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).

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.

Sample data for exercises in Further Adventures in Data Cleaning.

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.

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.

A messy data for demonstrating "how to clean data using spreadsheet". This dataset was intentionally formatted to be messy, for the purpose of demonstration. It was collated from here - https://openafrica.net/dataset/historic-and-projected-rainfall-and-runoff-for-4-lake-victoria-sub-regions

A well structured and professional Material Safety Data Sheet Template or MSDS for short, which can be used to store details about specific hazardous checmicals and materials. These sheets are critical for safety across all industries, including construction, cleaning, facilities management and more.

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.

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%.

Separate sheet highlights genes of interest encoding surface markers and transcription factors. Analysis includes means, standard deviation, CoV, and Mac:DC expression ratios. CoV, coefficient of variance; DC, dendritic cell; Mac, macrophage; MPS, mononuclear phagocyte system. (XLSX)

This is a spreadsheet of 1 of 10 companies in the shoe industry. Highlighting COGS, Total Revenue, Market share and Industry share.

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.

Hydroxyapatite for bone remodelling

Developing data-driven solutions that address real-world problems requires understanding of these problems’ causes and how their interaction affects the outcome–often with only observational data. Causal Bayesian Networks (BN) have been proposed as a powerful method for discovering and representing the causal relationships from observational data as a Directed Acyclic Graph (DAG). BNs could be especially useful for research in global health in Lower and Middle Income Countries, where there is an increasing abundance of observational data that could be harnessed for policy making, program evaluation, and intervention design. However, BNs have not been widely adopted by global health professionals, and in real-world applications, confidence in the results of BNs generally remains inadequate. This is partially due to the inability to validate against some ground truth, as the true DAG is not available. This is especially problematic if a learned DAG conflicts with pre-existing domain doctrine. Here we conceptualize and demonstrate an idea of a “Causal Datasheet” that could approximate and document BN performance expectations for a given dataset, aiming to provide confidence and sample size requirements to practitioners. To generate results for such a Causal Datasheet, a tool was developed which can generate synthetic Bayesian networks and their associated synthetic datasets to mimic real-world datasets. The results given by well-known structure learning algorithms and a novel implementation of the OrderMCMC method using the Quotient Normalized Maximum Likelihood score were recorded. These results were used to populate the Causal Datasheet, and recommendations could be made dependent on whether expected performance met user-defined thresholds. We present our experience in the creation of Causal Datasheets to aid analysis decisions at different stages of the research process. First, one was deployed to help determine the appropriate sample size of a planned study of sexual and reproductive health in Madhya Pradesh, India. Second, a datasheet was created to estimate the performance of an existing maternal health survey we conducted in Uttar Pradesh, India. Third, we validated generated performance estimates and investigated current limitations on the well-known ALARM dataset. Our experience demonstrates the utility of the Causal Datasheet, which can help global health practitioners gain more confidence when applying BNs.

Each sample that is received by NSIL is assigned a laboratory number and a case file is initiated by the sample custodian. The case file will contain all relevant paperwork for that sample including the sample submission sheet, laboratory raw data worksheets, the final results report and any other relevant documentation. The sample custodian enters the client information into the NSIL Sample tracking system (Sample receipt database) and generates appropriate client and sample receipt information. The laboratory analysts perform the appropriate analyses and record the results and whether the results are compliant or non-compliant with the assigned acceptance levels. The analysts also record the record of charges and the analytical and quality assurance units that were used to complete all analysis. The database is used to track samples analyzed by NSIL from sample receipt to reporting of results. It tracks numbers of samples, number of analytical units, types of samples, purpose for sampling ans analytical costs.

Raw data used in analysis of determinants of dividend policy - a case of banking sector in Serbia.

The information in this dataset is from "Feasibility of Infiltration Galleries for Managed Aquifer Recharge in the Northeast Arkansas Delta" by Godwin et al., 2020. Included in the dataset are the following raw data: Table of well log point -Coordinates and other characteristic data for each groundwater well log point used in confining unit mapping survey. These points can be used for various spatial analyses of the Mississippi River Valley Alluvial Aquifer (MRVAA) and its upper confining unit. Check the Arkansas Water Well Construction Commission database for updated spatial information, updated and improved logs, and newly added well logs. Raw geophysical data files -Electrical resistivity survey files from the selected reservoir sites collected in partnership with the United States Geological Survey. These are the raw files from Inverse-Schlumberger method survey lines at five reservoir sites, which measure differences in soil electrical properties. These differences correspond to changes in soil texture. Soil sample textural analysis data -Data includes sand/silt/clay analysis result sheet and sand fractionization result sheets for samples from the reservoir sites selected after geophysical surveys were conducted. Data should be used only with considerations of the sampling and analysis methods described in the publication. Soil sample chemical analysis data -Includes major and minor metals/nutrients, pH, and other chemical properties for samples from selected sites. Data should be used only with considerations of the sampling and analysis methods described in the publication. Resources in this dataset:Resource Title: Table of Well Points . File Name: WellLogPointsTable.xlsxResource Description: This table includes the spatial coordinates, web links, and confining unit thickness data for all of the irrigation wells used in the mapping survey. Well data are from the Arkansas Water Well Construction Commission Database.Resource Title: Sand-Silt-Clay Soil Sample Data. File Name: All Samples Sand-Silt-Clay.xlsxResource Description: Soil boring sample analysis results from University of Missouri Soil Lab for sand-silt-clay fractionalization. Resource Title: Select Soil Sample Sand Fractionalization . File Name: Select Samples Sand Fractionation.xlsxResource Description: The results of sand grain-size fractionalization analysis conducted on select samples at the University of Missouri Soil LabResource Title: Soil Sample Sieve Analysis. File Name: Soil Sample Sieve Analysis DWMRU Lab.xlsxResource Description: Results of in-house sieve analysis (USDA Delta Water Management Research Unit) on selected soil boring samples. Resource Title: Soil Sample Chemical Analyses. File Name: MissouriSoilTestingLaboratory_Results Sheet.xlsxResource Description: Results of various soil chemical analyses conducted at the University of Missouri Soil LabResource Title: Raw Geophysical Data Files. File Name: Electrical Resistivity Profile Raw Files.zipResource Description: Raw geophysical data files -Electrical resistivity survey files from the selected reservoir sites collected in partnership with the United States Geological Survey. These are the raw files from Inverse-Schlumberger method survey lines at five reservoir sites, which measure differences in soil electrical properties. These differences correspond to changes in soil texture.

This is a compiled datasets comprising of data from various companies' 10-K annual reports and balance sheets. The data is a longitudinal or panel data, from year 2009-2022(/23) and also consists of a few bankrupt companies to help for investigating factors. The names of the companies are given according to their Stocks. Companies divided into specific categories.

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