In the attached Excel file, "Example Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described. Additionally, there are three sheets with sample graphs created using one of the three datasets. · Sheets 1 and 2: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Sheets 3 and 4: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Sheets 5 and 6: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.

In "Sample Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described (CrP Sample Dataset, Glycolytic Dataset, Oxidative Dataset). Additionally, there are three sheets with sample graphs created using one of the three datasets (CrP Sample Graph, Glycolytic Graph, Oxidative Graph). Each dataset and graph pairs are from different subjects. · CrP Sample Dataset and CrP Sample Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Glycolytic Dataset and Glycolytic Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Oxidative Dataset and Oxidative Graph: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a sustained, light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

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.

The overall aims of this NQPHN funded research were to develop a field education placement curriculum for social work student placements in GP clinics in North Queensland, to trial six social work student GP placements, evaluate these and develop a sustainability plan. The project was supported through regular meetings with a reference group.

Ethics approval for this study for data collection involving surveys, non-identifying student clinic records and reference group meetings was sought from the James Cook University Human Ethics committee and granted, approval number H8532.

A field education curriculum for social work placements in GP practices was developed in 2021/22 informed by a systematic literature review exploring social work practice in primary health settings (Zuchowski & McLennan, 2022), reference group insights and project worker discussions. After the student placements were completed, feedback was used to update and finalise the curriculum.

Data collection involved a survey sent to key stakeholders and an analysis of the de-identified record of the students work in the clinic key. They aim of the data collection was to review and evaluate the social work placements in the GP clinics. Survey questions sought feedback on the curriculum modules, the type of services provided by students, the types of patient engagement, the benefits and challenges of student placements in primary health care, noted patient outcomes, the contribution of the placement to social work student learning and field education, service provider relationships, the value of the placements, key stakeholder contributions to the placements, required supports and overall satisfaction levels. The Qualtrics survey link was sent to 29 key stakeholders in the student placement and project, including s students, task supervisors, field educators, GP’s, other health professionals, mentors and reference group members. Sixteen responses were received, which resulted in a 55% response rate. A thematic analysis was applied to the qualitative data in the survey and reporting forms. The answers to the survey and comments on the reporting forms were entered into the software NVivo for coding. The data were analysed via a process of inductive, axial and selective coding (Alston and Bowles, 2018). The draft themes were presented to the reference group members to seek further insights and feedback, before finalisation. Quantitative data was derived from the survey and record forms were entered in an excel spreadsheet to produce descriptive graphs and tables (Alston and Bowles, 2018).

Software/equipment used to create/collect the data: Qualtric

This dataset consists of the quantitative data (All data 4 8 22.csv) and a report (First Survey report 14 6 2022) from the first survey, saved in MS Word and PDF formats. The data files are available via conditional access only. The survey questions are available for download below.

SMaRteN, in partnership with Vitae, conducated research into the impact of COVID-19 on the working lives of doctoral researchers and research staff.SMaRteN www.smarten.org.uk

The UK Research and Innovation (UKRI) funded Student Mental Health Research Network (SMaRteN) is working to support and encourage better research into student mental health. SMaRteN is based at Institute of Psychiatry, Psychology and Neurosciences at King’s College London. Vitae is a non-profit programme supporting the professional and career development of researchers. www.vitae.ac.uk @vitae_news Covid-19 and the associated lock down has caused substantive disruption to the study and work of doctoral students and researchers in universities. The response to the pandemic has varied across universities and research funders.SMaRteN and Vitae aim to develop a national picture for how doctoral researchers and research staff have been affected by the pandemic.​The survey includes questions relating to the impact of COVID-19 on research work, mental wellbeing, social connection. We further address the impact of COVID-19 on changes to employment outside of academia, living arrangements and caring arrangements and the consequent effect of these changes on research work. The survey considers the support provided by supervisors / line managers and by universities. Please note, Time 2 (follow up) data was collected, corresponding to this survey, in September / October 2020. This can be accessed via the link at the bottom of the page.Data available here as either an SPSS or Excel download:SPSS file contains labels Excel file contains labels and brief notes about codingRecoding data for CV19 impact - SPSS Syntax file describes steps taken to code data CV19_impact_on_researchers - word document, export from Qualtrics of the survey. Qualitative data - Benefits and Challenges of the lockdown. Respondents gave qualitative responses to a number of questions. We are slowly cleaning these up to get them fully anonymised. At the moment, we have only done this for the sample (approximately 1,000) of responses that have been analysed to look at the benefits and challenges of the lockdown.Please note, data has been removed from this data set to ensure participant anonymity.For further information, please contact Dr Nicola Byrom - nicola.byrom@kcl.ac.uk

The Residential School Locations Dataset [IRS_Locations.csv] contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites.

https://www.educationcounts.govt.nz/data-services/directories/maori-medium-schoolsLatest Directory Update: April 2020Directory InformationWe have recently been in contact with schools and many have agreed to have their email addresses publicly released. These email addresses have now been added to the Māori Medium Schools Directory downloads. Persons or organisations wishing to send email material to individuals or organisations whose email addresses appear in this directory must comply with the requirements of the Unsolicited Electronic Messages Act 2007. Publication of email addresses on this site should not be taken as deemed consent to receiving unsolicited email.Principal names are available for State and State Integrated Schools only. The most up to date information that we have available has been provided. Recent changes may not be reflected.Māori Language in EducationMāori medium education is where students are taught all or some curriculum subjects in the Māori language for at least 51 percent of the time (Māori Language Immersion Levels 1-2).Māori language in English medium where students are learning Te Reo Māori as a language subject, or taught curriculum subjects in the Māori language for up to 50 percent of the time (Māori Language Immersion levels 3-5). No Māori Language in Education is where the student learns at most Simple words, greetings or songs in Māori (Level 6- Taha Māori) or no Māori language learning of any kind.Schools included in this directory are all schools recorded as having at least 1 student enrolled in Māori medium education (Māori Language Immersion Levels 1-2). This includes 4 types of schools:Māori medium school is a school where all Students are recorded as enrolled in Māori medium educationSchool with Māori medium education is a school where some students do Māori medium education and the rest do no Māori language in education.Mixed Māori Language in Education School is a school where all students are either involved in Māori medium education or Māori language in English medium education.School with Mixed Māori Language in Education School is a school where some students do Maori medium education, some do Māori language in English medium education and some do no Māori language in education.Using a Directory of Educational InstitutionsTo learn more about these types of functions use the package help functionThese directories are easiest to use when you download the excel workbook of the directory you wish to use to your desktop so that you can use the full range of excel features including find, sort and filter.You can use the find function to search for a specific institute if you have its name/number. Sorting and filtering make it easier to find and analyse the education institute data. Excel allows you to sort your worksheet by using information from one or more columns, for example, by school type and region. Sorting has the effect of placing records, in this case education institutions, with the same sort criteria next to each other. Filtering allows you to block out records you don't want to see, leaving a view of the data you are interested in.Instructions to filter documentClick on the row number with the column headingsGo to [Data] [Filter] AutoFilterFrom here, you can select the data you require by using the drop down arrowsFor exampleFor the schooling directory, if you wish to identify all full primary schools in Upper HuttGo to column 'R' Territorial Local Authority click down arrow, select Upper Hutt City, then go to column 'N' Type select Full PrimaryWhen you have finished your query simply click on the columns you filtered and select all to display all the institutions again

1. Description of the data
   1.1 Type of study This is a part of Dissertation work of Master Student at School of Engineering, RK University, Rajkot India 1.2 Types of data This is an experimental data collected from a three-stepped solar still (0.6m × 0.6m) constructed by the student and experiment conducted at Bhavnagar (21.74°N,72.10°E) to yield 2.5 litres/day 1.3 Format and scale of the data The file format is excel, the number of records is presented for the month of April 22 to May 10, 2018. CAD model was used to create the drawing for Solar Still. The experiment was conducted for the same TDS of water daily. These are the initial indicative values. Formats and software enable sharing and long-term validity of data as a part of academic work.

This zip file contains data files for 3 activities described in the accompanying PPT slides 1. an excel spreadsheet for analysing gain scores in a 2 group, 2 times data array. this activity requires access to –https://campbellcollaboration.org/research-resources/effect-size-calculator.html to calculate effect size.2. an AMOS path model and SPSS data set for an autoregressive, bivariate path model with cross-lagging. This activity is related to the following article: Brown, G. T. L., & Marshall, J. C. (2012). The impact of training students how to write introductions for academic essays: An exploratory, longitudinal study. Assessment & Evaluation in Higher Education, 37(6), 653-670. doi:10.1080/02602938.2011.5632773. an AMOS latent curve model and SPSS data set for a 3-time latent factor model with an interaction mixed model that uses GPA as a predictor of the LCM start and slope or change factors. This activity makes use of data reported previously and a published data analysis case: Peterson, E. R., Brown, G. T. L., & Jun, M. C. (2015). Achievement emotions in higher education: A diary study exploring emotions across an assessment event. Contemporary Educational Psychology, 42, 82-96. doi:10.1016/j.cedpsych.2015.05.002andBrown, G. T. L., & Peterson, E. R. (2018). Evaluating repeated diary study responses: Latent curve modeling. In SAGE Research Methods Cases Part 2. Retrieved from http://methods.sagepub.com/case/evaluating-repeated-diary-study-responses-latent-curve-modeling doi:10.4135/9781526431592

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

SMaRteN, in partnership with Vitae, conducated research into the impact of COVID-19 on the working lives of doctoral researchers and research staff. This is the Time 2 data set. Data was collected at the end of September and start of October 2020. Please see link at bottom of page for the first data set.SMaRteN www.smarten.org.ukThe UK Research and Innovation (UKRI) funded Student Mental Health Research Network (SMaRteN) is working to support and encourage better research into student mental health. SMaRteN is based at Institute of Psychiatry, Psychology and Neurosciences at King’s College London.Vitae is a non-profit programme supporting the professional and career development of researchers. www.vitae.ac.uk @vitae_newsCovid-19 and the associated lock down has caused substantive disruption to the study and work of doctoral students and researchers in universities. The response to the pandemic has varied across universities and research funders.SMaRteN and Vitae aim to develop a national picture for how doctoral researchers and research staff have been affected by the pandemic.​The survey includes questions relating to the impact of COVID-19 on research work, mental wellbeing, social connection. We further address the impact of COVID-19 on changes to employment outside of academia, living arrangements and caring arrangements and the consequent effect of these changes on research work. The survey considers the support provided by supervisors / line managers and by universities.Data available here as either an SPSS or Excel download:SPSS file contains labelsExcel file contains labels and brief notes about codingRecoding data for CV19 impact - SPSS Syntax file describes steps taken to code dataCV19_impact_on_researchers - word document, export from Qualtrics of the survey.Please note, data has been removed from this data set to ensure participant anonymity.For further information, please contact Dr Nicola Byrom - nicola.byrom@kcl.ac.uk

Research in assessing the Mental Health Problems (MHPs), e.g., stress, anxiety, and depression of university students has had much interest worldwide for the last decade. This article provides a large and comprehensive dataset concerning the MHPs of 2028 students from 15 top-ranked universities in Bangladesh, including 9 government/public universities and 6 private universities. To collect the data, the GAD-7 (for Anxiety), PSS-10 (for Stress), and PHQ-9 (for Depression) models are adopted to reflect equivalent academic perspectives. Additionally, student sociodemographic data are collected. The adoption of these three models are done by a team of five professors and a student psychologist to best capture the academic and socio-demographic factors that influence MHPs among university students. To conduct the survey, a google form is developed and circulated among the 15 faculty representatives from the participating universities who further circulated and conducted the survey with the students. Collected data is evaluated to ensure the sufficiency of sample size, and internal consistency and reliability of the response. Furthermore, the levels of anxiety, stress, and depression are calculated using the data to demonstrate its' applicability. This dataset can be used to measure the trajectory of students’ the mental and psychosocial stressors, to adopt required mental health and counselling services, and to conduct data intensive Machine Learning (ML) model development to predictive MPH assessment.