This USGS Data Release represents the synthetic and real data from hydrologically diverse streams used to test the performance and limitations of the Two-tracer Ratio-based Mixing Model (TRaMM) which uses high-frequency measures of two tracers (A and B) and streamflow to separate total streamflow into water from slowflow and fastflow sources. The ratio between the concentrations of the two tracers is used to create a time-variable estimate of the concentration of each tracer in the fastflow end-member. Synthetic data from a groundwater dominated stream and an overland flow dominated stream were used to test the sensitivity of the model to various conditions and tracer concentrations. The sensitivity analysis provides understanding of the relation between the inputs and outputs of the model while providing information regarding its practicality and limitations. High-frequency nitrate and specific conductance data from Chesterville Branch, Maryland and Indian Creek, Kansas in 2013 were used to test the model under real-world conditions. These data support the following publication: Kronholm, S.C. and Capel, P.D., 2016, Estimation of time-variable fast flow path chemical concentrations for application in tracer-based hydrograph separation analyses: Water Resour. Res., 52, 6881-6896, https://doi.org/10.1002/2016WR018797.

Smartphone Sensor Data for Mental Health Research

User Engagement, Experience, and Ethics

By [source]

About this dataset

In addition to smartphone sensor data, survey responses were also collected that provide an insight into participants' views on passive data collection for research purposes. This remarkable set of information opens up new possibilities in terms of understanding and treating mental health by leveraging technological advances — while also providing valuable insights into important ethical considerations related to it. Our dataset thus offers researchers a crucial tool for unlocking advances in our understanding of mental health and its associated conditions — paving the way for further exploration through different contexts.$

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How to use the dataset

This dataset contains valuable and comprehensive sensor data collected from smartphones as part of a feasibility study aimed at understanding mental health through the use of smartphone data. This dataset is ideal for researchers and data scientists who are interested in exploring the potential of smartphone sensors for aiding understanding of mental health.

In this guide, we will be discussing how to best utilize this dataset to explore the different aspects associated with a user’s experience with collecting mental health-related data via their device. All these attributes have been organized into columns within the dataset.

The first set of columns ‘os’, ‘model’ and ‘phone_age’ provide us information related to the participants' devices such as its type/make, operating system and age respectively. This can be used to group users who share similar technologies or devices, which can help us better understand how device differences may affect user engagement or experience with collecting this kind of data.

The second set consists of demographics-related details such as participant 'age', 'gender' and 'phone_use' (or frequency). These columns give us insight into who is using what types/makes of devices in order to collect their mental health related data; it may uncover any trends associated with certain demographic segments receiving more benefit from certain types/makes compared to others etc.

The third set pertains more closely towards understanding participant engagement; these include 'time', 'bluetooth_use', 'running_problem' statuses which enable us determine whether participants experienced any issues using Bluetooth while trying to collect their respective datasets; did they feel comfortable enough while doing so? etc It also includes 'data_use' which would tell us how much usage was obtained from each participant on average (in MB). Additionally there are also survey based opinions on acceptability ('settings') describing whether participants felt that automated collection was acceptable or not included alongside battery status ('battery').
All in all by applying a combination analysis approach – examining different attributes separately as well as consulting other sources like survey results – deeper insights around user experience can be discerned via this unique dataset!

Research Ideas

• Analyzing the data to understand user engagement with the app, in order to develop methods of encouraging consistent use of smartphone sensors for mental health research.
• Investigating how battery life and device settings affect user experience with the app, as knowing these factors could help optimize usage in future studies.
• Combining this data with other datasets to build a better understanding of how mental health changes over time and how different activities might affect it – such as looking at changes in communication patterns or phone usage depending on mood levels/symptoms

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: participant_info.csv | Column name | Description | |:--------------|:-----------------------------------------| | os | Operating system of the device. (String) | | model | Model of the ...

Previous mass spectrometry (MS)-based global proteomics studies have detected a combined total of 86% of all Treponema pallidum proteins under infection conditions (in vivo-grown T. pallidum). Recently, a method was developed for the long-term culture of T. pallidum under in vitro conditions (in vitro-cultured T. pallidum). Herein, we used our previously reported optimized MS-based proteomics approach to characterize the T. pallidum global protein expression profile under in vitro culture conditions. These analyses provided a proteome coverage of 94%, which extends the combined T. pallidum proteome coverage from the previously reported 86% to a new combined total of 95%. This study provides a more complete understanding of the protein repertoire of T. pallidum. Further, comparison of the in vitro-expressed proteome with the previously determined in vivo-expressed proteome identifies only a few proteomic changes between the two growth conditions, reinforcing the suitability of in vitro-cultured T. pallidum as an alternative to rabbit-based treponemal growth. The MS proteomics data have been deposited in the MassIVE repository with the data set identifier MSV000093603 (ProteomeXchange identifier PXD047625).

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

This release combines fourteen waves of Understanding Society data with harmonised data from all eighteen waves of the BHPS. As multi-topic studies, the purpose of Understanding Society and BHPS is to understand short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move they are followed within the UK and anyone joining their households are also interviewed as long as they are living with them. The study has five sample components: the general population sample; a boost sample of ethnic minority group members; an immigrant and ethnic minority boost sample (from wave 6); participants from the BHPS; and a second general population boost sample added at this wave. In addition, there is the Understanding Society Innovation Panel (which is a separate standalone survey (see SN 6849)). The fieldwork period is for 24 months. Data collection uses computer assisted personal interviewing (CAPI) and web interviews (from wave 7), and includes a telephone mop-up. From March 2020 (the end of wave 10 and the 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended, and the survey was conducted by web and telephone only, but otherwise has continued as before. Face-to-face interviewing was resumed from April 2022. One person completes the household questionnaire. Each person aged 16 is invited to complete the individual adult interview and self-completed questionnaire. Parents are asked questions about their children under 10 years old. Youths aged 10 to 15 are asked to respond to a self-completion questionnaire. For the general and BHPS samples biomarker, genetic and epigenetic data are also available. The biomarker data, and summary genetics and epigenetic scores, are available via UKDS (see SN 7251); detailed genetics and epigenetics data are available by application (see below). In 2020-21 an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). Participants are asked consent to link their data to wide-ranging administrative data sets (see below).

Further information may be found on the Understanding Society Main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence, Special Licence and Secure Access versions:

There are three versions of the main Understanding Society data with different access conditions. One is available under the standard End User Licence (EUL) agreement (SN 6614), one is a Special Licence (SL) version (this study) and the third is a Secure Access version (SN 6676). The SL version contains month as well as year of birth variables, more detailed country and occupation coding for a number of variables, various income variables that have not been top-coded, and other potentially sensitive variables (see 6931_eul_vs_sl_variable_differences document available with the SL version for full details of the differences). The Secure Access version, in addition to containing all the variables in the SL version, also contains day of birth as well as Grid Reference geographical variables. Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL and Secure Access versions of the data have more restrictive access conditions and prospective users of those versions should visit the catalogue entries for SN 6931 and SN 6676 respectively for further information.

Low- and Medium-level geographical identifiers are also available subject to SL access conditions; see SNs 6666, 6668-6675, 7453-4, 7629-30, 7245, 7248-9 and 9169-9170. Schools data are available subject to SL access conditions in SN 7182. Higher Education establishments for Wave 5 are available subject to SL access conditions in SN 8578. Interviewer Characteristics data, also subject to SL access conditions is available in SN 8579. In addition, a fine detail geographic dataset (SN 6676) is available under more restrictive Secure Access conditions that contains National Grid postcode grid references (at 1m resolution) for the unit postcode of each household surveyed, derived from ONS Postcode Directories (ONSPD). For details on how to make an application for Secure Access dataset, please see the SN 6676 catalogue record.

How to access genetic and/or bio-medical sample data from Understanding Society:

Information on how to access genetics and epigenetics data directly from the study team is available on the Understanding Society Accessing data webpage.

Linked administrative data

Linked Understanding Society / administrative data are available on a number of different platforms. See the Understanding Society Data linkage webpage for details of those currently available and how they can be accessed.

Latest edition information

For the 18th edition (November 2024) Wave 14 data has been added. Other minor changes and corrections have also been made to Waves 1-13. Please refer to the revisions document for full details.

m_hhresp and n_hhresp files updated, December 2024

In the previous release (18th edition, November 2024), there was an issue with household income estimates in m_hhresp and n_hhresp where a household resides in a new local authority (approx. 300 households in wave 14). The issue has been corrected and imputation models re-estimated and imputed values updated for the full sample. Imputed values will therefore change compared to the versions in the original release. The variables affected are w_ficountax_dv, w_fihhmnnet3_dv, n_fihhmnnet4_dv and n_ctband_dv.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain over 2,047 variables.

This comprehensive dataset is a meticulously curated collection of mental health statuses tagged from various statements. The dataset amalgamates raw data from multiple sources, cleaned and compiled to create a robust resource for developing chatbots and performing sentiment analysis.

Data Source:

The dataset integrates information from the following Kaggle datasets:

• 3k Conversations Dataset for Chatbot
• Depression Reddit Cleaned
• Human Stress Prediction
• Predicting Anxiety in Mental Health Data
• Mental Health Dataset Bipolar
• Reddit Mental Health Data
• Students Anxiety and Depression Dataset
• Suicidal Mental Health Dataset
• Suicidal Tweet Detection Dataset

Data Overview:

The dataset consists of statements tagged with one of the following seven mental health statuses: - Normal - Depression - Suicidal - Anxiety - Stress - Bi-Polar - Personality Disorder

Data Collection:

The data is sourced from diverse platforms including social media posts, Reddit posts, Twitter posts, and more. Each entry is tagged with a specific mental health status, making it an invaluable asset for:

• Developing intelligent mental health chatbots.
• Performing in-depth sentiment analysis.
• Research and studies related to mental health trends.

Features:

• unique_id: A unique identifier for each entry.
• Statement: The textual data or post.
• Mental Health Status: The tagged mental health status of the statement.

Usage:

This dataset is ideal for training machine learning models aimed at understanding and predicting mental health conditions based on textual data. It can be used in various applications such as:

• Chatbot development for mental health support.
• Sentiment analysis to gauge mental health trends.
• Academic research on mental health patterns.

Acknowledgments:

This dataset was created by aggregating and cleaning data from various publicly available datasets on Kaggle. Special thanks to the original dataset creators for their contributions.

Identifying Entities (Diseases, Treatments) in Healthcare Data

Finding diseases and treatments in medical text—because even AI needs a medical degree to understand doctor’s notes! 🩺🤖

📊 Understanding the Dataset

In the contemporary healthcare ecosystem, substantial amounts of unstructured textual facts are generated day by day thru electronic health facts (EHRs), medical doctor’s notes, prescriptions, and medical literature. The potential to extract meaningful insights from this records is critical for improving patient care, advancing clinical studies, and optimizing healthcare offerings. The dataset in cognizance incorporates text-based totally scientific statistics, in which sicknesses and their corresponding remedies are embedded inside unstructured sentences.

The dataset consists of categorized textual content samples, that are classified into: -**Train Sentences**: These sentences comprise clinical records, including patient diagnoses and the treatments administered. -**Train Labels**: The corresponding annotations for the train sentences, marking diseases and remedies as named entities. -**Test Sentences**: Similar to educate sentences however used to evaluate model overall performance. -**Test Labels**: The ground reality labels for the test sentences.

A sneak from the dataset may look as follows:

🔍 Example from Dataset:

Train Sentences:

_ "The patient was a 62 -year -old man with squamous epithelium, who was previously treated with success with a combination of radiation therapy and chemotherapy."

Train Labels:

• Disease: 🦠 lung cancer
• Treatment: 💉 Radiation therapy, chemotherapy

This dataset requires the use of** designated Unit Recognition (NER)** to remove and map and map diseases for related treatments 💊, causing the composition of unarmed medical data for analytical purposes.

⚙️ Dataset Properties

1. Unnecessary medical text: Data set contains free-powered medical notes, where disease and treatment conditions are clearly mentioned. Removing this information without clear mapping is a challenge.
2. Many unit types: Datasets contain different - -called institutions such as diseases, treatment, symptoms and possibly medication.
3. Relevant addiction: Many treatments apply to many diseases, and proper mapping depends on reference. For example, "radiotherapy" is used for different cancers, which makes relevant understanding significantly.
4. Unbalanced data distribution: Some diseases and treatment can be displayed more often than others, to balance model performance requires techniques such as overfalling, sub -sampling or transmission of learning.
5. Domain-specific language: is rich in lesson medical terminology, which requires special preprochet using domain-specific NLP techniques and medical oncology such as UML or SNOM CT.

🚧 Challenges Working with Dataset

• Complex medical vocabulary: Medical texts often use vocals, which require special NLP models that are trained at the clinical company.

• Implicit Relationships: Unlike based datasets, ailment-treatment relationships are inferred from context in preference to explicitly stated.

• Synonyms and Abbreviations: Diseases and treatments can be cited the use of special names (e.G., ‘myocardial infarction’ vs. ‘coronary heart assault’). Handling such versions is vital.

• Noise in Data: Unstructured records may additionally contain irrelevant records, typographical errors, and inconsistencies that affect extraction accuracy.

🛠️ Approach to Extracting Insights from the Dataset

To extract sicknesses and their respective treatments from this dataset, we follow a based NLP pipeline:

1. Data Preprocessing 🧹

• Text Cleaning: Remove needless characters, numbers, and stopwords whilst preserving clinical terms.
• Tokenization: Split sentences into phrases for higher processing.
• Medical Term Standardization: Use area-precise libraries like SciSpacy to standardize synonyms and abbreviations.

2. Named Entity Recognition (NER) Model Development 🤖

• Annotation: Ensure accurate labeling of sicknesses and treatments in the dataset.
• Model Selection: Train a deep-mastering-based version like BioBERT or a rule-based model the use of spaCy.
• Training: Use annotated data to teach a custom NER model that classifies words as sickness or treatment entities.
• Evaluation: Measure precision, bear in mind, and F1-score to evaluate version overall performance.

3. Mapping Diseases to Treatments 🔄

• Contextual Relationship Extraction: Identify which treatment corresponds to which sickness using dependency parsing and courting extraction.
• Dictionary or Tabular Output: Store extracted mappings in a based layout.

Example Output:

| 🦠 Disease | 💉 Treatments | |----------|--------------------...

1. Climate change in the Arctic is two to three times faster than anywhere else in the world. It is therefore crucial to understand the effects of weather on keystone arctic species, particularly those such as caribou (Rangifer tarandus) that sustain northern communities. Bridging long-term scientific and indigenous knowledge offers a promising path to achieve this goal, as both types of knowledge may complement one another.
2. We assessed the influence of environmental variables on the spring and fall body condition of caribou from the Porcupine Caribou Herd. This herd ranges in the Yukon and Northwest Territories (Canada) and Alaska (USA), and is the only large North American herd that has not declined since the 2000s. Using observations recorded through an indigenous community-based monitoring program between 2000-2010, we analyzed temporal trends in caribou condition and quantified the effects of weather and critical weather-dependent variables (insect harassment and vegetation growth), on spring (n = 617 individuals) and fall (n =711) caribou condition.
3. Both spring and fall body condition improved from 2000 to 2010, despite a continuous population increase of ca. 3.6% per year. Spring and fall caribou condition were influenced by weather on the winter and spring ranges, particularly snow conditions and spring temperatures. Both snow conditions and spring temperatures improved during our study period, likely contributing to the observed caribou population increase. Insect harassment during the previous summer and the frequency of icing events also influenced caribou condition.
4. Synthesis and applications. Our study first shows how untangling the relative influences of seasonal weather variables allows a much better understanding of variation in seasonal body condition in Rangifer populations. Second, it indicates that a large migratory caribou population can grow and improve body condition in a global context of caribou decline and climate warming, thereby warning against generalizations about the influence of climate on all Rangifer populations. Finally, it testifies how data from indigenous community-based monitoring on a large spatiotemporal scale can remarkably improve ecological understanding of wildlife sustaining human communities. We recommend that management practices promote indigenous community-based ecological monitoring whenever feasible.06-Nov-2019

CPEXCV_Merge_DC8_Data are pre-generated aircraft merge data files created utilizing data collected during the Convective Processes Experiment - Cabo Verde (CPEX-CV) onboard the DC-8 aircraft. Data collection for this product is complete. Seeking to better understand atmospheric processes in regions with little data, the Convective Processes Experiment – Cabo Verde (CPEX-CV) campaign conducted by NASA is a continuation of the CPEX – Aerosols & Winds (CPEX-AW) campaign that took place between August to September 2021. The campaign will take place between 1-30 September 2022 and will operate out of Sal Island, Cabo Verde with the primary goal of investigating atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. CPEX-CV will work towards its goal by addressing four main science objectives. The first goal is to improve understanding of the interaction between large-scale environmental forcings such as the Intertropical Convergence Zone (ITCZ), Saharan Air Layer, African easterly waves, and mid-level African easterly jet, and the lifecycle and properties of convective cloud systems, including tropical cyclone precursors, in the tropical East Atlantic region. Next, observations will be made about how local kinematic and thermodynamic conditions, including the vertical structure and variability of the marine boundary layer, relate to the initiation and lifecycle of convective cloud systems and their processes. Third, CPEX-CV will investigate how dynamical and convective processes affect size dependent Saharan dust vertical structure, long-range Saharan dust transport, and boundary layer exchange pathways. The last objective will be to assess the impact of CPEX-CV observations of atmospheric winds, thermodynamics, clouds, and aerosols on the prediction of tropical Atlantic weather systems and validate and interpret spaceborne remote sensors that provide similar measurements. To achieve these objectives, the NASA DC-8 aircraft will be deployed with remote sensing instruments and dropsondes that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. Instruments onboard the aircraft include the Airborne Third Generation Precipitation Radar (APR-3), lidars such as the Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), Advanced Vertical Atmospheric Profiling System (AVAPS) dropsonde system, Cloud Aerosol and Precipitation Spectrometer (CAPS), and the Airborne In-situ and Radio Occultation (AIRO) instrument. Measurements taken by CPEX-CV will assist in moving science forward from previous CPEX and CPEX-AW missions, the calibration and validation of satellite measurements, and the development of airborne sensors, especially those with potential for satellite deployment.

Matlab is needed in order to open the data files.

FIREXAQ_Merge_Data are pre-generated merge data files collected during FIREX-AQ. These files contain merged data products collected onboard the DC-8 aircraft. Completed during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA’s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite’s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts. Data collection is complete.

Project Description:

Title: Pandas Data Manipulation and File Conversion

Overview: This project aims to demonstrate the basic functionalities of Pandas, a powerful data manipulation library in Python. In this project, we will create a DataFrame, perform some data manipulation operations using Pandas, and then convert the DataFrame into both Excel and CSV formats.

Key Objectives:

1. DataFrame Creation: Utilize Pandas to create a DataFrame with sample data.
2. Data Manipulation: Perform basic data manipulation tasks such as adding columns, filtering data, and performing calculations.
3. File Conversion: Convert the DataFrame into Excel (.xlsx) and CSV (.csv) file formats.

Tools and Libraries Used:

• Python
• Pandas

Project Implementation:

1. DataFrame Creation:

   • Import the Pandas library.
   • Create a DataFrame using either a dictionary, a list of dictionaries, or by reading data from an external source like a CSV file.
   • Populate the DataFrame with sample data representing various data types (e.g., integer, float, string, datetime).

2. Data Manipulation:

   • Add new columns to the DataFrame representing derived data or computations based on existing columns.
   • Filter the DataFrame to include only specific rows based on certain conditions.
   • Perform basic calculations or transformations on the data, such as aggregation functions or arithmetic operations.

3. File Conversion:

   • Utilize Pandas to convert the DataFrame into an Excel (.xlsx) file using the to_excel() function.
   • Convert the DataFrame into a CSV (.csv) file using the to_csv() function.
   • Save the generated files to the local file system for further analysis or sharing.

Expected Outcome:

Upon completion of this project, you will have gained a fundamental understanding of how to work with Pandas DataFrames, perform basic data manipulation tasks, and convert DataFrames into different file formats. This knowledge will be valuable for data analysis, preprocessing, and data export tasks in various data science and analytics projects.

Conclusion:

The Pandas library offers powerful tools for data manipulation and file conversion in Python. By completing this project, you will have acquired essential skills that are widely applicable in the field of data science and analytics. You can further extend this project by exploring more advanced Pandas functionalities or integrating it into larger data processing pipelines.in this data we add number of data and make that data a data frame.and save in single excel file as different sheet name and then convert that excel file in csv file .

The goal of this investigation is to understand the wave-driven circulation and variability of the polar atmosphere through an integrated study that combines satellite measurements, lidar measurements, meteorological analyses, and model simulations will be conducted. This study is an international collaboration between investigators at six institutions in Germany, Japan and the United States. An international network of four Rayleigh lidars located in observatories at Andoya, Norway (69°N, 16°E), Chatanika, Alaska (65°N, 147°W), Kangarlussuaq, Greenland (67°N, 51°W) and Kühlungsborn, Germany (54°N, 12°E) provide a chain of measurements from the eastern Arctic to the western Arctic under distinct synoptic regimes (i.e., the Arctic stratospheric vortex, the Aleutian anticyclone, the stratospheric surf-zone). The lidars will yield high-resolution temperature and density measurements that allow characterization of the planetary waves, tides, and gravity waves. The satellite observations yield synoptic-scale temperature measurements of the mesosphere and upper stratosphere while the meteorological soundings and analyses provide synoptic-scale measurements of the troposphere and lower stratosphere. We have three specific goals; i) to extend the scope of current Rayleigh lidar measurements to characterize a wider range of waves than previously measured ii) to combine lower-resolution global data from satellite observations and meteorological analyses with higher-resolution data from Arctic Rayleigh lidar systems to document both the synoptic conditions and wave activity. iii) to investigate the observed wave behavior using a comprehensive general circulation model to understand the wave mean-flow interactions. The proposed activity will provide a comprehensive analysis of the circulation of the Arctic atmosphere that will directly address the following specific studies; coupling and feedbacks between waves and large-scale circulation; the structure; evolution, and variability of polar vortices and anticyclones; links between the middle and lower atmosphere; and atmospheric tele-connections. The study will advance our understanding of wave mean-flow interactions both regionally and globally that is critical for understanding dynamical driving of the circulation and the evolution of the climate. This study will provide data and analyses in support of studies of ozone depletion, stratospheric climate, and long-range horizontal and vertical transport in the Arctic. The observations, analyses and results of this activity will contribute to the CAWSES, CEDAR, SEARCH, and SPARC programs. This project entails a three-year study of of the circulation of the Arctic atmosphere that combines lidar observations at 4 sites (Andoya, Norway, Chatanika, Alaska (Poker Flat), Kangerlussuaq, Greenland, and Kühlungsborn, Germany) during winter months (September through April each year) with data analysis and modeling. In fall of 2011, the researchers will upgrade the Poker Flat lidar to extend its range and power. For the remainder of the measurement period (into late winter 2013/2014) the lidar observations will be handled by on-site staff.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2023, is designed for analysts to conduct cross-sectional analysis for the 2023 calendar year. The Calendar Year datasets combine data collected in a specific year from across multiple waves and these are released as separate calendar year studies, with appropriate analysis weights, starting with the 2020 Calendar Year dataset. Each subsequent year, an additional yearly study is released.

The Calendar Year data is designed to enable timely cross-sectional analysis of individuals and households in a calendar year. Such analysis can however, only involve variables that are collected in every wave (excluding rotating content which is only collected in some of the waves). Due to overlapping fieldwork the data files combine data collected in the three waves that make up a calendar year. Analysis cannot be restricted to data collected in one wave during a calendar year, as this subset will not be representative of the population. Further details and guidance on this study can be found in the xxxx_main_survey_calendar_year_user_guide_2023.

These calendar year datasets should be used for cross-sectional analysis only. For those interested in longitudinal analyses using Understanding Society please access the main survey datasets: Safeguarded (End User Licence) version or Safeguarded/Special Licence version.

Understanding Society: the UK Household Longitudinal Study, started in 2009 with a general population sample (GPS) of UK residents living in private households of around 26,000 households and an ethnic minority boost sample (EMBS) of 4,000 households. All members of these responding households and their descendants became part of the core sample who were eligible to be interviewed every year. Anyone who joined these households after this initial wave, were also interviewed as long as they lived with these core sample members to provide the household context. At each annual interview, some basic demographic information was collected about every household member, information about the household is collected from one household member, all 16+ year old household members are eligible for adult interviews, 10-15 year old household members are eligible for youth interviews, and some information is collected about 0-9 year olds from their parents or guardians. Since 1991 until 2008/9 a similar survey, the British Household Panel Survey (BHPS), was fielded. The surviving members of this survey sample were incorporated into Understanding Society in 2010. In 2015, an immigrant and ethnic minority boost sample (IEMBS) of around 2,500 households was added. In 2022 a GPS boost sample (GPS2) of around 5,700 households was added. To know more about the sample design, following rules, interview modes, incentives, consent, questionnaire content please see the study overview and user guide.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2023 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see document '9471_eul_vs_sl_variable_differences' for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (Safeguarded (EUL)) and 6931 (Safeguarded/SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2023 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,800 variables.

Freshwater algae exhibit complex dynamics, particularly in meso-oligotrophic lakes with sudden and dramatic increases in algal biomass following long periods of low background concentration. While the fundamental prerequisites for algal blooms, namely light and nutrients, are well-known, their specific causation involves an intricate chain of conditions. Here we examine a recent massive Uroglena bloom in Lake Geneva (Switzerland/France). We show that a certain sequence of meteorological conditions triggered this specific algal bloom event: heavy rainfall promoting excessive organic matter and nutrients loading, followed by wind-induced coastal upwelling, and a prolonged period of warm, calm weather. The combination of satellite remote sensing, in-situ measurements, ad-hoc biogeochemical analyses, and three-dimensional modeling proved invaluable in unraveling the complex dynamics of algal blooms highlighting the substantial role of littoral-pelagic connectivities in large low-nutrient lakes. These findings underscore the advantages of state-of-the-art multidisciplinary approaches for an improved understanding of dynamic systems as a whole.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of coronary heart disease (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to coronary heart disease (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with coronary heart disease was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with coronary heart disease was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with coronary heart disease, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have coronary heart diseaseB) the NUMBER of people within that MSOA who are estimated to have coronary heart diseaseAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have coronary heart disease, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from coronary heart disease, and where those people make up a large percentage of the population, indicating there is a real issue with coronary heart disease within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of coronary heart disease, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of coronary heart disease.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Metadata record for data from ASAC Project 2794 See the link below for public details on this project.

Public: This study will use innovative technology to measure the winter spatial foraging patterns and net energy gain of adult female elephant seals (and potentially Weddell seals), while simultaneously providing high-resolution data on the physical nature of the water column in which the seals live. By combining biological and physical data with satellite derived sea-ice information, this study will improve our understanding of predator foraging success (and therefore mechanisms which regulate population trajectories) and provide physical oceanographers with fundamental data on the importance mechanisms that determine the winter ice and bottom water formation that under-pin the Antarctic marine ecosystem.

Project objectives: The extent and nature of Antarctic winter sea ice is thought to have profound impacts on biological productivity, the recruitment of Antarctic krill, and the flow-on effects through the Antarctic marine food web. 1. Winter sea-ice formation is also hypothesised to play an important, yet highly-variable role in ocean circulation patterns through the production of cold, dense winter bottom water. 2. The mechanisms determining the inter-annual variation in winter ice formation are poorly understood, as are the complex feedback processes involved, but they are nonetheless recognised as being vulnerable to human-induced climate change. 3. Given the dynamically-linked nature of winter-ice and biological productivity, long-term climatic changes will have broad scale influences on Antarctic biota.

This study will use innovative technological developments to quantify the response of one of the major Antarctic marine predators, the southern elephant seal (Mirounga leonina), to inter-annual variation in winter ice conditions. We will measure the winter spatial foraging patterns and net energy gain of adult female elephant seals while simultaneously providing high-resolution data on the physical nature of the water column in which the seals are living. The combination of these biological and physical data with satellite-derived sea-ice information will relate variation in the winter-ice to broad scale biological production through the foraging success (maternal investment and therefore demographic performance) of a top Antarctic marine predator, as well as providing physical oceanographers with fundamental data on the important mechanisms that determine the winter ice and bottom water formation that under-pin the Antarctic marine ecosystem. The specific objectives are to:

1. Measure the foraging performance of the seals in terms of spatially-specific net energy gain while at sea, in relation to intra- and inter-annual variation in sea-ice and oceanic processes.
2. Use newly-developed (and tested) animal-borne satellite-linked Conductivity-Temperature-Depth Satellite Relay Data Loggers (CTD-SRDLs) to provide oceanographic quality data on local physical characteristics (temperature and salinity).
3. Record fine-scale foraging parameters (dive depth, duration, swimming speed) using "Dead-Reckoning" Data Loggers (DRDLs) and feeding events using Stomach Temperature Sensors (STSs).
4. Integrate these data collected in years and regions of different winter ice extent and conditions.
5. Assess diet during the winter months using stable isotope and fatty acid signature analysis.
6. Combine the biological and physical information to refine current models of predator performance based on annual climatic features. These models will be used to examine a range of climate-change scenarios, initially for elephant seals but with a view to broadening the species application at a later stage.

Taken from the 2008-2009 Progress Report: Progress against objectives: Due to logistic constraints, no satellite telemetry was conducted at Casey or Macquarie Island this year, but preliminary surveys of the region were conducted for both elephant and Weddell seals (see report for 2753). However we did deploy CTD satellite tags on elephant seals at Isles Kerguelen and Elephant Island to contribute to the IPY MEOP program. These animals either traversed the Southern Ocean to forage over the Antarctic continental shelf, or remained very close to their breeding island, indicating that even within a population there are markedly different foraging strategies.

Taken from the 2010-2011 Progress Report: Public summary of the season progress: Due to pre-departure accident for one of the field team leaders we were unable to reach Casey this year to complete that component of the program. Forty CTD satellite tags were successfully deployed at Vestfold Hills in January and February 2011. These tags are currently still transmitting from foraging locations along the Antarctic continental shelf and the ice edge.

Project 2695 (ASAC_2695) was incorporated into this project.

An Access database containing data from this project is available for download at the provided URL.

The data have also been loaded into the Australian Antarctic Data Centre's ARGOS tracking database. The database can be accessed at the provided URLs.

Metadata record for data from ASAC Project 2668 See the link below for public details on this project.

The dataset contains data in the following formats:

The *.met files contain the height, time, direction and range of a meteor detection.

The *.vel file contains meteor determined wind velocities: the horizontal and vertical velocities.

There are other ancillary parameters in each file but these are the main ones.

The parameters are described in the pdf document included in the dataset. We have been able to get IDL based reading routines from the radar company (ATRAD) but in general, one is expected to write ones own software for reading the datasets.

Public The gap in our knowledge of the mesosphere and lower thermosphere (MLT) has stemmed from a difficulty in probing this remote region of our atmosphere. Spanning the height range between 50 and 110 km, the MLT is sometimes jokingly termed the 'ignorosphere'. However, observations from sites in Antarctica can now be combined with satellite data to overcome the limitations of our observing techniques. This project seeks to learn more about the many processes that contribute to the character of this region, with the goal of enhancing our understanding of the earth's atmosphere and identifying the effects of global climate change.

Project objectives: This project aims to provide a point of focus within the Australian Antarctic Program for investigations of the polar mesosphere and lower thermosphere (MLT) using satellite observations. Ground-based measurements typically have excellent vertical and temporal resolution, but are limited in their horizontal coverage. Satellite observations, on the other hand, provide a global perspective that cannot be achieved with ground-based instruments. Our knowledge of the polar MLT and its role in the global climate system can be significantly enhanced through studies that combine ground-based and satellite based measurements.

The importance of ground-based measurements of the structure and dynamics of the polar MLT is underlined by the Australian Antarctic Program's support of the unique combination of experiments operated at Davis station. An MF (medium frequency) radar measures horizontal wind speeds in this region every few minutes. A VHF (very high frequency) radar, LIDAR (laser radar) and a spectrometer provide other wind and temperature measurements when conditions allow. And all of these instruments yield data with a temporal and altitude resolution that cannot be achieved using a satellite.

Satellite observations of the MLT have, until recently, neglected the polar regions. The Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) mission, whose primary goal is to investigate and understand the basic structure, variation, and energy balance of the MLT region and the Ionosphere [Yee, 2003], sought to redress this neglect. Since its launch in December 2001, the TIMED satellite has made observations that extend well into the polar regions and include the latitude of Davis

Significantly, the instigators of TIMED recognised the contribution that ground-based experiments will make to its scientific yield by explicitly including them in the mission. A group of Ground Based Investigators (GBIs) have been funded to facilitate the incorporation of ground-based data sets into TIMED activities. The Davis MF radar is one of the instruments to be included in the TIMED mission through this mechanism.

It is therefore timely to focus some of our research activity on the opportunities provided by satellites such as TIMED. The availability of polar satellite data extends the reach of our existing ground-based experiments and adds value to our scientific endeavours. As a result, the common goals of the TIMED mission and the Australian Antarctic Science Program are achieved, our understanding of the role of Antarctica in the global climate system is enhanced and our international scientific profile is increased.

A document providing further details about the history of the project is available for download at the provided URL.

Taken from the 2009-2010 Progress Report: Progress against objectives: -Adding value to satellite data and ground-based data: As a result of the Fulbright sponsored visit of co-investigator Palo in late 2008, it is now clear that, due to differences in the characteristics of space- and ground-based data, the design of techniques for combining data sets should be specific to the wave class being considered (principally planetary waves and tides).

Significant contributions to the Aeronomy of Ice in the Mesosphere (AIM) satellite mission have been made using the tidal observations and analysis that form part of project 674. In the context of the current project, progress has been made in the following areas.

The 2007/2008 season of southern hemisphere observations has become a focus because both the AIM satellite instruments and the Antarctic MF radars operated well for much of that time. The Cloud Imaging and Particle Size (CIPS) instrument on AIM has now been used extensively to image and map the occurrence of Polar Mesospheric Clouds (PMC) and to identify gravity wave signatures within these clouds The position and time of the centre pixel of each usable CIPS image in the 2007/2008 season forms the basis of a number of our studies. These locations and times are combined with a representation of the tidal wind field that can be calculated for the mesosphere and lower thermosphere south of about 60 degrees. Values of the tides at the time of the CIPS samples provide a measure of the wind variations due to the tides (but not the mean winds of planetary waves) throughout the season.

This extensive tidal data base is being used to consider the temporal and seasonal variability of PMC occurrence. Satellite up-leg and down-leg observations show systematic differences that are yet to be explained. A proxy for the temperature history of air parcels sampled by the satellite that considers the tidal perturbations due to the zonally symmetric tides (diurnal and semidiurnal) has been proposed. Knowledge of the spatial and temporal variation of the wind field obtained from the tides is then used to trace the air parcel position back in time by 3 or 6 hours (estimates of the time taken to form a PMC) and to assess the extent of the upwelling and thus temperature influence on the observed air parcel. Similarities to the PMC occurrence are apparent and are being further investigated.

Tides are a possible modulator of gravity wave activity in the polar mesosphere so the role they might play in distorting the observed distribution of gravity waves is being explored. The distribution of the winds in the tidal wind field sampled by the CIPS instrument (whose sampling scheme is determined by the orbit period and satellite precession rate) has been compared to the actual distribution (derivable from the tidal winds by applying a regular sampling regime). Although the potential for bias is present, the range of heights below the cloud layer in which the tides have had significant amplitude is only a few kilometres so it is currently thought the bias will not be great. Comparisons of the distributions of the zonally and meridionally propagating gravity waves are to be made by our colleagues to consider this question further.

The potential for the AIM sampling scheme to 'alias' tidal variations into the planet-scale maps of ice occurrence has been considered. Regularly sampled tidal winds and those sampled by a CIPS sampling scheme have been analysed for their spatial and temporal variations and comparisons made to see if aliasing is occurring. However, this study is yet to be extended to the entire season. At this stage, only wind effects have been included. Improvements to a model that calculates the tidal temperature response is required and a strategy for making those improvements has been identified but has not been programmed into software.

In addition to the AIM satellite studies, some more general areas of investigation have been pursued (albeit at a low level of activity).

A technique whereby the theoretical structure of atmospheric tides (described using Hough modes) is extended to include the characteristics of a real atmosphere (Hough mode extensions or HMEs) has been proposed for combining data sets and is being explored. Discussions with our colleagues from NCAR (USA) and Clemson University (USA) (who generate the HMEs) have identified some concerns about the quality of the representation of tidal dissipation and the effect this has on the HMEs. We await further advice on this.

A technique whereby planetary-wave heat fluxes can be calculated using space-based temperatures and ground-based winds has been designed and is to be tested using the results of a previous ground-based only study. The long period (multiple days) and large scale of these waves, along with the ability to remove the mean temperature by decomposition, (and therefore any instrumental biases) make this study possible given the practical difficulties noted elsewhere. The software required for the extraction of the necessary data from the TIMED/SABER instrument data base at the University of Colorado is being developed in conjunction with colleagues there.

An explanation for a climatological dip in ground-based measurements of temperature at 87 km above Davis was proposed after TIMED/SABER satellite observations of large scale structures showed the presence of slowly moving wavenumber one features at the time of the dip. The outline of a manuscript on this subject has been drafted but the software required for some of the diagrams of the paper using University of Colorado computers is still being developed (see above). On completion, the proposed explanation will be tested against a more extensive data base.

These data represent 33MHz data. For 55MHz data from the meteor radar, see the related metadata record at the

FIREXAQ_MetNav_AircraftInSitu_ER2_Data_1 are meteorological and navigational data collected onboard the Earth Resources-2 (ER-2) aircraft during the Fire Influence on Regional to Global Environments Experiment - Air Quality (FIREX-AQ) Campaign. Completed during summer 2019, FIREX-AQ used a combination of instrumented airplanes, satellites, and ground-based instrumentation. Specifically, data was collected by the NASA Airborne Science Data Telemetry (NASDAT) System on the ER-2 platform. Data collection for this product is complete. Completed during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA’s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. The FIREX-AQ campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite’s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.

Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and parcels nationally.

Over 60M parcels reflecting over 330M permits over the past 20 years.

This comprehensive dataset contains building permits issued in the United States, providing valuable insights into residential and commercial construction activities. With over millions of records covering millions of homes, this dataset offers a vast opportunity for analysis and business growth.

Includes permits from various states across the US

Covers residential and commercial construction activities

Insights:

Residential vs. Commercial: Analyze the distribution of permits by type (residential, commercial) to understand local market trends.

Construction Activity: Track permit issuance over time to identify patterns and fluctuations in construction activity.

Geographic Patterns: Examine the concentration of permits by state, county, or city to reveal regional development opportunities.

Potential Applications:

Contractors and Builders: Utilize this dataset to identify potential projects, estimate job values, and stay up-to-date on permit requirements.

Local Governments: Analyze building permit data to inform land-use planning, zoning regulations, and infrastructure development.

Investors and Developers: Explore the types of construction projects being undertaken in specific areas, enabling informed investment decisions.

Value Propositions:

Understand Current Home Condition: Gain insights into the current state of homes by analyzing building permit data, allowing you to:

Identify areas with high concentrations of permits

Determine the scope and type of work being performed

Infer the potential for improved home values

Lender Lead Generation: Use this dataset to identify potential refinance candidates based on improved homes, enabling lenders to:

Target homeowners who have invested in their properties

Offer tailored financial solutions to capitalize on increased property value

Contractor Lead Generation:

Solar installers can target neighbors of solar customers, increasing the chances of successful referrals and upselling opportunities.

Pool cleaners can target new pools, identifying potential customers for maintenance and cleaning services.

Roofing contractors can target homes with recent roofing permits, offering replacement or repair services to homeowners.

Home Service Providers:

Handyman services can target homes with permit records, offering a range of maintenance and repair services.

Appliance installers can target new kitchens and bathrooms, identifying potential customers for appliance installation and integration.

Real Estate Professionals:

Realtors can analyze permit data to understand local market trends, adjusting their sales strategies to capitalize on areas with high construction activity.

Property managers can identify potential investment opportunities, using permit data to evaluate the feasibility of investment projects.

Data Analysis Ideas:

Trend Analysis: Identify trends in permit issuance by type (residential, commercial), project size, or location to forecast future demand.

Geospatial Analysis: Visualize permit data on a map to analyze the concentration of construction activity and identify areas with high growth potential.

Correlation Analysis: Examine the relationship between permit issuance and local economic indicators (e.g., GDP, unemployment rates) to understand the impact of construction on the local economy.

Business Use Cases:

Market Research: Analyze permit data to inform business decisions about market trends, competition, and growth opportunities.

Risk Assessment: Identify areas with high concentrations of permits and potential risks (e.g., building code non-compliance) to adjust business strategies accordingly.

Investment Analysis: Use permit data to evaluate the feasibility of investment projects in specific regions or markets.

Data Visualization Ideas:

Interactive Maps: Create interactive maps to visualize permit concentration by location, type, and project size.

Permit Issuance Charts: Plot permit issuance over time to illustrate trends and fluctuations in construction activity.

Bar Charts by Category: Display the distribution of permits by category (e.g., residential, commercial) to highlight market trends.

Additional Ideas:

Combine with other datasets: Integrate building permit data with other sources (e.g., crime statistics, weather patterns) to gain a more comprehensive understanding of local conditions.

Analyze by demographic factors: Examine how permit issuance varies across different demographics (e.g., age, income level) to understand market preferences and behaviors.

Develop predictive models: Create statistical models to forecast future permit issuance based on historical trends and external factors.

Project and Permit...

Arctic sea ice is undergoing significant and accelerating change, which has been observed in increasing detail over the past several decades. Stakeholders in the Arctic and beyond are impacted by the cascading effects that these changes have on resource accessibility, ecosystem health, and earth?s physical climate system. Planning mitigation and adaptation strategies requires improvements in the ability to predict the arctic climate system. Confounding predictive model development are large gaps that remain in the understanding of the role albedo feedbacks are playing in sea ice loss. This project will improve the understanding the impacts of solar energy absorption and partitioning on ice mass balance by tracking Arctic Observing Network (AON) sea ice sites as they move through space and time, using the rich datasets being collected at the sites as case studies. The approach integrates AON data with measurements from prior field campaigns, atmospheric reanalysis products, and high resolution remote sensing data using a 3D resolved sea ice-ocean mixed layer coupled model. The objectives are to quantify the deposition of solar energy within a 10x10km study domain around the sites with meter-scale resolution, identify the fate of that solar energy over time (e.g. ocean storage vs. ice bottom melt vs. lateral melt), and improve the ability of our resolved scale model to represent the processes controlling solar absorption and fate. The model explicitly represents critical ice and ocean processes such as melt pond formation, brine drainage, freshwater balance, and upper ocean stratification, providing a tool for data integration that can account for all first order processes impacting radiative transfer and heat storage. Important gaps in initialization datasets will be addressed by an ensemble modeling approach. Iterative comparison of site observations with model states will inform the selection of poorly constrained initial conditions, evaluate system sensitivity, and allow testing of improved model parameterizations for processes such as pond evolution.