The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

The Chrysler Herbarium (CHRB) at Rutgers University is the last internationally recognized herbarium still in existence in the state of New Jersey (USA). Over 150,000 vascular plant and algal collections, about 7,000 moss and liverwort specimens, and 2,600 lichen specimens form our collection and are arranged and catalogued systematically. The collection is worldwide in scope, with an emphasis on New Jersey and the Mid-Atlantic area, and contains specimens back to the early 1800s. The Rutgers Mycological Herbarium (RUTPP), which is housed together with CHRB, has been estimated to contain more than 40,000 fungal collections, and has a strong focus on microfungi and plant pathogens. Dr. James White is the curator of the mycological collections, and Dr. Lena Struwe is the Director of the Chrysler Herbarium.

Sexual harassment (SH) is a prevalent issue within higher education, impacting individuals, groups, and institutions globally. Despite its significance, SH often goes unreported and under-researched, particularly in regions like sub-Saharan Africa. In Tanzania, limited data exists on this subject. A recent study at Kilimanjaro Christian Medical University College aimed to assess the prevalence and types of gender-based SH among students and staff. Findings revealed a concerning 37% prevalence among staff and 32% among students, with a significant number reporting experiences of SH within the institution. Awareness of support services and reporting mechanisms was found to be low, highlighting the need for improved resources and education on addressing SH effectively.

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.

EGRA Details:

"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.

To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)

The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.

As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.

In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."

Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.

The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey

This project's goal was to maximise the returns of investing in the lives of young people by deriving specific lessons for inclusion policies in education and disseminating the findings among key policy makers. This dataset provides information on how classrooms are formed in 127 Chilean schools.Equality of opportunity is considered by many a basic human right. It is achieved when everybody can reach their full potential, and nobody is limited by the circumstances of their own birth. However, today millions of youths around the world face persistent gaps in opportunity. This is both a social and an economic issue, because economic potential is lost when many of our youth do not have access to safe environments, high quality education and employment opportunities. This project focuses on ways to eradicate disparities in education and their consequences for labour market opportunities. In particular, it uses data from innovative inclusion policies in Chile, a country characterized by high income inequality, to find ways to close these opportunity gaps early on, i.e., before university enrolment and labour market entry. The goal of this research is not only to provide a scientific evaluation of educational policies in Chile, but also to draw practical public policy lessons that can be useful to any country. To achieve this, the project combines exceptionally detailed data with structural modelling. Most of the data have already been or will be collected by the Chilean Ministry of Education in Chile. They will be complemented with a small data collection carried out by the candidate at a minimal cost, leveraging on established relationships with research users in-country. Structural modelling is the analysis of the mechanisms through which policies work. It is what allows us to extrapolate, from specific contexts, general conclusions that are applicable to many countries. The project addresses three related research questions. First, it evaluates an affirmative action programme called PACE (Programa de Acceso Efectivo y Acompanamiento a la Educacion Superior), which guarantees admission to university to the best students in disadvantaged high schools in Chile. The study will use a Randomized Control Trial that exploits the planned programme roll-out to scientifically evaluate programme effectiveness and to identify the key ingredients for inclusion policy success. Second, it determines if differences exist in the effectiveness of the pilot programme for PACE, the Propedeutico programme, between high schools that do and that do not stream students of similar ability into the same classes. In doing so, the study extends our understanding of the role of tracking and peers in the production of achievement. For example, findings will determine if students compete more fiercely for university admission when they are in classrooms with similar peers. Third, it evaluates the impact of higher education on disadvantaged youth. To do so, it uses cut-off rules for university admission to apply a policy evaluation technique known as regression discontinuity. The benefits of higher education on the academic and labour market outcomes of disadvantaged youths are not well understood because very few poor students are observed enrolling in university. Because these are the very students that inclusion policies target, evaluating the benefits for them is of paramount importance for policy makers and researchers. This project's goal is to maximise the returns of investing in the lives of young people. This not only reduces the vast human cost of inequality, but it also increases aggregate earnings and economic growth. Due to the candidate's network in academia and in the public sector in Chile (including in the Chilean Ministry of Education), the results of the study can have a direct and immediate impact on the educational policy discourse in Chile. Over the years, the Chilean Governments have shown willingness to enact reforms that have a strong evidence-base. Therefore, potentially hundreds of thousands of poor children in Chile can be directly affected in the short term. Other countries could then follow the Chilean example, amplifying the potential impact to millions of underprivileged and talented children around the world. 127 head-teachers in Chile, spread around the country.

BackgroundUniversities undertake the majority of publicly funded research in Germany and hence bear a responsibility to contribute to global health efforts. So far, involvement and impact of German medical faculties in global health are unknown. Our aim was to systematically asses and evaluate German medical faculties’ contribution to global health related research and education, as well as their policies and practices concerning open access publishing and equitable licensing.MethodsWe assessed the involvement in global health of all 36 publicly funded medical faculties in Germany during 2010–2014 in three areas: innovation, access and education, using the following indicators: research funding and publications focused on global health or poverty-related and neglected diseases; open access publishing and policies promoting access to medical innovations worldwide; provision of global health education. Data were gathered from public databases, university websites and questionnaires sent to individual universities for validation and triangulation.ResultsThere was a high level of variability between institutions and indicators. The proportion of research funding for poverty-related and neglected diseases research ranged between 0.0–1.1%. The top five institutions received nearly 85% of the total poverty-related and neglected diseases research funding. 20 of 36 universities had an institutional open access publishing policy, 19 had an open access publishing fund, 16 had neither. Only one university reported having used an equitable licensing policy. 22 of 36 faculties provided some global health education, but only one of them included global health in their core undergraduate medical curriculum as a compulsory course with more than just single lectures.ConclusionObtained data indicate that global health and poverty-related and neglected diseases research at German medical faculties is highly concentrated in a few institutions, open-access publishing and equitable licensing policies are mostly absent, and only little global health education exists. Universities and government should address global health strategically in both research and education at medical faculties to reflect the country’s economic and political weight and human resource potential.

Number of Students: Haryana: Colleges data was reported at 668,674.000 Person in 2021. This records an increase from the previous number of 632,781.000 Person for 2020. Number of Students: Haryana: Colleges data is updated yearly, averaging 570,885.500 Person from Sep 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 668,674.000 Person in 2021 and a record low of 203,834.000 Person in 2010. Number of Students: Haryana: Colleges data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.

The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by USGS GAP, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The State, Regional and LCC geodatabases contain two feature classes. The PADUS1_3_FeeEasement feature class and the national MPA feature class. Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new Date of Establishment field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas. Additional dates will be provided in future updates. 4) A national wilderness area update from wilderness.net 5) The Access field that describes public access to protected areas as defined by data stewards or categorical assignment by Primary Designation Type. . The new Access Source field documents local vs. categorical assignments. See the PAD-US Standard Manual for more information: gapanalysis.usgs.gov/padus 6) The transfer of conservation measures (i.e. GAP Status Codes, IUCN Categories) and documentation (i.e. GAP Code Source, GAP Code Date) from PAD-US version 1.2 or categorical assignments (see PAD-US Standard) when not provided by data stewards 7) Integration of non-sensitive National Conservation Easement Database (NCED) easements from August 2011, July 2012 with PAD-US version 1.2 easements. Duplicates were removed, unless 'Stacked' = Y and multiple easements exist. 8) Unique ID's transferred from NCED or requested for new easements. NCED and PAD-US are linked via Source UID in the PAD-US version 1.3 Easement feature class. 9) Official (member and eligible) MPAs from the NOAA MPA Inventory (March 2011, www.mpa.gov) translated into the PAD-US schema with conservation measures transferred from PAD-US version 1.2 or categorically assigned to new protected areas. Contact the PAD-US Coordinator for documentation of categorical GAP Status Code assignments for MPAs. 10) Identified MPA records that overlap existing protected areas in the PAD-US Fee feature class (i.e. PADUS Overlap field in MPA feature class). For example, many National Wildlife Refuges and National Parks are also MPAs and are represented in the PAD-US MPA and Fee feature classes.

India Number of Students: Colleges data was reported at 26,552,301.000 Person in 2017. This records an increase from the previous number of 26,388,693.000 Person for 2016. India Number of Students: Colleges data is updated yearly, averaging 23,470,323.500 Person from Sep 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 26,552,301.000 Person in 2017 and a record low of 11,551,516.000 Person in 2010. India Number of Students: Colleges data remains active status in CEIC and is reported by Department of Higher Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.

The precise location and geometry of oceanic spreading centers and associated transform faults or discontinuities' boundary has fundamental implications in our understanding of oceanic accretion, the accommodation of deformation around rigid lithospheric blocks, and the distribution of magmatic and volcanic processes. The now widely used location of mid oceanic ridges worldwide, published by P. Bird in 2003, can be updated based on recent publicly available and published ship-based multibeam swath bathymetry data (100-m resolution or better), now available to ~25% of the ocean seafloor, but covering a significant proportion of the mid-ocean ridge system (>70%). Here we publish the MAPRIDGES database built under the coordination of CGMW (Commission for the Geological Map of the World), with a first version V1.0 (06/2024) that provides high resolution and up-to-date datasets of mid-ocean ridge segments and associated transform faults, and follow-up updates that will also include non-transform offsets. The detailed mapping of individual mid oceanic ridge segments was conducted using GMRT (Ryan et al., 2009) (version 4.2 for MAPRIDGES V1.0), other publicly available datasets (e.g., NCEI, Pangaea, AWI), and existing literature. MAPRIDGES will be revised with the acquisition of additional datasets, new publications, and correction of any errors in the database. The MAPRIDGE database was built in a GIS environment, where each feature holds several attributes specific to the dataset. We include three different georeferenced shapefile layers: 1) Ridge Segments, 2) Transform Faults, and 3) Transform Zones. The latest corresponds to zones of distributed strike-slip deformation that lack a well-defined fault localizing strain, but that are often treated as transform faults. 1) The Ridge Segments Layer contains 1461 segments with 9 attributes: - AREA_LOCA: The Name of the Ridge System - LOC_SHORT: The short form of the Ridge System using 3 characters - LAT: The maximum latitude of the ridge segment - LONG: the maximum longitude of the ridge segment - LENGTH: the length of the ridge segment in meters - CONFIDENCE: the degree of confidence on digitization based on the availability of high-resolution bathymetry data: 1 = low to medium confidence, 2 = high confidence - REFERENCES: supporting references used for the digitization - NAME_CODE: unique segment code constructed from the LOC_SHORT and LAT attributes in degree, minute, second coordinate format - NAME_LIT: name of the segment from the literature if it exists 2) The Transform Fault Layer contains 260 segments with 4 attributes: - NAME_TF: Name of the transform fault according to the literature - LENGTH: length of the transform fault in meters - LAT: The maximum latitude of the fault segment - LONG: the maximum longitude of the fault segment 3) The Transform Zone layer contains 10 segments with 4 attributes: - NAME_TF: Name of the transform zone according to the literature - LENGTH: length of the transform fault in meters - LAT: The maximum latitude of the fault segment - LONG: the maximum longitude of the fault segment To facilitate revisions and updates of the database, relevant information, corrections, or data could be sent to B. Sautter (benjamin.sautter@univ-ubs.fr) and J. Escartín (escartin@geologie.ens.fr).

United Arab Emirates Number of Graduates: Colleges and High Education data was reported at 26,040.000 Person in 2017. This records an increase from the previous number of 24,667.000 Person for 2016. United Arab Emirates Number of Graduates: Colleges and High Education data is updated yearly, averaging 13,700.000 Person from Jun 1994 (Median) to 2017, with 23 observations. The data reached an all-time high of 26,040.000 Person in 2017 and a record low of 2,407.000 Person in 1994. United Arab Emirates Number of Graduates: Colleges and High Education data remains active status in CEIC and is reported by Ministry of Planning. The data is categorized under Global Database’s United Arab Emirates – Table AE.G005: Education Statistics.

The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government.

Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized.

Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers.

Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.

Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis.

Number of Colleges: Kerala data was reported at 1,332.000 Unit in 2021. This records an increase from the previous number of 1,328.000 Unit for 2020. Number of Colleges: Kerala data is updated yearly, averaging 1,239.000 Unit from Sep 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 1,332.000 Unit in 2021 and a record low of 556.000 Unit in 2010. Number of Colleges: Kerala data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD002: Number of Colleges.

Number of Students: Delhi: Colleges data was reported at 299,597.000 Person in 2021. This records an increase from the previous number of 281,983.000 Person for 2020. Number of Students: Delhi: Colleges data is updated yearly, averaging 261,089.000 Person from Sep 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 299,597.000 Person in 2021 and a record low of 150,323.000 Person in 2010. Number of Students: Delhi: Colleges data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.

Number of Students: West Bengal: Colleges data was reported at 1,795,258.000 Person in 2021. This records an increase from the previous number of 1,679,228.000 Person for 2020. Number of Students: West Bengal: Colleges data is updated yearly, averaging 1,554,327.000 Person from Sep 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 1,795,258.000 Person in 2021 and a record low of 609,140.000 Person in 2010. Number of Students: West Bengal: Colleges data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.

No of Students: Professional Colleges: Others: Male data was reported at 182.000 Person in 2016. This records a decrease from the previous number of 189.000 Person for 2015. No of Students: Professional Colleges: Others: Male data is updated yearly, averaging 176.000 Person from Jun 1997 (Median) to 2016, with 20 observations. The data reached an all-time high of 205.000 Person in 2010 and a record low of 92.000 Person in 1997. No of Students: Professional Colleges: Others: Male data remains active status in CEIC and is reported by Pakistan Bureau of Statistics. The data is categorized under Global Database’s Pakistan – Table PK.G010: Education Statistics: Number of Students.

ObjectiveSelf-medication has emerged as a significant global public health concern. Despite possessing a certain level of medication knowledge, university students in China exhibit a high-risk profile regarding self-medication practices. This study aimed to systematically investigate the current status and influencing factors of self-medication among university students in Guangdong Province, China, thereby providing evidence-based recommendations for targeted intervention strategies.MethodsA cross-sectional study was conducted based on the Knowledge-Attitude-Practice (KAP) model. Data were collected via anonymous questionnaire surveys distributed to university students in Guangdong Province, China. A total of 816 valid responses were analyzed. The questionnaire assessed demographic characteristics along with dimensions of medication knowledge, attitudes, and practices. Multiple linear regression analyses were subsequently performed to evaluate the impact of demographic factors on each dimension of the KAP model.ResultsStudents demonstrated a relatively high overall qualification rate in medication knowledge (93.50%), with 43.38% achieving a “Good” level and 50.12% rated as “Fair.” However, noticeable deficiencies were identified in attitudes and practices, with qualification rates approximately 75% in both dimensions. Notably, only 6.50% achieved a “Good” level in medication practices, while a substantial proportion (24.26%) was rated as “Unqualified.” Regression analyses revealed that age, current academic stage, and study mode significantly influenced medication knowledge scores. No significant demographic factors were associated with medication attitudes. However, age and the primary source of medication information significantly impacted self-medication practices. These findings offer empirical evidence essential for developing targeted medication safety education interventions among university students.ConclusionA clear discrepancy between knowledge and practice regarding self-medication exists among university students in Guangdong Province, China. Comprehensive intervention strategies are therefore, urgently required to promote rational medication behaviors within this population.

Number of Students: Karnataka: Colleges data was reported at 1,769,083.000 Person in 2021. This records an increase from the previous number of 1,661,251.000 Person for 2020. Number of Students: Karnataka: Colleges data is updated yearly, averaging 1,429,524.000 Person from Sep 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 1,769,083.000 Person in 2021 and a record low of 1,178,153.000 Person in 2010. Number of Students: Karnataka: Colleges data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.

Sri Lanka Number of University Lecturers data was reported at 5,498.000 Person in 2017. This records an increase from the previous number of 5,440.000 Person for 2016. Sri Lanka Number of University Lecturers data is updated yearly, averaging 3,390.000 Person from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 5,610.000 Person in 2014 and a record low of 1,811.000 Person in 1991. Sri Lanka Number of University Lecturers data remains active status in CEIC and is reported by Central Bank of Sri Lanka. The data is categorized under Global Database’s Sri Lanka – Table LK.G034: University Education Statistics.