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).

Sample data for exercises in Further Adventures in Data Cleaning.

This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

This table contains information on the balance sheet of the general government sector. The information is limited to financial assets and liabilities. For each reporting period the opening and closing stocks, financial transactions and other changes are shown. Transactions are economic flows that are the result of agreements between units. Other changes are changes in the value of assets or liabilities that do not result from transactions such as revaluations or reclassifications. The figures are consolidated which means that flows between units that belong to the same sector are eliminated. As a result, assets and liabilities of subsectors do not add up to total assets or liabilities of general government. For example, loans of the State provided to social security funds are part of loans of the State. However, these are not included in the consolidated assets of general government, because it is an asset of a government unit with a government unit as debtor. Financial assets and liabilities in this table are presented at market value. The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date.

Data available from: Yearly figures from 1995, quarterly figures from 1999.

Status of the figures: The figures for the period 1995-2022 are final. The figures for 2023 and 2024 are provisional.

Changes as of 24 December 2024: Figures on the third quarter of 2024 are available. The figures for the second quarter of 2024 have been adjusted.

When will new figures be published? Provisional quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter may be revised, in December the figures on the second quarter may be revised and in March the first three quarters may be revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the most actual figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to the three revised years at the end of June. More information on the revision policy of Dutch national accounts and government finance statistics can be found under 'relevant articles' under paragraph 3.

This dataset contains the valuation template the researcher can use to retrieve real-time Excel stock price and stock price in Google Sheets. The dataset is provided by Finsheet, the leading financial data provider for spreadsheet users. To get more financial data, visit the website and explore their function. For instance, if a researcher would like to get the last 30 years of income statement for Meta Platform Inc, the syntax would be =FS_EquityFullFinancials("FB", "ic", "FY", 30) In addition, this syntax will return the latest stock price for Caterpillar Inc right in your spreadsheet. =FS_Latest("CAT") If you need assistance with any of the function, feel free to reach out to their customer support team. To get starter, install their Excel and Google Sheets add-on.

Hydroxyapatite for bone remodelling

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Standard error reference tables for the Retail Sales Index in Great Britain.

BGS has collected approximately 33,000 offshore samples, using grab samplers, dredges and shallow coring devices (to a maximum depth of 6m below the sea bed). The data set consists of: i) The sample data sheets: these contain index information and geological descriptions of the samples. Before 1983, all data were entered on a single sheet. From 1983 onwards, the data were entered on three sheets, and therefore usually contain more detailed information than the old style data sheets. ii) Coded geological descriptions in digital format: from 1983 onwards, coded geological information regarding the descriptions were entered on data sheets. These were subsequently digitised, and information is available for about 10,000 samples in this form. iii) Sample material: the samples, cores and any remains of sub-samples are retained for further inspection and analysis. iv) Results of analyses: these are mainly short reports or single page documents resulting from analytical tests, for example, micropalaeontological examination or age dating. Digital data sets of test results (PSA, geochemistry and geotechnical) are described separately.

We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.

A large data-set of index and dynamic parameters measured from resonant column (RC) and cyclic torsional shear(CTS) tests on 170 undisturbed isotropically consolidated fine-grained specimens deriving from 90 sites in Central and Northern Italy is made available. Tests were all performed over the past 20 years at the Geotechnical Laboratory of the Civil and Environmental Engineering Department of the Florence University using the same apparatus and following the same standardized procedures.

The experimental data are organized in an excel file (named as “Italian_Clays_Archive.xlsx”). For each tested sample, the main physical, index and dynamic properties measured are archived with the code number of the sample (No) in the sheet named as “Dataset” as well as any information available about the borehole from which the sample has been taken. The list and the meaning of the symbols used can be found in the sheet named as “Legend”. Other sheets containing borehole stratigraphy are named as “XX-ST” (where “XX” stands as the bore-hole code, BH) and they can be recalled directly from the “Dataset” sheet. Note that stratigraphy is given in its original format, when available. However, depth and thickness of each layer can be easily deduced by the figure provided and the soil lithology is well represented by the symbol used that are those generally adopted internationally. Finally, the sheets named as "YY-CTS-STEPZ" (where “YY” and “Z” stand as the sample code, No, and the step number, respectively) contain the shear stress and strain values measured after CTS tests at different steps (i.e. amplitudes of the cyclic dynamic torsional loading applied) during the 1st, 5th, 15th, 20th and 25^th.and/or and/or the corresponding shear modulus and damping ratio calculated from the same cycles.

The selected samples were taken mostly in Holocene and Pleistocene fluvio-lacustrine soil deposits at depths ranging from 1 m to 75 m below ground level and they mainly consist of normally and over-consolidated clayey silts or clays (1 < OCR < 9.4) of medium-to-high plasticity (4 < PI < 84), with very low-to high consistency (-1< Ic < 1.9) and initial void ratio, e₀, ranging between 0.175 and 2.456. The database also includes some samples of organic clays of low consistency, very high water content and void ratio and low unit weight. The initial (small strain) values of shear modulus, G₀, and damping ratio, D₀, range between 21 MPa and 292 MPa and between 0.8% and 5.1%, respectively. The smallest and the largest shear strain values induced by RC and CTS tests are 1.9x10^-5 % and 6.3x10^-1%, respectively.

Download Employee Vehicle Personal Use Excel SheetThis dataset lists the employee name and taxable benefit for personal use of City of Greater Sudbury Vehicle as travel expenses for the year 2020. Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Data for other years is available in separate datasets. Updated quarterly when expenses are prepared.

Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2021. Details are provided on the employee (name, title, department), the travel (dates, location, purpose) and the cost (expenses, recoveries). Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Updated quarterly when expenses are prepared. Expenses for other years are available in separate datasets.

A random sample of households were invited to participate in this survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

4D light sheet imaging, computational reconstruction, and cell tracking in mouse embryos -- example data (raw .czi and fused .klb light sheet images of mouse E7.5)

Images obtained on Lightsheet Z.1 (Zeiss) with incubation and dual pco.edge 4.2 cameras (PCO), with 20X/1.0 plan apochromat water-dipping detection objective (RI=1.34–1.35) and dual 10X/0.2 illumination objectives. The specimen tank was filled with culture medium and maintained at 37°C and 5% CO2. Mouse post-gastrulation embryos were subjected to 3 hours of imaging at 18-minute intervals, imaged from the ventral aspect in two views offset by 100, using our Zeiss Lightsheet Adaptive Position System (ZLAPS), linked below. The .czi files were compressed with bzip2 and presented here. Also included is the fused .klb dataset,which is the result of deconvol...

This data set is derived from displacement volume measurements and dry weight conversion calculations of plankton samples collected by a MOCNESS-1 on the RV/N.B.Palmer cruises NBP0103, NBP0104, NBP0202, NBP0204 from the Southern Ocean in austral fall/winter of 2001 and 2002. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=This data set is derived from displacement volume measurements and dry weight conversion calculations. The MOCNESS-1 plankton sampler has nine rectangular nets (1m x 1.4 m) with a mesh size of 0.333 mm, which are opened and closed sequentially by commands through conducting cable from the surface (Wiebe et al., 1976).

Methods:
Displacement volume measurement: The entire sample plus liquid was measured in a large graduated cylinder then poured through a sieve into a second cylinder. The difference in volume is the displacement volume.
Detailed instructions:

Measuring Displacement Volume
Supplies: rubber gloves, safety goggles, 2 1-liter graduated cylinders, 2 smaller graduated cylinders (25 to 100 ml), 2 funnels: 1 wide-necked open funnel and 1 small-necked one with mesh attached to the inside or a sieve that fits inside the small-necked funnel, squeeze bottles (water and formalin or other preservative), sieve of mesh size equal to or smaller than that on sampling net.
Put on rubber gloves
Remove jars for 1 net from sample box (may be from one to many jars for a single net sample)
Fill in the data sheet with MOC tow#, date, and net#. jar#
Take first sample to hood. Put on safety goggles.
Remove lid and internal label with long forceps. Get most of zooplankton off by dipping into jar and place label inside lid after checking that internal label agrees with lid label.
Remove large (>5cc) animals (medusae, some fish or shrimp) and measure their displacement volume in the small graduated cylinders:
-Put animal and enough liquid to cover in one small graduated cylinder.-Note this volume on
data sheet.
-Place small sieve in small funnel and set them on top of second empty small grad graduated cylinder.
-Pour animal plus liquid into sieve and let drain.
-Note this volume on data sheet as well as the type of animal.
-Return the specimen to the main sample.
Pour the large sample into the 1-liter graduated cylinder using the open funnel on top (no mesh in funnel). Rinse sparingly the jar, funnel and sides of the graduated cylinder. Diluting the sample with water could cause it to rot. Add a little water with the squeeze bottle to bring the level up to an even line on the graduated cylinder.
Note this volume on the data sheet (sample + liquid)
Place the large funnel containing the sieve or mesh on top of the second, empty graduated cylinder.
Pour the sample into the empty grad. Don't worry about animals stuck to the sides of the first grad. Do not add any liquid to wash sample into the second grad.
Swirl the funnel to remove excess liquid until most of liquid is done dripping (about 1 minute, but varies sample to sample). Carefully drawing the samples toward the center with large forceps is sometimes helpful.
Note this volume on data sheet (liquid vol.)
Rinse the graduated cylinder and the mesh-funnel into the sieve with the hose and return most of the dry sample to the jar using the open funnel.
Use water from faucet with hose to wash the sample on sieve to one side and then use squirt bottle of water (sparingly) or the preservative filled one to rinse the sample from sieve to jar.
Add enough of the filtered formalin to fill the jar, dispose of remainder in appropriate waste container.
- Check the sample's pH and add buffer (sodium borate or borax) if = 8.0.
- Replace cap, swirl if buffer of formalin was added, and rinse outside of jar.
- Rinse everything well after each net sample.

Dry weight calculations:
dry weight = (dvol/(100.139))(1/1.003); [mg/m3]
integrated dry weight = depth interval * dry weight; [mg/m2]
total dry weight for the entire sampled water column = sum of integrated dry weights for all nets for one tow; [mg/m2] awards_0_award_nid=54617 awards_0_award_number=unknown SOGLOBEC NSF ANT awards_0_funder_name=NSF Antarctic Sciences awards_0_funding_acronym=NSF ANT awards_0_funding_source_nid=369 cdm_data_type=Other comment=SO-GLOBEC MOCNESS biovolume data
displacement volumes and dry weight calculations for MOCNESS-1 samples
NJCopley Oct-25-2005 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.2372.1 Easternmost_Easting=-65.529 geospatial_lat_max=-65.147 geospatial_lat_min=-69.243 geospatial_lat_units=degrees_north geospatial_lon_max=-65.529 geospatial_lon_min=-75.732 geospatial_lon_units=degrees_east geospatial_vertical_max=800.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/2372 institution=BCO-DMO instruments_0_acronym=MOC1 instruments_0_dataset_instrument_description=MOCNESS 1 meter square nets (150 and 335 micrometer mesh) The MOCNESS-1 plankton sampler has nine rectangular nets (1m x 1.4 m) with a mesh size of 0.333 mm, which are opened and closed sequentially by commands through conducting cable from the surface instruments_0_dataset_instrument_nid=4180 instruments_0_description=The Multiple Opening/Closing Net and Environmental Sensing System or MOCNESS is a family of net systems based on the Tucker Trawl principle. The MOCNESS-1 carries nine 1-m2 nets usually of 335 micrometer mesh and is intended for use with the macrozooplankton. All nets are black to reduce contrast with the background. A motor/toggle release assembly is mounted on the top portion of the frame and stainless steel cables with swaged fittings are used to attach the net bar to the toggle release. A stepping motor in a pressure compensated case filled with oil turns the escapement crankshaft of the toggle release which sequentially releases the nets to an open then closed position on command from the surface. -- from the MOCNESS Operations Manual (1999 + 2003). instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L22/current/NETT0097/ instruments_0_instrument_name=MOCNESS1 instruments_0_instrument_nid=437 instruments_0_supplied_name=MOCNESS1 metadata_source=https://www.bco-dmo.org/api/dataset/2372 Northernmost_Northing=-65.147 param_mapping={'2372': {'lat': 'master - latitude', 'lon': 'master - longitude', 'depth_close': 'flag - depth'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/2372/parameters people_0_affiliation=Woods Hole Oceanographic Institution people_0_affiliation_acronym=WHOI people_0_person_name=Peter H. Wiebe people_0_person_nid=50454 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI people_1_person_name=Nancy Copley people_1_person_nid=50396 people_1_role=Technician people_1_role_type=related people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Nancy Copley people_2_person_nid=50396 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=SOGLOBEC projects_0_acronym=SOGLOBEC projects_0_description=The fundamental objectives of United States Global Ocean Ecosystems Dynamics (U.S. GLOBEC) Program are dependent upon the cooperation of scientists from several disciplines. Physicists, biologists, and chemists must make use of data collected during U.S. GLOBEC field programs to further our understanding of the interplay of physics, biology, and chemistry. Our objectives require quantitative analysis of interdisciplinary data sets and, therefore, data must be exchanged between researchers. To extract the full scientific value, data must be made available to the scientific community on a timely basis. projects_0_geolocation=Southern Ocean projects_0_name=U.S. GLOBEC Southern Ocean projects_0_project_nid=2039 projects_0_project_website=http://www.ccpo.odu.edu/Research/globec_menu.html projects_0_start_date=2001-01 sourceUrl=(local files) Southernmost_Northing=-69.243 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-75.732 xml_source=osprey2erddap.update_xml() v1.3

Abstract

This dataset was generated from a set of Excel spreadsheets from an Information and Communication Technology Services (ICTS) administrative database on student applications to the University of Cape Town (UCT). This database contains information on applications to UCT between the January 2006 and December 2014. In the original form received by DataFirst the data were ill suited to research purposes. This dataset represents an attempt at cleaning and organizing these data into a more tractable format. To ensure data confidentiality direct identifiers have been removed from the data and the data is only made available to accredited researchers through DataFirst's Secure Data Service.

The dataset was separated into the following data files:

1. Application level information: the "finest" unit of analysis. Individuals may have multiple applications. Uniquely identified by an application ID variable. There are a total of 1,714,669 applications on record.
2. Individual level information: individuals may have multiple applications. Each individual is uniquely identified by an individual ID variable. Each individual is associated with information on "key subjects" from a separate data file also contained in the database. These key subjects are all separate variables in the individual level data file. There are a total of 285,005 individuals on record.
3. Secondary Education Information: individuals can also be associated with row entries for each subject. This data file does not have a unique identifier. Instead, each row entry represents a specific secondary school subject for a specific individual. These subjects are quite specific and the data allows the user to distinguish between, for example, higher grade accounting and standard grade accounting. It also allows the user to identify the educational authority issuing the qualification e.g. Cambridge Internal Examinations (CIE) versus National Senior Certificate (NSC).
4. Tertiary Education Information: the smallest of the four data files. There are multiple entries for each individual in this dataset. Each row entry contains information on the year, institution and transcript information and can be associated with individuals.

Analysis unit

Applications, individuals

Kind of data

Administrative records [adm]

Mode of data collection

Other [oth]

Cleaning operations

The data files were made available to DataFirst as a group of Excel spreadsheet documents from an SQL database managed by the University of Cape Town's Information and Communication Technology Services . The process of combining these original data files to create a research-ready dataset is summarised in a document entitled "Notes on preparing the UCT Student Application Data 2006-2014" accompanying the data.