban infrastructure, and (iii) agricultural infrastructure. General infrastructure describes the combined effects of settlements and agricultural infrastructure. We found significant correlations (p < 0.05) between POP concentrations in air and specific SIIs. PCB levels in air had a statistically significant correlation to the SII ranking urban impacts around the sampling sites, while Endosulfan I, Endosulfan II, and Endosulfan sulfate had a statistically significant correlation with SII ranking agricultural impacts. The complete GAPS data set from 2004–2014 (1040 samples at 119 locations) was standardized based on the SII rankings to assess the global temporal trends of legacy POPs. SIIs were incorporated in the multiple regression analysis to determine global halving times. This includes short-term monitoring data from 79 locations that were previously excluded. Furthermore, the SII approach allows the integration of global monitoring data from different studies for broader global temporal trend analysis. This ability to link the results of independent and small-scale studies can enhance temporal trend analysis in support of the larger scale initiatives, such as inter alia, the Global Monitoring Plan and Effectiveness Evaluation of the Stockholm Convention in the case of POPs. This simple approach using open source data has a broad potential for application for other classes of air pollutants.

Analysis of ‘New Mexico, 2010 Census, Primary and Secondary Roads’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/651ad94d-acd4-4862-89d3-70678d38a8cd on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

--- Original source retains full ownership of the source dataset ---