Mission statement, Vision and Core Values of the OIG. Includes links to the OIG FY 2015 Action Plan, OIG Status Report on NAPA Recommendations, 2012 and the OIG Organizational Assessment, National Academy of Public Administration (NAPA), 2009

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

Iowa Housing Data

The original Ames data that is being used for the competition House Prices: Advanced Regression Techniques and predicting sales price is edited and engineered to suit a beginner for applying a model without worrying too much about missing data while focusing on the features.

Contents

The train data has the shape 1460x80 and test data has the shape 1458x79 with feature 'SalePrice' to be predicted for the test set. The train data has different types of features, categorical and numerical.

A detailed info about the data can be obtained from the Data Description file among other data files.

Transformations

a. Handling Missing Values: Some variables such as 'PoolQC', 'MiscFeature', 'Alley' have over 90% missing values. However from the data description, it is implied that the missing value indicates the absence of such features in a particular house. Well, most of the missing data implies the feature does not exist for the particular house on further inspection of the dataset and data description.

Similarly, features which are missing such as 'GarageType', 'GarageYrBuilt', 'BsmtExposure', etc indicated no garage in that house but also corresponding attributes such as 'GarageCars', 'GarageArea','BsmtCond' etc are set to 0.

A house on a street might have similar front lawn area to the houses in the same neighborhood, hence the missing values can be median of the values in a neighborhood.

Missing values in features such as 'SaleType', 'KitchenCond', etc have been imputed with the mode of the feature.

b. Dropping Variables: 'Utilities' attribute should be dropped from the data frame because almost all the houses have all public Utilities (E,G,W,& S) available.

c. Further exploration: The feature 'Electrical' has one missing value. The first intuition would be to drop the row. But on further inspection, the missing value is from a house built in 2006. After the 1970's all the houses have Standard Circuit Breakers & Romex 'SkBrkr' installed. So, the value can be inferred from this observation.

d. Transformation: There were some variables which are really categorical but were represented numerically such as 'MSSubClass', 'OverallCond' and 'YearSold'/'MonthSold' as they are discrete in nature. These have also been transformed to categorical variables.

e. X Normalizing the 'SalePrice' Variable: During EDA it was discovered that the Sale price of homes is right skewed. However on normalizing the skewness decreases and the (linear) models fit better. The feature is left for the user to normalize.

Finally the train and test sets were split and sale price appended to train set.

Acknowledgements

The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.

Inspiration

The data after the transformation done by me can easily be fitted on to a model after label encoding and normalizing features to reduce skewness. The main variable to be predicted is 'SalePrice' for the TestData csv file.

This data release includes biomarker ratio values calculated from measurements made at the USGS for the reference oil NSO-1 that were reported in a journal article entitled Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS.

This is version 3.1.1.2020f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v3.1.0.2019f to include 2020 and so spans 1931-2020. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20210101_v3-1-1-2020f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/ References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note. Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016. Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

aThe percentages for each city were computed from [58] using the countr y's percentage of children under 20 years old. Taiwan's percentage was obtained from [59].

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Population Health (BRFSS: HRQOL)

Examining Trends, Disparities and Determinants of Health in the US Population

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.

The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!

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How to use the dataset

This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.

Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.

Research Ideas

• Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
• Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
• Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc

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: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...

The Audio Cartography project investigated the influence of temporal arrangement on the interpretation of information from a simple spatial data set. I designed and implemented three auditory map types (audio types), and evaluated differences in the responses to those audio types.

The three audio types represented simplified raster data (eight rows x eight columns). First, a "sequential" representation read values one at a time from each cell of the raster, following an English reading order, and encoded the data value as loudness of a single fixed-duration and fixed-frequency note. Second, an augmented-sequential ("augmented") representation used the same reading order, but encoded the data value as volume, the row as frequency, and the column as the rate of the notes play (constant total cell duration). Third, a "concurrent" representation used the same encoding as the augmented type, but allowed the notes to overlap in time.

Participants completed a training session in a computer-lab setting, where they were introduced to the audio types and practiced making a comparison between data values at two locations within the display based on what they heard. The training sessions, including associated paperwork, lasted up to one hour. In a second study session, participants listened to the auditory maps and made decisions about the data they represented while the fMRI scanner recorded digital brain images.

The task consisted of listening to an auditory representation of geospatial data ("map"), and then making a decision about the relative values of data at two specified locations. After listening to the map ("listen"), a graphic depicted two locations within a square (white background). Each location was marked with a small square (size: 2x2 grid cells); one square had a black solid outline and transparent black fill, the other had a red dashed outline and transparent red fill. The decision ("response") was made under one of two conditions. Under the active listening condition ("active") the map was played a second time while participants made their decision; in the memory condition ("memory"), a decision was made in relative quiet (general scanner noises and intermittent acquisition noise persisted). During the initial map listening, participants were aware of neither the locations of the response options within the map extent, nor the response conditions under which they would make their decision. Participants could respond any time after the graphic was displayed; once a response was entered, the playback stopped (active response condition only) and the presentation continued to the next trial.

Data was collected in accordance with a protocol approved by the Institutional Review Board at the University of Oregon.

• Additional details about the specific maps used in this are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

• Details of the design process and evaluation are provided in the associated dissertation, which is available from ProQuest and University of Oregon's ScholarsBank.

• Scripts that created the experimental stimuli and automated processing are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

Preparation of fMRI Data

Conversion of the DICOM files produced by the scanner to NiFTi format was performed by MRIConvert (LCNI). Orientation to standard axes was performed and recorded in the NiFTi header (FMRIB, fslreorient2std). The excess slices in the anatomical images that represented tissue in the next were trimmed (FMRIB, robustfov). Participant identity was protected through automated defacing of the anatomical data (FreeSurfer, mri_deface), with additional post-processing to ensure that no brain voxels were erroneously removed from the image (FMRIB, BET; brain mask dilated with three iterations "fslmaths -dilM").

Preparation of Metadata

The dcm2niix tool (Rorden) was used to create draft JSON sidecar files with metadata extracted from the DICOM headers. The draft sidecar file were revised to augment the JSON elements with additional tags (e.g., "Orientation" and "TaskDescription") and to make a more human-friendly version of tag contents (e.g., "InstitutionAddress" and "DepartmentName"). The device serial number was constant throughout the data collection (i.e., all data collection was conducted on the same scanner), and the respective metadata values were replaced with an anonymous identifier: "Scanner1".

Preparation of Behavioral Data

The stimuli consisted of eighteen auditory maps. Spatial data were generated with the rgeos, sp, and spatstat libraries in R; auditory maps were rendered with the Pyo (Belanger) library for Python and prepared for presentation in Audacity. Stimuli were presented using PsychoPy (Peirce, 2007), which produced log files from which event details were extracted. The log files included timestamped entries for stimulus timing and trigger pulses from the scanner.

• Log files are available in "sourcedata/behavioral".
• Extracted event details accompany BOLD images in "sub-NN/func/*events.tsv".
• Three column explanatory variable files are in "derivatives/ev/sub-NN".

References

Audacity® software is copyright © 1999-2018 Audacity Team. Web site: https://audacityteam.org/. The name Audacity® is a registered trademark of Dominic Mazzoni.

FMRIB (Functional Magnetic Resonance Imaging of the Brain). FMRIB Software Library (FSL; fslreorient2std, robustfov, BET). Oxford, v5.0.9, Available: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/

FreeSurfer (mri_deface). Harvard, v1.22, Available: https://surfer.nmr.mgh.harvard.edu/fswiki/AutomatedDefacingTools)

LCNI (Lewis Center for Neuroimaging). MRIConvert (mcverter), v2.1.0 build 440, Available: https://lcni.uoregon.edu/downloads/mriconvert/mriconvert-and-mcverter

Peirce, JW. PsychoPy–psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2):8 – 13, 2007. Software Available: http://www.psychopy.org/

Python software is copyright © 2001-2015 Python Software Foundation. Web site: https://www.python.org

Pyo software is copyright © 2009-2015 Olivier Belanger. Web site: http://ajaxsoundstudio.com/software/pyo/.

R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available: https://www.R-project.org/.

rgeos software is copyright © 2016 Bivand and Rundel. Web site: https://CRAN.R-project.org/package=rgeos

Rorden, C. dcm2niix, v1.0.20171215, Available: https://github.com/rordenlab/dcm2niix

spatstat software is copyright © 2016 Baddeley, Rubak, and Turner. Web site: https://CRAN.R-project.org/package=spatstat

sp software is copyright © 2016 Pebesma and Bivand. Web site: https://CRAN.R-project.org/package=sp

This document, Innovating the Data Ecosystem: An Update of The Federal Big Data Research and Development Strategic Plan, updates the 2016 Federal Big Data Research and Development Strategic Plan. This plan updates the vision and strategies on the research and development needs for big data laid out in the 2016 Strategic Plan through the six strategies areas (enhance the reusability and integrity of data; enable innovative, user-driven data science; develop and enhance the robustness of the federated ecosystem; prioritize privacy, ethics, and security; develop necessary expertise and diverse talent; and enhance U.S. leadership in the international context) to enhance data value and reusability and responsiveness to federal policies on data sharing and management.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

IMS: Inductive Monitoring System The Inductive Monitoring System (IMS) is a tool that uses a data mining technique called clustering to extract models of normal system operation from archived data. IMS works with vectors of data values. IMS analyzes data collected during periods of normal system operation to build a system model. It characterizes how the parameters relate to one another during normal operation by finding areas in the vector space where nominal data tends to fall. These areas are called nominal operating regions and correspond to clusters of similar points found by the IMS clustering algorithm. These nominal operating regions are stored in a knowledge base that IMS uses for real-time telemetry monitoring or archived data analysis. During the monitoring operation, IMS reads real-time or archived data values, formats them into the predefined vector structure, and searches the knowledge base of nominal operating regions to see how well the new data fits the nominal system characterization. For each input vector, IMS returns the distance that vector falls from the nearest nominal operating region. Data that matches the normal training data well will have a deviation distance of zero. If one or more of the data parameters is slightly outside of expected values, a small non-zero result is returned. As incoming data deviates further from the normal system data, indicating a possible malfunction, IMS will return a higher deviation value to alert users of the anomaly. IMS also calculates the contribution of each individual parameter to the overall deviation, which can help isolate the cause of the anomaly.

Historical dataset showing Argentina clean water access by year from N/A to N/A.

The aim of this paper is to assess how values are set through co-design and translated into a housing project. To do so, we develop an analytical framework to conduct a longitudinal single case-study that traces back the co-design process of the resident-led housing cooperative La Borda, in Barcelona. Our findings shed light on how co-design unfolds and uncover trade-offs carried out to overcome tensions mostly between individual and collective demands, and between building costs and quality. The Suplementary File includes the complete dataset of the textual excerpts of the reviewed documents that was used to conduct the analysis.

Sprocket Central Pty Ltd provides a large dataset relating to its customers. The dataset includes information about Customer Demographic, Customer Addresses and Transactions data in the past 3 months. In the present work, we review the data quality to ensure that it's ready for the analysis in phase two. The data quality framework includes: - Accuracy: Correct values - Completness: Data fields with values - Consistency: Values free from contradiction - Currency: Values up to date - Relevancy: Data items with value Meta-data - Validity: Data containing allowable values - Uniqueness: Records that are duplicated

This study will be performed using Python programming language.

Abstract The article deals with the interrelationships between information and value with an intrinsic relation between the two terms. The starting point of the article is the following question: What are the interrelationships between information and value and how do these interrelations produce an impact on the production of types of evaluative information? The objective is to address the underlying fundamentals of the concept of value by perceiving how these concepts interrelate to the concept of information and considering the formation of the types of evaluative information in the causal and consequential context. The methodology was based on a bibliographical research in dialogue with different documentary literature on the concepts of value and information, both in Information Science and related areas such as Philosophy, Sociology, Political Science, History, Education, Linguistics, Psychology, Communication, among others. It may be concluded a perspective of social construction based on interactions that stimulate the decision making of the subjects. that the interrelationship between information and value is developed by causal and consequential issues; these issues observed from a coordinated point of view are one of the main phenomena that promote the dynamics and construction of information among the subjects (rever texto original).

Estimating the value of changes in water quality requires the definition of biophysical features that link changes in ecosystems to changes in social systems. Those linking features must be interpretable to people and serve as effective ecological indicators. This work defines a linking feature that is appropriate for capturing existence values in a forthcoming national stated preference survey of Clean Water Act regulations. Further, we modeled and spatially predicted this feature to account for the dependence of survey respondents’ preference on baseline aquatic conditions near them. We outline steps to provide insights on the mechanisms that will aid in forecasting changes in the linking feature in responses to candidate policy options.

The green data center market in the Middle East and Africa was expected to be worth **** billion U.S. dollars in 2023, a marginal increase on the previous year. Stronger growth is forecast over the coming years, with the market set to be worth **** billion U.S. dollars by 2030.Further information on the green data center market can be found here.

Historical dataset showing Taiwan healthcare spending per capita by year from N/A to N/A.

Database with the review of the economic values of ecosystem services in Colombia