CollegeScorecard US College Graduation and Opportunity Data

Exploring Student Success and Outcomes

By Noah Rippner [source]

About this dataset

This dataset provides an in-depth look at the data elements for the US College CollegeScorecard Graduation and Opportunity Project Use Case. It contains information on the variables used to create a comprehensive report, including Year, dev-category, developer-friendly name, VARIABLE NAME, API data type, label, VALUE, LABEL , SCORECARD? Y/N , SOURCE and NOTES. The data is provided by the U.S Department of Education and allows parents, students and policymakers to take meaningful action to improve outcomes. This dataset contains more than enough information to allow people like Maria - a 25 year old recent US Army veteran who wants a degree in Management Systems and Information Technology -to distinguish between her school options; access services; find affordable housing near high-quality schools which are located in safe neighborhoods that have access to transport links as well as employment opportunities nearby. This highly useful dataset provides detailed analysis of all this criteria so that users can make an informed decision about which school is best for them!

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How to use the dataset

This dataset contains data related to college students, including their college graduation rates, access to opportunity indicators such as geographic mobility and career readiness, and other important indicators of the overall learning experience in the United States. This guide will show you how to use this dataset to make meaningful conclusions about high education in America.

First, you will need to be familiar with the different fields included in this CollegeScorecard’s US College Graduation and Opportunity Data set. Each record is comprised of several data elements which are defined by concise labels on the left side of each observation row. These include labels such as Name of Data Element, Year, dev-category (i.e., developmental category), Variable Name, API data type (i.e., type information for programmatic interface), Label (i.e., descriptive content labeling for visual reporting), Value , Label (i.e., descriptive value labeling for visual reporting). SCORECARD? Y/N indicates whether or not a field pertains to U.S Department of Education’s College Scorecard program and SOURCE indicates where the source of the variable can be found among other minor details about that variable are found within Notes column attributed beneath each row entry for further analysis or comparison between elements captured across observations

Now that you understand the components associated within each element or label related within Observation Rows identified beside each header label let’s go over some key steps you can take when working with this particular dataset:

• Utilize year specific filters on specified fields if needed — e.g.; Year = 2020 & API Data Type = Character

• Look up any ‘NCalPlaceHolder” values if applicable — these are placeholders often stating values have been absolved fromScorecards display versioning due conflicting formatting requirements across standard conditions being met or may state these details have still yet been updated recently so upon assessment wait patiently until returns minor changes via API interface incorporate latest returned results statements inventory configuration options relevant against budgetary cycle limits established positions

• Pivot data points into more custom tabular structured outputs tapering down complex unstructured RAW sources into more digestible Medium Level datasets consumed often via PowerBI / Tableau compatible Snapshots expanding upon Delimited text exports baseline formats provided formerly

• Explore correlations between education metrics our third parties documents generated frequently such values indicative educational adherence effects ROI growth potential looking beyond Campus Panoramic recognition metrics often supported outside Social Medial Primary

Research Ideas

• Creating an interactive dashboard to compare school performance in terms of safety, entrepreneurship and other criteria.
• Using the data to create a heat map visualization that shows which cities are most conducive to a successful educational experience for students like Maria.
• Gathering information about average course costs at different universities and mapping them relative to US unemployment rates indicates which states might offer the best value for money when it comes to higher education expenses

Ack...

The service is part of KonsortSWD project deliverable, NFDI funding number 442494171.

Debates have arisen as to whether non-human animals actually can learn abstract non-symbolic numerousness or whether they always rely on some continuous physical aspect of the stimuli, covarying with number. Here we investigated archerfish (Toxotes jaculatrix) non-symbolic numerical discrimination with accurate control for co-varying continuous physical stimulus attributes. Archerfish were trained to select one of two groups of black dots (Exp. 1: 3 vs. 6 elements; Exp. 2: 2 vs. 3 elements); these were controlled for several combinations of physical variables (elements’ size, overall area, overall perimeter, density and sparsity), ensuring that only numerical information was available. Generalization tests with novel numerical comparisons (2 vs. 3, 5 vs. 8 and 6 vs. 9 in Exp. 1; 3 vs. 4, 3 vs. 6 in Exp. 2) revealed choice for the largest or smallest numerical group according to the relative number that was rewarded at training. None of the continuous physical variables, including spatia...

Introduction

The Free-living Food Intake Cycle (FreeFIC) dataset was created by the Multimedia Understanding Group towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the (3D) acceleration and orientation velocity signals ((6) DoF) from (22) in-the-wild sessions provided by (12) unique subjects. All sessions were recorded using a commercial smartwatch ((6) using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants.

Description

FreeFIC includes (22) in-the-wild sessions that belong to (12) unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the (22) recordings sums up to (112.71) hours, with a mean duration of (5.12) hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below.

The script(s) and the pickle file must be located in the same directory.

Tested with Python 3.6.4

Requirements: Numpy, Pickle and Matplotlib

Calculate and echo dataset statistics

$ python stats_dataset.py

Visualize signals and ground truth

$ python viz_dataset.py

FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found here and here.

Publications

If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work:

@article{kyritsis2020data,
title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches},
author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios},
journal={IEEE Journal of Biomedical and Health Informatics}, year={2020},
publisher={IEEE}}

@inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
year={2019}, organization={IEEE}}

Technical details

We provide the FreeFIC dataset as a pickle. The file can be loaded using Python in the following way:

import pickle as pkl import numpy as np

with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh)

The dataset variable in the snipet above is a dictionary with (5) keys. Namely:

'subject_id'

'session_id'

'signals_raw'

'signals_proc'

'meal_gt'

The contents under a specific key can be obtained by:

sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth

The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to (22). Elements across all lists are aligned; e.g., the (3)rd element of the list under the 'session_id' key corresponds to the (3)rd element of the list under the 'signals_proc' key.

sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: ([1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]). It should be emphasized that the subjects with ids (15, 16, 17, 18, 19) and (20) belong to the held-out part of the FreeFIC dataset (more information can be found in ( )the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info here and here); i.e., FIC’s subject with id equal to (2) is the same person as FreeFIC’s subject with id equal to (2).

ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between (1) and (5). It should be noted that not all subjects have the same number of sessions.

raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a (N_{acc} \times 4) numpy.ndarray that contains the timestamps in seconds (first column) and the (3D) raw accelerometer measurements in (g) (second, third and forth columns - representing the (x, y ) and (z) axis, respectively). The data under the 'gyr' key is a (N_{gyr} \times 4) numpy.ndarray that contains the timestamps in seconds (first column) and the (3D) raw gyroscope measurements in ({degrees}/{second})(second, third and forth columns - representing the (x, y ) and (z) axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info here and here). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different ((N_{acc} eq N_{gyr})). This behavior is predictable and is caused by the Android platform.

proc: list Each element of this list is an (M\times7) numpy.ndarray that contains the timestamps, (3D) accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the (x,y) and (z) accelerometer values in (g) and the fifth, sixth and seventh columns contain the (x,y) and (z) gyroscope values in ({degrees}/{second}). Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of (100) Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info here and here). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component).

meal_gt: list Each element of this list is a (K\times2) matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals (K) varies across recordings (e.g., a recording exist where a participant consumed two meals).

Ethics and funding

Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - BigO: Big data against childhood obesity.

Contact

Any inquiries regarding the FreeFIC dataset should be addressed to:

Dr. Konstantinos KYRITSIS

Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124

Tel: +30 2310 996359, 996365 Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

This project was undertaken to establish a computerized skeletal database composed of recent forensic cases to represent the present ethnic diversity and demographic structure of the United States population. The intent was to accumulate a forensic skeletal sample large and diverse enough to reflect different socioeconomic groups of the general population from different geographical regions of the country in order to enable researchers to revise the standards being used for forensic skeletal identification. The database is composed of eight data files, comprising four categories. The primary "biographical" or "identification" files (Part 1, Demographic Data, and Part 2, Geographic and Death Data) comprise the first category of information and pertain to the positive identification of each of the 1,514 data records in the database. Information in Part 1 includes sex, ethnic group affiliation, birth date, age at death, height (living and cadaver), and weight (living and cadaver). Variables in Part 2 pertain to the nature of the remains, means and sources of identification, city and state/country born, occupation, date missing/last seen, date of discovery, date of death, time since death, cause of death, manner of death, deposit/exposure of body, area found, city, county, and state/country found, handedness, and blood type. The Medical History File (Part 3) represents the second category of information and contains data on the documented medical history of the individual. Variables in Part 3 include general comments on medical history as well as comments on congenital malformations, dental notes, bone lesions, perimortem trauma, and other comments. The third category consists of an inventory file (Part 4, Skeletal Inventory Data) in which data pertaining to the specific contents of the database are maintained. This includes the inventory of skeletal material by element and side (left and right), indicating the condition of the bone as either partial or complete. The variables in Part 4 provide a skeletal inventory of the cranium, mandible, dentition, and postcranium elements and identify the element as complete, fragmentary, or absent. If absent, four categories record why it is missing. The last part of the database is composed of three skeletal data files, covering quantitative observations of age-related changes in the skeleton (Part 5), cranial measurements (Part 6), and postcranial measurements (Part 7). Variables in Part 5 provide assessments of epiphyseal closure and cranial suture closure (left and right), rib end changes (left and right), Todd Pubic Symphysis, Suchey-Brooks Pubic Symphysis, McKern & Steward--Phases I, II, and III, Gilbert & McKern--Phases I, II, and III, auricular surface, and dorsal pubic pitting (all for left and right). Variables in Part 6 include cranial measurements (length, breadth, height) and mandibular measurements (height, thickness, diameter, breadth, length, and angle) of various skeletal elements. Part 7 provides postcranial measurements (length, diameter, breadth, circumference, and left and right, where appropriate) of the clavicle, scapula, humerus, radius, ulna, scarum, innominate, femur, tibia, fibula, and calcaneus. A small file of noted problems for a few cases is also included (Part 8).

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2022-23 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to K-12 public schools that were open in October 2022 to coincide with the official 2022-23 student enrollment counts collected on Fall Census Day in 2022 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

The table UCMR 4 HAA Addtl. Data Elements is part of the dataset Unregulated Contaminant Monitoring Rule ***, available at https://redivis.com/datasets/fvhc-9z0abnn4w. It contains 265885 rows across 7 variables.

A thorough summary of students' academic achievement as well as the different personal, social, and lifestyle elements that may affect it may be found in this dataset, which is named "Student Academic achievement and Lifestyle Dataset,". It gathers data on the demographics, study habits, family history, extracurricular activities, and other pertinent factors of pupils. Researchers, educators, and data analysts may better grasp the intricate connections between these variables and children' academic achievement with the use of this dataset.

145 student records and 33 attributes make up the dataset; each record corresponds to a distinct student. Age, gender, high school graduation type, scholarship type, weekly study hours, parental education and employment, and participation at seminars or conferences are some of the characteristics that are included. Indicators of behavior and engagement, such as reading frequency, attendance, note-taking practices, and involvement in debates or flipped classes, are also included. Course grades, cumulative GPA, and predicted GPA at graduation are other metrics used to assess academic success.

The purpose of the data collection was to examine the ways in which environmental and personal factors influence differences in student performance. It offers insightful information on the behavioral and academic trends that might influence student performance. Exploratory data analysis (EDA), data visualization, and predictive modeling, such as forecasting GPA or discovering important elements impacting academic achievement, are only a few of the uses for this dataset. Additionally, it may be used as a resource for educational data mining and the creation of systems for tracking student performance.

All things considered, this dataset provides a solid basis for investigating academic performance from a multidisciplinary perspective, including educational, social, and

Many passerines have elaborated songs hypothesized to have evolved through sexual selection. Extra-pair mating can be a contributing factor in the evolution of complex songs by increasing the variance in male fitness. We investigated this by quantifying the relationship between male song performance and complexity and levels of paternity loss through extra-pair mating by their female mates in the Grass Wren (Cistothorus platensis), a socially monogamous passerine with elaborate songs. We conducted fieldwork in the Uspallata Valley, Mendoza, Argentina, over two breeding seasons and recorded the songs of 30 focal males during the egg-laying stage of their social mate. We collected blood samples from adults and nestlings and used ddRAD sequencing SNP data to determine parentage. We assessed extra-pair mating behaviour of females by measuring paternity loss of their social partner and examined whether variation in paternity loss was associated with structural characteristics of that male’s ..., , # Elements of male song performance and complexity are associated with reduced risk of paternity loss in a South American passerine

Dataset DOI: 10.5061/dryad.zcrjdfnqz

Description of the data and file structure

GENERAL INFORMATION

Title of Dataset: Elements of male song performance and complexity are associated with reduced risk of paternity loss in a South American passerine

Date of data collection: October to February 2016-2017

Geographic location of data collection: Uspallata, Mendoza, Argentina

Files and variables

File: data_1_ESM.csv

Description:Â Structural song measurements and EPP measurements for each male

Variables

• id_male:Â The leg band combination of male
• number_male:Â The number of the aluminum leg band
• year:Â Breeding season 2016 or 2017
• male_year:Â Concatenation between male and breeding season
• id_nest:Â Identification of studied nest
• delta_time:Â Song duration (s) Difference between Begin time and End Ti...,

Dataset abstract The dataset includes an annotated dataset of N = 1413 sentences (or parts thereof) taken from an authentic spoken corpus data from West Flemish and French Flemish (Dialects of Dutch). The sentences are annotated for V2 variation (Subject-Verb inversion, the outcome variable of the associated study) and seven predictor variables, including city, region, prosodic integration, form and function of the topicalized constituent, form of the subject, and the number of constituents in the prefield on (non)inverted word order. The dataset also includes geographical data to create a dialect map showing the relative frequencies of V2 variation. An R Notebook with the data analysis is provided. Article abstract This paper explores V2 variation in West Flemish and French Flemish dialects of Dutch based on an extensive corpus of authentic spoken data. After taking stock of the existing literature, we probe into the effect of region, prosodic integration, form and function of the topicalized constituent, form of the subject, and the number of constituents in the prefield on (non)inverted word order. This is the first study that carries out regression analysis on the combined impact of these variables in the entire West Flemish and French Flemish region, with additional visualization of effect sizes. The results show that noninversion is generally more widespread than originally anticipated, with unexpected higher occurrence of noninversion in continental West Flemish and lower frequencies in western West Flemish. With the exception of the variable number of constituents in the prefield, all other variables had a significant impact on word order: Clausal topicalized elements, elements that have peripheral functions, and elements that lack prosodic integration all favor noninverted word order. The form of the subject also impacted word order, but its effect is sometimes overruled by discourse considerations.

GameStop Customer Reviews Dataset

Analytical Review Data of GameStop Products

By Crawl Feeds [source]

About this dataset

GameStop Product Reviews Dataset

Comprehensive and Detailed Customer Reviews and Ratings of Products from GameStop

Data Overview:

This dataset comprises a rich variety of information centered on customer reviews and ratings for products purchased from GameStop. For each review, the data includes detailed aspects such as the product name, brand, SKU (Stock Keeping Unit), helpful and non-helpful votes count, reviewer's name along with their review title & description. Further insights can be found through additional features that outline whether or not the reviewer recommends the product, whether they are a verified purchaser and encompass individual & average ratings for each product.

Other significant facets encapsulated within this valuable resource involve multimedia elements like images posted in reviews. To verify temporal relevance, timestamps revealing when the review was written (reviewed_at) as well as when the data was collected (scraped_at) are provided.

Additionally, URLs related to both specific items up for purchase (url) at GameStop's site and other users' reviews pages (reviews_link) have been accumulated within. The total number of customer feedback posts per item is also available under reviews_count series.

Structure:

The dataset structure presents serialized versions of the afore-mentioned fields. This includes strings such as 'name', 'brand', 'review_title', etc; date times including 'reviewed_at' and 'scraped_at'; floating point numbers such as 'rating' & 'average_rating'; integers representing counts ('helpful_count','not_helpful_count' ); boolean flags determining reviewers recommendations or verified purchase status ('recommended_review','verifed_purchaser') along with some potential null entries spotted across several columns making it dynamic yet intuitive even to an unfamiliar eye.

Use Case:

This dataset can serve multiple functions depending largely on user requirements.There are intriguing prospects around tracking consumer sentiments across time periods which could lend fascinating insights into sales patterns.Another possibility might revolve around determining best selling items or brands on GameStop according to customer impressions and sales counts. Additionally, there is potential to link buying trends with whether the product was purchased legitimately or not.

This dataset could also be used by product managers to enhance existing ones or create improved versions of them taking into account customer suggestions from their review content.Finaly, marketing teams could use this dataset to strategize campaigns by identifying products with positive reviews & scaling promotions for those.

Of course,the versatility of this resource opens up vast domains, ranging from sentiment analysis and recommendation systems using machine learning methodologies,to data visualization projects that help demonstrate consumer trends in a more approachable

How to use the dataset

• Sentiment Analysis: Use the 'review_description' field to understand customer sentiment towards specific products. NLP techniques can be deployed to derive sentiments from reviews text, which could help in understanding overall consumer opinion.

• Brand Analysis: Use the 'brand' field for comparative analysis of various brands sold on GameStop's platform.

• Product Recommendation System: Develop a product recommendation system based on the user's past purchase record represented by 'brand', 'sku', and past reviews.

• Customer Segmentation: Analyse fields like 'rating', 'recommended_review', and 'verifed_purchaser' for advanced segmentation of customers.

• Product Performance Analysis: By examining fields like average rating (average_rating), number of reviews (reviews_count), recommend status (recommended_review), one can gauge how well a product is performing or received by customers.

• Review Popularity Analysis: The dataset features two interesting variables - helpful_count and not_helpful_count; these reflect how other users perceived a review’s usefulness in helping them make purchasing decisions.

7 .**Time Series Forecasting**: Although we're instructed not to include any dates here, don't forget that this dataset has temporal elements ('reviewed_at') you could use for forecasting trends over time!

8 .**Reviewer Trustworthiness Assessment**: The verified purchaser field can be used as an indicator for trustworthiness of the review or reviewer bias.

P...

The General Household Survey (GHS) is a continuous national survey of people living in private households conducted on an annual basis, by the Social Survey Division of the Office for National Statistics (ONS). The main aim of the survey is to collect data on a range of core topics, covering household, family and individual information. This information is used by government departments and other organisations for planning, policy and monitoring purposes, and to present a picture of house holds, family and people in Great Britain. From 2008, the General Household Survey became a module of the Integrated Household Survey (IHS). In recognition, the survey was renamed the General Lifestyle Survey (GLF/GLS). The GHS started in 1971 and has been carried out continuously since then, except for breaks in 1997-1998 when the survey was reviewed, and 1999-2000 when the survey was redeveloped. Following the 1997 review, the survey was relaunched from April 2000 with a different design. The relevant development work and the changes made are fully described in the Living in Britain report for the 2000-2001 survey. Following its review, the GHS was changed to comprise two elements: the continuous survey and extra modules, or 'trailers'. The continuous survey remained unchanged from 2000 to 2004, apart from essential adjustments to take account of, for example, changes in benefits and pensions. The GHS retained its modular structure and this allowed a number of different trailers to be included for each of those years, to a plan agreed by sponsoring government departments. Further changes to the GHS methodology from 2005: From April 1994 to 2005, the GHS was conducted on a financial year basis, with fieldwork spread evenly from April of one year to March the following year. However, in 2005 the survey period reverted to a calendar year and the whole of the annual sample was surveyed in the nine months from April to December 2005. Future surveys will run from January to December each year, hence the title date change to single year from 2005 onwards. Since the 2005 GHS (held under SN 5640) does not cover the January-March quarter, this affects annual estimates for topics which are subject to seasonal variation. To rectify this, where the questions were the same in 2005 as in 2004-2005, the final quarter of the latter survey was added (weighted in the correct proportion) to the nine months of the 2005 survey. Furthermore, in 2005, the European Union (EU) made a legal obligation (EU-SILC) for member states to collect additional statistics on income and living conditions. In addition to this the EU-SILC data cover poverty and social exclusion. These statistics are used to help plan and monitor European social policy by comparing poverty indicators and changes over time across the EU. The EU-SILC requirement has been integrated into the GHS, leading to large-scale changes in the 2005 survey questionnaire. The trailers on 'Views of your Local Area' and 'Dental Health' have been removed. Other changes have been made to many of the standard questionnaire sections, details of which may be found in the GHS 2005 documentation. Further changes to the GLF/GHS methodology from 2008 As noted above, the General Household Survey (GHS) was renamed the General Lifestyle Survey (GLF/GLS) in 2008. The sample design of the GLF/GLS is the same as the GHS before, and the questionnaire remains largely the same. The main change is that the GLF now includes the IHS core questions, which are common to all of the separate modules that together comprise the IHS. Some of these core questions are simpl y questions that were previously asked in the same or a similar format on all of the IHS component surveys (including the GLF/GLS). The core questions cover employment, smoking prevalence, general health, ethnicity, citizenship and national identity. These questions are asked by proxy if an interview is not possible with the selected respondent (that is a member of the household can answer on behalf of other respondents in the household). This is a departure from the GHS which did not ask smoking prevalence and general health questions by proxy, whereas the GLF/GLS does from 2008. For details on other changes to the GLF/GLS questionnaire, please see the GLF/GLS 2008: Special Licence Access documentation held with SN 6414. Currently, the UK Data Archive holds only the SL (and not the EUL) version of the GLF/GLS for 2008. Changes to the drinking section There have been a number of revisions to the methodology that is used to produce the alcohol consumption estimates. In 2006, the average number of units assigned to the different drink types and the assumption around the average size of a wine glass was updated, resulting in significantly increased consumption estimates. In addition to the revised method, a new question about wine glass size was included in the survey in 2008. Respondents were asked whether they have consumed small (125 ml), standard (175 ml) or large (250 ml) glasses of wine. The data from this question are used when calculating the number of units of alcohol consumed by the respondent. It is assumed that a small glass contains 1.5 units, a standard glass contains 2 units and a large glass contains 3 units. (In 2006 and 2007 it was assumed that all respondents drank from a standard 175 ml glass containing 2 units.) The datasets contain the original set of variables based on the original methodology, as well as those based on the revised and (for 2008 onwards) updated methodologies. Further details on these changes are provided in the Guidelines documents held in SN 5804 - GHS 2006; and SN 6414 - GLF/GLS 2008: Special Licence Access. Special Licence GHS/GLF/GLS Special Licence (SL) versions of the GHS/GLF/GLS are available from 1998-1999 onwards. The SL versions include all variables held in the standard 'End User Licence' (EUL) version, plus extra variables covering cigarette codes and descriptions, and some birthdate information for respondents and household members. Prospective SL users will need to complete an extra application form and demonstrate to the data owners exactly why they need access to t he extra variables, in order to get permission to use the SL version. Therefore, most users should order the EUL version of the data. In order to help users choose the correct dataset, 'Special Licence Access' has been added to the dataset titles for the SL versions of the data. A list of all GHS/GLF/GLS studies available from the UK Data Archive may be found on the GHS/GLF/GLS major studies web page. See below for details of SL datasets for the corresponding GHS/GLF/GLS year (1998-1999 onwards only). UK Data Archive data holdings and formats The UK Data Archive GHS/GLF/GLS holdings begin with the 1971 study for EUL data, and from 1998-1999 for SL versions (see above). Users should note that data for the 1971 study are currently only available as ASCII files without accompanying SPSS set-up files. SPSS files for the 1972 study were created by John Simister, and redeposited at the Archive in 2000. Currently, the UK Data Archive holds only the SL versions of the GHS/GLF/GLS for 2007 and 2008. Reformatted Data 1973 to 1982 - Surrey SPSS Files SPSS files have been created by the University of Surrey for all study years from 1973 to 1982 inclusive. These early files were restructured and the case changed from the household to the individual with all of the household information duplicated for each individual. The Surrey SPSS files contain all the original variabl es as well as some extra derived variables (a few variables were omitted from the data files for 1973-76). In 1973 only, the section on leisure was not included in the Surrey SPSS files. This has subsequently been made available, however, and is now held in a separate study, General Household Survey, 1973: Leisure Questions (held under SN 3982). Records for the original GHS 1973-1982 ASCII files have been removed from the UK Data Archive catalogue, but the data are still preserved and available upon request. Users should note that GHS/GLF/GLS data are also available in formats other than SPSS.

This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2018. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR® Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases

D I S C L A I M E R: Normalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter.

Home Performance with ENERGY STAR® Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings represent contractor-reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the Home Performance with ENERGY STAR impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf.

This dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides state-aggregated data for hospital utilization. These are derived from reports with facility-level granularity across three main sources: (1) National Healthcare Safety Network (NHSN) (after December 15, 2022) (2) HHS TeleTracking (before December 15, 2022), (3) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities, and (4) historical NHSN timeseries data (before July 15, 2020). Data in this file have undergone routine data quality review of key variables of interest by subject matter experts to identify and correct obvious data entry errors.

The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

No statistical analysis is applied to account for non-response and/or to account for missing data.

The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

This file contains data that have been corrected based on additional data quality checks applied to select data elements. The resulting dataset allows various data consumers to use for their analyses a high-quality dataset with consistent standards of data processing and cleaning applied.

The following fields in this dataset are derived from data elements included in these data quality checks: