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 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.

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.

A disorganized toy spreadsheet used for teaching good data organization. Learners are tasked with identifying as many errors as possible before creating a data dictionary and reconstructing the spreadsheet according to best practices.

Sample data for exercises in Further Adventures in Data Cleaning.

Dataset

This dataset was created by Ayo Apata

Contents

Sample credentialing data.

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 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.

Annex 2 INFORMAS Food Labelling Module Protocol

The Data Crunch handout series, developed at the Research Data Service Center at the University of Bonn, concisely describes various aspects of research data management (RDM) and is aimed at all researchers and interested parties who want to expand their knowledge of RDM.In the "DIY: FAIR Spreadsheet" handout a summary of best practices, the do's and dont's when working with spreadsheets and examples of helpful resources are provided. ---------------------- The Data Crunch "DIY: FAIR Spreadsheet" handout was created by Ewa E. Bres from the Research Data Service Center.

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in 2040) or to the period 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided that tabulates best models for each downscaled climate dataset and for all downscaled climate datasets considered together. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Climatology Index (MCI) and the Model Variability Index (MVI) of Srivastava and others (2020). The four indices consist of annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days compared against the same indices computed based on the PRISM and SFWMD gridded precipitation datasets for five climate regions: climate region 1 in Northwest Florida, 2 in North Florida, 3 in North Central Florida, 4 in South Central Florida, and climate region 5 in South Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model interpolation method of Daly and others (2008). The South Florida Water Management District’s (SFWMD) precipitation super-grid is a gridded precipitation dataset developed by modelers at the agency for use in hydrologic modeling (SFWMD, 2005). This dataset is considered by the SFWMD as the best available gridded rainfall dataset for south Florida and was used in addition to PRISM to identify best models in the South Central and South Florida climate regions. Best models were selected based on MCI and MVI evaluated within each individual downscaled dataset. In addition, best models were selected by comparison across datasets and referred to as "ALL DATASETS" hereafter. Due to the small sample size, all models in the using the Weather Research and Forecasting Model (JupiterWRF) dataset were considered as best models.

Follow these instructions to use the Google Spreadsheet in your own activity. Begin by copying the Google Spreadsheet into your own Google Drive account. Prefill the username column for your students/participants. This will help keep the students from overwriting their peers' work.Change the editing permissions for the spreadsheet and share it with your students/participants.Demonstrate what data goes into each column from the Wikipedia page. Be sure to demonstrate how to find the latitude and longitude from Wikipedia. For the images, make sure the students copy the url that ends in the appropriate file type (jpg, png, etc).Be prepared for lots of mistakes. This is a great learning opportunity to talk about data quality. When the students are done completing the spreadsheet, check the spreadsheet for obvious errors. Pay special attention to the sign of the longitude. All of those values should be negative. Download the spreadsheet as a CSV.Log into your AGO Org account.Click on the Content tab -> Add item -> From my computerUpload the CSV and save it as a layer feature. Be sure to include a few tags (Mesoamerica, pyramid, Aztec, Maya would be good ones).Once the layer has been uploaded and converted into a feature layer, click the Settings button and check Delete Protection and save. From the feature layer Overview tab, change the share settings to share with your students. I usually set up a group (something like Mesoamerica), add the students to the group, then share the feature layer with that group.From here explore the data. Symbolize the data by culture to see if there are spatial patterns to their distribution. Symbolize the data by height to see if some cultures built taller pyramids or if taller pyramids were confined to certain regions. Students can also set up the pop-ups to use the image URL in the data.From here, students can save their maps, add additional data from ArcGIS Online, create story maps, etc. If you are looking for more great data, from your ArcGIS Online map, choose Add -> Add Layer from Web and paste the following into the URL. https://services1.arcgis.com/TQSFiGYN0xveoERF/arcgis/rest/services/MesoAmerican_civs/FeatureServerImage thumbnail is from Wikipedia.

This Excel template is an example taken from the GEO web site (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html#GAtemplates) which has been modified to conform to the SysMO JERM (Just Enough Results Model). Using templates helps with searching and comparing data as well as making it easier to submit data to public repositories for publications.

DISCHARGE_SAMPLE_PUB_PT: Sample points recording stream discharge. Stream discharge is the volume of water passing a location per unit of time, and is generally expressed as cubic feet per second (cfs). The discharge table defined in this data standard stores the summary measurements. Raw data can be stored in a spreadsheet or document and related to the record.

GSA, the nation's largest public real estate organization, provides workspace for over one million federal workers. These employees, along with government property, are housed in space owned by the federal government and in leased properties including buildings, land, antenna sites, etc. across the country.

An Excel spreadsheet containing the full dataset, showing its sub-sampling

This dataset contains X-ray diffraction (XRD) data taken from wells and outcrops as part of the DOE GTO supported Utah FORGE project located near Roosevelt Hot Springs. It contains an Excel spreadsheet with the XRD data, a text file with sample site names, types, and locations in UTM, Zone 12, NAD83 coordinates, and a GIS shapefile of the sample locations with attributes.

Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2020. 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.

Descrizione del dataset / Dataset description

[ITALIANO]
Il dataset comprende:

• 2 tabelle di esempio, con un numero di righe minimo che consenta di fornire indicazioni sul riempimento di ogni colonna
  • Una per la metadatazione relativa agli oggetti esibilti (.csv)
  • Una per la metadatazione relativa al processo di digitalizzazione (.ods)
• 2 tabelle vuote, da usare per la raccolta di dati in nuovi progetti
  • Una per la metadatazione relativa agli oggetti esibilti (.ods)
  • Una per la metadatazione relativa al processo di digitalizzazione (.ods)

I template delle tabelle sono attualmente disponibili solo in Italiano. Segue una traduzione in inglese, con annessa descrizione di ciascuno dei campi.

[ENGLISH]
The dataset includes:

• 2 sample tables, with a minimum number of rows to give indications on how to fill each column
  • One for metadata related to the exhibited objects (.csv)
  • One for metadata related to the digitisation process (.ods)
• 2 empty tables, to be used for data collection in new projects
  • One for metadata related to the exhibited objects (.ods)
  • One for metadata related to the digitisation process (.ods)

The table templates are currently only available in Italian. Below, each table's field is translated from Italian to English and described.

Italian to English Fields Translation and Description

DATASET DELL’OGGETTO

[Object Dataset - English Translation and Description]

• NR (English translation: ID): Unique identifier for the item, numeric, alphanumeric, alphabetic or descriptive, not repeated within the dataset.

• NR collegato (English translation: Linked ID): Identifier of an item related to the main one in this row (“NR”).

• Relazione (English translation: Relation): Type of relationship linking the main item and the related item (e.g., “part of”, “represents”).

• Sala mostra (English translation: Exhibition Room): Name or code of the exhibition room where the item has been (or is) displayed.

• Didascalia (English translation: Caption): Descriptive text of the item, often containing information relevant to other fields.

• Consistenza (English translation: Quantity / Extent): Quantity or measure of the item (e.g., number of components).

• Tipologia documentaria (English translation: Document Type): Type of object, selected from a controlled vocabulary (e.g., Map, Book, Model, Specimen, etc.).

• Tecnica (English translation: Technique): Technique used to create the item, chosen from a controlled vocabulary (e.g., Engraving, Watercolour, Sculpture, etc.).

• Tipologia riprod. in mostra (English translation: Exhibition Reproduction Type): Type of representation of the item in the exhibition, if it differs from the original format.

• Soggetti (English translation: Subjects): Proper or common names of subjects present or represented, with recommended use of persistent identifiers like VIAF or ULAN.

• Titolo originale (English translation: Original Title): Original title of the work, often provided by the author, with language tag (e.g., @ita).

• Titolo museale (English translation: Exhibition Title): Title assigned by the curator or museum for the exhibition.

• Titolo @en (English translation: Title (English)): English translation of the title, without language tag.

• Data (English translation: Date): Dating of the item, single date or a range of years.

• Scopritore (English translation: Discoverer): Name of the person who discovered or collected the item, with possible ULAN/VIAF ID.

• Autore (English translation: Author): Name of the author of the work or item, with possible ULAN/VIAF ID.

• Traduttore (English translation: Translator): Name of the person who translated the work, with possible ULAN/VIAF ID.

• Disegnatore (English translation: Illustrator / Draftsman): Person responsible for drawing the item, with possible ULAN/VIAF ID.

• Incisore (English translation: Engraver): Person who engraved the object, with possible ULAN/VIAF ID.

• Editore (English translation: Publisher): Name of the publisher or publishing house, with possible ULAN/VIAF ID.

• Luogo editore (English translation: Publisher Location): Geographical location of the publisher.

• Preparatore museale (English translation: Museum Preparer): Person or institution responsible for the museum preparation of the item, with possible ULAN/VIAF ID.

• Committente (English translation: Commissioner): Name of the person or institution that commissioned the work, with possible ULAN/VIAF ID.

• Tipologia opera parente (English translation: Related Work Type): Type/category of a parent or related work, chosen from a controlled vocabulary.

• Titolo opera parente (English translation: Related Work Title): Title of the related or parent work.

• Volume (English translation: Volume): Volume number or code, if the item is part of a series.

• Collezione (English translation: Collection): Name of the collection to which the item belongs.

• Ente conservatore (English translation: Holding Institution): Name of the institution holding the item.

• Luogo conservazione (English translation: Place of Preservation): Physical place or city where the item is preserved.

• Collocazione / Inventario (English translation: Shelfmark / Inventory Code): Identifier used for cataloguing or inventory at the holding institution.

• Collocazione fisica (English translation: Physical Location): Internal location used for logistical or exhibition needs.

• Regno (English translation: Kingdom): Main biological kingdom classification of a fossil (e.g., Animalia).

• Phylum (English translation: Phylum): Internal taxonomic category identifying structural characteristics (e.g., Chordata).

• Classe (English translation: Class): Taxonomic class defining distinctive traits (e.g., Mammalia).

• Ordine (English translation: Order): Taxonomic order grouping similar families (e.g., Xenarthra).

• Famiglia (English translation: Family): Taxonomic family grouping related genera (e.g., Mylodontidae).

• Genere (English translation: Genus): Taxonomic genus grouping similar species (e.g., Scelidotherium).

• Specie (English translation: Species): Species of the organism (e.g., gladius).

• Taxon_data (English translation: Taxon Data URL): Link to authoritative taxonomy record (e.g., mindat.org).

• Periodo_geologico (English translation: Geological Period): Geological period when the fossil was formed (e.g., Pleistocene).

• Età_specifica (English translation: Specific Geological Age): More precise age within a geological period (e.g., Messinian).

• Formazione_geologica (English translation: Geological Formation): Stratigraphic unit where the fossil was found.

• Ambiente_deposizionale (English translation: Depositional Environment): Type of environment where the fossil was deposited (e.g., deep sea).

• Stato_geografico_raccolta (English translation: Country of Collection): Country or region where the fossil was collected.

• Luogo_raccolta (English translation: Collection Site): Specific locality where the fossil was found (e.g., Lecce).

DATASET DEL PROCESSO DI DIGITALIZZAZIONE

[Digitisation Process Dataset - English Translation and Description]

• NR (English translation: ID): Unique reference number for the item in the dataset.

• OGGETTO (English translation: Object): Description of the item (e.g., manuscript, medal).

• VETRINA (English translation: Display Case): Case or position where the object is exhibited.

• DIDASCALIA (English translation: Caption): Descriptive text accompanying the item in the