Spreadsheet files list statistically significant LCMS peaks between liver access Salmonella and cells sourced from alternate locations.

The U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers (USACE) are collaborating with the U.S. Federal Emergency Management Agency (FEMA) on improving flood-frequency analysis methods to account for mixed populations arising from different flood causal mechanisms. Precipitation data at different timescales are widely used in flood-typing studies. Various gridded precipitation datasets were validated by comparison against station observations to support flood-typing over six pilot regions in the contiguous U.S. (CONUS) where flood-typing approaches will be initially tested. The six pilot regions are (1) the Delaware River, (2) the Iowa River, (3) Puget Sound, (4) the Red River of the North, (5) the Trinity River, and (6) the Upper Colorado River. The datasets were validated by comparison against gage data from the NOAA Global Historical Climatology Network daily (GHCNd) for the periods 1981-2013 and 1998-2013. A Microsoft Excel workbook is provided, which tabulates ...

The list of communes of France contains 38 data allowing to identify the communes and to link the French communes with files from the Open Data, using the Insee code of the commune, or the postal code(s), codes of departments, regions, cantons or academy. The files also contain data on population, area, density, coordinates (of the town hall and geographical center), altitude (average, minimum and maximum) and various information. The simplified geography of the territory of the communes is present in the files marked "with geography" or "with polygon". The names of the cities are offered in 5 different formats (with or without article and/or preposition, in lower or upper case...). The municipalities of the overseas departments, regions and collectivities (DROM-COM) are included in the files but some data may be missing. ### Available file formats The files are available in csv, csv.gz and json on data.gouv.fr. Files in Excel (xlsx), Parquet (.parquet) and Feather (.feather) formats are not accepted on data.gouv.fr but are freely available on villedereve.fr/open-data-donnees-libres-sur-les-communes. ### Years Available The files are available for the years 2022, 2023, 2024 and 2025. The geographies used are those of year N-1 (e.g. 1 January 2024 for file 2025). The differences between the files from one year to the next mainly concern the population as well as administrative changes (groupings or deletions of municipalities, mainly). ### List of data available in files - insee_code: Common code, INSEE code, code assigned by INSEE to the municipality - standard_name: Standard name of the municipality, with its article (e.g.: Le Havre) - name_without_pronoun: Name of the municipality, without its article if applicable (e.g. Havre) - name_a: Name of the municipality, preceded by the preposition to, to or from and article of the municipality, if applicable (e.g.: Le Havre) - name of: Name of the municipality, preceded by the preposition of the municipality's article(s), if any (e.g. Le Havre) - name_without_accent: Name of the municipality without accent, special characters or spaces - Standard_name: Name of municipality in capital letters (e.g.: THE HAVRE) - typecom: Type of municipality in abbreviated version (COM, COMA, COMD, ARM) - typecom_text: Type of municipality in text version - reg_code: Region code assigned by INSEE to the region of the municipality - reg_name: Name of the region where the municipality is located - dep_code: Department code assigned by INSEE to the department of the commune - dep_nom: Name of the department where the municipality is located - canton_code: Canton code of the commune - canton_name: Name of the canton of the municipality - epci_code: EPCI code (public institutions of inter-municipal cooperation) assigned by INSEE to the region of the municipality - epci_name: Name of the EPCI where the municipality is located - postal_code: Main postal code of the municipality - postal_codes: Postal codes attached to the municipality - academie_code: Code of the academy of attachment of the schools of the commune - academie_nom: Name of the home academy - employment_zone: Area of use of the municipality, defined by INSEE - code_insee_centre_zone_emploi: INSEE code of the municipality centre of the area of employment - population: Municipal population - area_hectare: Area of the municipality, in hectare - area_km2: Area of the municipality, in km2 - density: Density of the municipality, inhabitant per km2 - average_altitude: Average altitude, m - minimum_altitude: Minimum altitude, m - maximum_altitude: Maximum altitude, m - latitude_mairie: Latitude of the town hall - longitude_mairie: Longitude of the town hall - latitude_centre: Latitude of the centroid of the communal territory - longitude_centre: Longitude of the centroid of the communal territory - densite_grid: Communal grid of density at 7 levels, according to INSEE - nice: Gentile (names of inhabitants) - url_wikipedia: URL of the wikipedia page of the municipality - url_villedereve: URL of the page City of dream of the municipality ### Data source - INSEE - geo.api.gouv.fr - Ministry of Education - La Poste

US Presidents Dataset

US Presidents: Birth, Death, Terms, and Parties

By Throwback Thursday [source]

About this dataset

The dataset also contains essential personal information, including each president's date of birth and date of death. Additionally, it includes specific details about when each president took office and when they left office.

Furthermore, the dataset provides insight into where each president was born and where they ultimately passed away. This includes information on both the cities and states associated with their births and deaths.

With this extensive collection of data on US presidents throughout history, researchers can analyze trends related to education backgrounds, regional representation among presidents' origins and final resting places, as well as political party distributions throughout different eras in American history

How to use the dataset

• Number: The numerical order of the US Presidents

  • This column provides the sequential number assigned to each President. You can use this information to quickly identify specific presidents within the dataset.

• Colleges: The colleges or universities attended by the US Presidents

  • In this column, you can find details about which colleges or universities each President attended during their academic years.

• Birth City: The city where the US Presidents were born

  • This column lists the birth city of each President. It can be interesting to explore patterns or similarities between their places of birth.

• Birth State: The state where the US Presidents were born

  • Similar to Birth City, this column contains information about which state each President was born in.

• Birth Date: The date of birth for each President

  • Discovering famous birthdays has always been intriguing! Explore this column for insights into when these influential figures were born.

• Death City: The city where the US Presidents died

  • Uncover notable locations by exploring where each President passed away using this data column.

• Death State: The state where the US Presidents died

  • Just like Death City, you can gain insights into important locations associated with Presidential deaths through this data field.

• Death Date: The date of death for each President

  • Although it is a solemn topic, knowing when these historical figures passed away offers context within their lifetime.

• Left Office :The date when people left office

• Took Office:The date when US Presidents took office.

• Party: The political party affiliation of the US Presidents

  • Understanding the political party affiliations of each President can reveal interesting trends, patterns, and shifts in party dominance over time.

By utilizing this dataset and interpreting these columns, you can gain valuable insights into the lives and backgrounds of the US Presidents. Additionally, this information also allows for comparisons between presidents based on various factors such as birthplace or educational background.

Feel free to leverage visualizations, statistical analyses or create your research questions to dive deeper into this data!

Remember that using dates from different columns together will help you organize and analyze the

Research Ideas

• analyzing the relationship between the colleges attended by US Presidents and their political affiliations
• studying the impact of geographical factors, such as birth cities and states, on presidential careers or political ideologies
• examining trends in terms served and the length of time between taking office and leaving office for different political parties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ThrowbackDataThursday Week 8 - US Presidents.csv | Column name | Description | |:----------------|:--------------------------------------------------------------------------------------| | Number | The numerical order of each US President. (Numeric) | | Colleges | Information about the colleges or universities attended by each President. (Text) | | Birth City | The city where each President was born. (Text) | | Birth State | The state where each President was born. (Text) | | ...

Go Linter Evaluation Dataset

This is a publicly available dataset for 'An empirical evaluation of Golang static code analysis tools for real-world issues.' Please refer to the data according to the names of the spreadsheets.

Authors: Jianwei Wu, James Clause

Collected Survey Data:
- This Excel file contains the collected survey data for the empirical study in details.

R Scripts and Raw Data:
- These scripts are used for data analysis and processing.
- This is the initial data collected from surveys or other sources before any processing or analysis.

Surveys for External Participants:
- This Excel file contains survey data collected for the evaluation of Go linters.
- This folder contains the surveys sent to external participants for collecting their feedback or data.

Recruitment Letter.pdf:
- This PDF contains an example of the recruitment letter sent to potential survey participants, inviting them to take part in the study.

Outputs from Existing Go Linters and Summarized Categories.xlsx:
- This Excel file contains outputs from various Go linters and categorized summaries of these outputs. It helps in comparing the performance and features of different linters.

Selection of Go Linters.xlsx:
- This Excel file lists the Go linters selected for evaluation, along with criteria or reasons for their selection.

UD IRB Exempt Letter.pdf:
- This PDF contains the Institutional Review Board (IRB) exemption letter from the University of Delaware (UD), indicating that the study involving human participants was exempt from full review.

Survey Template.pdf:
- This PDF contains an example of the survey sent to the participants.

govet issues.pdf:
- This PDF contains a list of reported issues about govet. Collected from various pull requests.

Approved linters:
- staticcheck gofmt govet revive gosec deadcode errcheck

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5 σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student's t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5 σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply.