These are a collection of XLSX sheets containing some of my favorite Excel tricks to reformat data to make analysis easier. I often use these to reformat column formatted data into plate layout or vice versa to better visualize and understand my data.

About this webinar We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors. This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool, Microsoft Excel, to sort, filter, copy, protect, transform, aggregate, summarise, and visualise research data. Webinar Topics Introduction to Microsoft Excel user interface Interpret data using sorting, filtering, and conditional formatting Summarise data using functions Analyse data using pivot tables Manipulate and visualise data Handy tips to speed up your work Licence Copyright © 2021 Intersect Australia Ltd. All rights reserved.

This provides a link to the Washington Secretary of State's Corporations Search tool. The Corporations Data Extract feature is no longer available. Customers needing a list of multiple businesses can use our advanced search to create a list of businesses under specific parameters. You can export this information to an Excel spreadsheet to sort and search more extensively. Below are the steps to perform this type of search. The more specified parameter searches provide narrower search results. Please visit our Corporations and Charities Filing System by following this link https://ccfs.sos.wa.gov/ Scroll down to the “Corporation Search” section and click the “Advanced Search” button on the right. Under the first section, specify how you would like the business name searched. Only use this for single business lookups unless all the businesses you are searching have a common name (use the “contains” selection). Select the appropriate business type from the dropdown if you are looking for a list of a specific business type. For a list of a particular business type with a specific status, select that status under “Business Status.” You can also search by expiration date in this section. Under the “Date of Incorporation/Formation/Registration,” you can search by start or end date. Under the “Registered Agent/Governor Search” section, you can search all businesses with the same registered agent on record or governor listed. Once you have made all your search selections, click the green “Search” button at the bottom right of the page. A list will populate; scroll to the bottom and select the green Excel document icon with CSV. An Excel document should automatically download. If you have popups blocked, please unblock our site, and try again. Once you have opened the downloaded Excel spreadsheet, you can adjust the width of each column and sort the data using the data tab. You can also search by pressing CTRL+F on a Windows keyboard.

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

Soybean aphid (Aphis glycines Matsumura; SA) is a major invasive pest of soybean [Glycine max (L.) Merr.] in northern production regions of North America. Although insecticides are currently the main method for controlling this pest, SA-resistant cultivars are being developed to sustainably manage SA in the future. The viability of SA-resistant cultivars may depend on identifying a diverse set of resistance genes from screening various germplasm sources, including wild soybean (Glycine soja Siebold and Zucc.), the progenitor of cultivated soybean. Data consisted of infestation ratings generated for a total of 337 distinct plant introduction lines of wild soybean that were exposed to avirulent SA biotype 1 for 14 d in 25 separate tests. Individual plants of the test lines were given a common rating by two researchers, based on a rating scale that progressed from 1=0 to 50, 2=51 to 100, 3=101 to 150, 4=151 to 200, 5=201 to 250, and 6 with >250 SA per test plant. Public dissemination of this dataset will allow for further analyses and evaluation of resistance among the test lines. Resources in this dataset:Resource Title: Infestation ratings for individual plants of various wild soybean lines. File Name: Web Page, url: https://ars.els-cdn.com/content/image/1-s2.0-S2352340917304432-mmc2.xlsx MS Excel spreadsheet showing infestation ratings for individual plants of 337 distinct plant introduction (PI) wild soybean lines following 14 d of exposure to SA.Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

This repository was created to store, organize, and share data collected for the Eastern Kentucky Project, focusing on hydrological research in the region. It serves as a centralized platform to manage data efficiently and facilitate collaboration among researchers and stakeholders involved in the project.

The repository primarily contains data from level loggers, which are crucial for monitoring and recording water levels, temperature, and other hydrological parameters over time. The collected data has been carefully extracted, processed, and stored in Excel files to ensure compatibility with various analysis tools. This structured format enables easy access and seamless integration into research workflows.

In addition to providing secure storage, the repository is designed to support efficient data sharing, transparency, and interdisciplinary collaboration. By offering a well-organized dataset, it enables researchers to analyze and build upon existing data, promoting high-quality research outputs. The repository ultimately aims to advance understanding and inform decision-making in water resource management for Eastern Kentucky.

Following a request from the European Commission, in 2015 EFSA created a database of host plant species of Xylella fastidiosa (EFSA, 2015). In 2018, a renovated and updated database of Xylella spp. (including both species X. fastidiosa and X. taiwanensis) and the related scientific report were published (EFSA, 2018). EFSA is going to maintain and update this database periodically.

In April 2020, EFSA released a new update of the Xylella spp. host plants database with information retrieved from literature search up to June 2019, Europhyt outbreak notifications up to 15 October 2019, and personal communications of experts (EFSA, 2020). The applied protocol for the extensive literature review, data collection and reporting, as well as results and lists of host plants are described in detail in the scientific report (EFSA, 2020).

The current database includes 343 host plants species in which the infection was assessed with at least two highly reliable detection methods (category A – see section 2.5.2 of EFSA (2020)), up to 595 host plants (regardless the detection methods applied – category E, see section 2.5.2 of EFSA (2020 ).

The Excel Files here attached represent the VERSION 3 of the Xylella spp. host plants database. For a detailed description of the information included in the database, please consult the related scientific report (EFSA, 2020).

The Excel file “Xylella spp. host plants database - VERSION 3” contains several sheets: the LEGENDA (with extensive description of each table), the full detailed raw data of the Xylella spp. host plant database (sheet “observation”) and several examples of data extraction.

Additional Excel files contain lists of host plant species of X. fastidiosa (subsp. unknown (i.e. not reported), fastidiosa, multiplex, pauca, morus, sandyi, tashke, fastidiosa/sandyi) and X. taiwanensis with different infection methods (natural, artificial and not specified, and according to different categories (A,B,C,D,E – see section 2.5.2 of EFSA (2020)).

Bibliography:

EFSA (European Food Safety Authority), 2015. Categorisation of plants for planting, excluding seeds, according to the risk of introduction of Xylella fastidiosa. EFSA Journal 2015;13(3):4061, 31 pp. doi:10.2903/j.efsa.2015.4061

EFSA (European Food Safety Authority), 2018. Scientific report on the update of the Xylella spp. host plant database. EFSA Journal 2018;16(9):5408, 87 pp. https://doi.org/10.2903/j.efsa.2018.5408

EFSA (European Food Safety Authority), 2020. Scientific report on the update of the Xylella spp. host plant database – systematic literature search up to 30 June 2019. EFSA Journal 2020;18(4):6114, 61 pp. https://doi.org/10.2903/j.efsa.2020.6114

File contains all data present in the manuscript. Every tab in the excel file corresponds to a different panel. Download the whole file to access all the raw data for all the panels.

We organize all the raw data from our research into a single Excel file titled ‘Source Data’. This file includes raw data of Figs. 1–4, Supplementary Fig. 1, Supplementary Fig. 2, Supplementary Fig. 5, Supplementary Figs. 7–13 and Supplementary Fig. 15–19.

The data was collected from experiments at the USDA-ARS in Beltsville, MD. The RTqPCR results were collected from processing samples on our BioRad machines. Count data was organized in Excel after being transferred from labnotebooks. Then data was analyzed in R.

The raw data was collected by methods outlined in the methods section (2. Methods). Data was recorded into an Excel spreadsheet, cleaned, then uploaded into JMP Pro 14 and Prism for statistical analysis and figure creation.

This is the master dataset used for the manuscript, titled “Does schistosome infection affect behavior through the gut-brain axis?”. Includes excel spreadsheets with sample data, descriptions, r-code, and phyloseq objects (.rds files).

Data used to generate figures in the paper of hyporheic exchange in a straight pool-riffle stream with floodplain are packed in an Excel file. The data are organized using the sort and names of the figures

The data used to generate figures in the paper of "hyporheic exchange in a meandering pool-riffle stream" are packed in an Excel file. The data are organized using the sort and names of the figures

General information

This item containst data sets for Schlegel et al, Nano Letters, 2023. DOI: https://doi.org/10.1021/acs.nanolett.2c04884

It contains confocal images, lattice light sheet images, flow cytometry data, compiled data as excle sheet and raw figure files.

Abstract Speed is key during infectious disease outbreaks. It is essential, for example, to identify critical host binding factors to pathogens as fast as possible. The complexity of host plasma membrane is often a limiting factor hindering fast and accurate determination of host binding factors as well as high-throughput screening for neutralizing antimicrobial drug targets. Here, we describe a multiparametric and high-throughput platform tackling this bottleneck and enabling fast screens for host binding factors as well as new antiviral drug targets. The sensitivity and robustness of our platform were validated by blocking SARS-CoV-2 particles with nanobodies and IgGs from human serum samples.

Data usage Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License.

Data Content Excel files for graphs Microscopy Images Flow cytometry data

Software to open files: .csv: Fiji (https://imagej.net/software/fiji/downloads) or Microsoft Excel .xlsx: Microsoft Excel .tif, .lsm: Fiji (https://imagej.net/software/fiji/downloads) .pzfx: GraphPad Prism .svg: Inkscape (https://inkscape.org/) .fcs: FCS Express .pdf: AdobeAcrobat or Mozilla Firefox .ijm: Fiji (https://imagej.net/software/fiji/downloads)

The dataset contains raw visual images, visualized tactile images along the X- and Z-axes and an Excel file that organize every sample and their correspondence in order. The tactile images are interpolated on the raw haptic signal to align with the visual images. Both the visual and tactile images have identical resolution of 620 X 410. The dataset consists of 743 records. Each record includes one visual image, two tactile images along the X and Z axes, and one defect segmentation image. Tactile image filenames ending with x and z denote X and Z components respectively.The samples in the dataset exhibit a wide range of colors and textures. Moreover, the dataset demonstrates the advantage of cross-modal data fusion. As a flexible material, leather may have defects on its surface and underside, which can be observed in the visual and tactile images, respectively. Combining visual and tactile images provides better information on the distribution of defects

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

Manipulative parasites often alter the phenotype of their hosts along multiple dimensions. ‘Multidimensionality’ in host manipulation could consist in the simultaneous alteration of several physiological pathways independently of one another, or proceed from the disruption of some key physiological parameter, followed by a cascade of effects. We compared multidimensionality in ‘host manipulation’ between two closely related amphipods, Gammarus fossarum and Gammarus pulex, naturally and experimentally infected with Pomphorhynchus laevis (Acanthocephala), respectively. To that end, we calculated in each host–parasite association the effect size of the difference between infected and uninfected individuals for six different traits (activity, phototaxis, geotaxis, attraction to conspecifics, refuge use and metabolic rate). The effects sizes were highly correlated between host–parasite associations, providing evidence for a relatively constant ‘infection syndrome’. Using the same methodology...

Supplementary Tables S1 to S6 in MS Excel format for "Identification of required host factors for SARS-CoV-2 infection in human cells" by Daniloski*, Jordan* et al.

Excel Spread Sheet containing the numeric data for Figs 1A, 2B, 2D, 3B, 4B, 4C, 4D, 4F, 5B, 5D, 5E, 6B, 6C, 7B, 7C, 7D,