Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Abstract

\[x\[This dataset was superseded by GIP AssetList Database v1.3 20150212

GUID: e0a8bc96-e97b-44d4-858e-abbb06ddd87f

on 12/2/2015\]x\]

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains the spatial and non-spatial (attribute) components of the Gippsland bioregion Asset List as two .mdb files, which are readable as an MS Access database or as an ESRI Personal Geodatabase.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Gippsland are included in the zip file as part of this dataset.

Elements are initially included in the preliminary assets database if they are partly or wholly within the bioregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Gippsland bioregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed.

Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "AssetList_database_GIP_v1p2_20150130.doc", located in the zip file as part of this dataset.

The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Detailed information describing the database structure and content can be found in the document "AssetList_database_GIP_v1p2_20150130.doc" located in the zip file.

Some of the source data used in the compilation of this dataset is restricted.

Purpose

\[x\[\\\\\THIS IS NOT THE CURRENT ASSET LIST\\\\\

This dataset was superseded by GIP AssetList Database v1.3 20150212

GUID: e0a8bc96-e97b-44d4-858e-abbb06ddd87f

on 12/2/2015

THIS DATASET IS NOT TO BE PUBLISHED IN ITS CURRENT FORM\]x\]

Dataset History

This dataset is an update of the previous version of the Gippsland asset list database: "Gippsland Asset List V1 20141210"; ID: 112883f7-1440-4912-8fc3-1daf63e802cb, which was updated with the inclusion of a number of additional datasets from the Victorian Department of the Environment and Primary Industries as identified in the "linkages" section and below.

Victorian Farm Dam Boundaries

https://data.bioregionalassessments.gov.au/datastore/dataset/311a47f9-206d-4601-aa7d-6739cfc06d61

Flood Extent 100 year extent West Gippsland Catchment Management Authority GIP v140701

https://data.bioregionalassessments.gov.au/dataset/2ff06a4f-fdd5-4a34-b29a-a49416e94f15

Irrigation District Department of Environment and Primary Industries GIP

https://data.bioregionalassessments.gov.au/datastore/dataset/880d9042-abe7-4669-be3a-e0fbe096b66a

Landscape priority areas (West)

West Gippsland Regional Catchment Strategy Landscape Priorities WGCMA GIP 201205

https://data.bioregionalassessments.gov.au/datastore/dataset/6c8c0a81-ba76-4a8a-b11a-1c943e744f00

Plantation Forests Public Land Management(PLM25) DEPI GIP 201410

https://data.bioregionalassessments.gov.au/datastore/dataset/495d0e4e-e8cd-4051-9623-98c03a4ecded

and additional data identifying "Vulnerable" species from the datasets:

Victorian Biodiversity Atlas flora - 1 minute grid summary

https://data.bioregionalassessments.gov.au/datastore/dataset/d40ac83b-f260-4c0b-841d-b639534a7b63

Victorian Biodiversity Atlas fauna - 1 minute grid summary

https://data.bioregionalassessments.gov.au/datastore/dataset/516f9eb1-ea59-46f7-84b1-90a113d6633d

A number of restricted datasets were used to compile this database. These are listed in the accompanying documentation and below:

• The Collaborative Australian Protected Areas Database (CAPAD) 2010

• Environmental Assets Database (Commonwealth Environmental Water Holder)

• Key Environmental Assets of the Murray-Darling Basin

• Communities of National Environmental Significance Database

• Species of National Environmental Significance

• Ramsar Wetlands of Australia 2011

Dataset Citation

Bioregional Assessment Programme (2015) GIP AssetList Database v1.2 20150130. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/6f34129d-50a3-48f7-996c-7a6c9fa8a76a.

Dataset Ancestors

• Derived From Flood Extent 100 year extent West Gippsland Catchment Management Authority GIP v140701

• Derived From Surface Water Economic Entitlements GIP 20141219

• Derived From West Gippsland Regional Catchment Strategy Landscape Priorities WGCMA GIP 20121205

• Derived From Irrigation District Department of Environment and Primary Industries GIP

• Derived From Surface Water and Groundwater Entitlement Data with Volumes - DEPI Regs Cat6 Victoria 20141218

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Victorian Water Supply Protection Areas

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Southern Rural Water SW Locations with BOM Regulations Category 6 Volumes Gippsland 20150430

• Derived From Gippsland Project boundary

• Derived From Victorian Groundwater Management Areas

• Derived From Plantation Forests Public Land Management(PLM25) DEPI GIP 201410

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Surface Water Entitlement Locations Gippsland Southern Rural Water 20141218

• Derived From Ramsar Wetlands of Australia

• Derived From National Groundwater Information System Victorian Extract (2014-03-21)

• Derived From GEODATA TOPO 250K Series 3

• Derived From Groundwater Licences Entitlement Volume To Bores Vic DEPI 20141021

• Derived From Groundwater Economic Elements Gippsland 20141120

• Derived From Commonwealth Heritage List Spatial Database (CHL)

• Derived From Potential Groundwater Dependent Ecosystems for West Gippsland Catchment Management Authority

• Derived From Victorian Biodiversity Atlas flora - 1 minute grid summary

• Derived From Unreg surface water licences DELWP Gippsland 20150301

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Gippsland Basin bioregion Asset List v01 - 20141210

• Derived From Victorian Farm Dam Boundaries

• Derived From Gippsland Basin bioregion Preliminary Assessment Extent (PAE)

• Derived From [Victoria Regional CMA - Water Asset Information Tool - WAIT

About this dataset

Context

The dataset tabulates the Tuscaloosa population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Tuscaloosa across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Tuscaloosa was 110,602, a 1.39% increase year-by-year from 2021. Previously, in 2021, Tuscaloosa population was 109,082, an increase of 4.67% compared to a population of 104,214 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Tuscaloosa increased by 31,687. In this period, the peak population was 110,602 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Tuscaloosa is shown in this column.
• Year on Year Change: This column displays the change in Tuscaloosa population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Tuscaloosa Population by Year. You can refer the same here

This dataset includes an aggregated and event-correlated analysis of power outages in the United States, synthesized by integrating three data sources: the Environment for the Analysis of Geo-Located Energy Information (EAGLE-I), the Electric Emergency Incident Disturbance Report (DOE-417), and Annual Estimates of the Resident Population for Counties 2024 (CO-EST2024-POP). The EAGLE-I dataset, spanning from 2014 to 2023, encompasses over 146 million customers and offers county-level outage information at 15-minute intervals. The data has been processed, filtered, and aggregated to deliver an enhanced perspective on power outages, which are then correlated with DOE-417 data based on geographic location as well as the start and end times of events. For each major disturbance documented in DOE-417, essential metrics are defined to quantify the outages associated with the event. This dataset supports researchers in examining outages triggered by major disturbances like extreme weather and physical disruptions, thereby aiding studies on power system resilience.

Links to the raw data for generating the correlated dataset are included below as "DOE-417", "EAGLE-I", and "CO-EST2024-POP" resources.

Acknowledgement: This work is funded by the Laboratory Directed Research and Development (LDRD) at the Pacific Northwest National Laboratory (PNNL) as part of the Resilience Through Data-Driven, Intelligently Designed Control (RD2C) Initiative.

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

Project Overview For a robot to repair its own error, it must first know it has made a mistake. One way that people detect errors is from the implicit reactions from bystanders – their confusion, smirks, or giggles clue us in that something unexpected occurred. To enable robots to detect and act on bystander responses to task failures, we developed a novel method to elicit bystander responses to human and robot errors. Data Overview This project introduces the Bystander Affect Detection (BAD) dataset – a dataset of videos of bystander reactions to videos of failures. This dataset includes 2,452 human reactions to failure, collected in contexts that approximate “in-the-wild” data collection – including natural variances in webcam quality, lighting, and background. The BAD dataset may be requested for use in related research projects. As the dataset contains facial video data of participants, access can be requested along with the presentation of a research protocol and data use agreement that protects participants. Data Collection Overview and Access Conditions Using 46 different stimulus videos featuring a variety of human and machine task failures, we collected a total of 2,452 webcam videos of human reactions from 54 participants. Recruitment happened through the online behavioral research platform Prolific (https://www.prolific.co/about), where the options were selected to recruit a gender-balanced sample across all countries available. Participants had to use a laptop or desktop. Compensation was set at the Prolific rate of $12/hr, which came down to about $8 per participant for about 40 minutes of participation. Participants agreed that their data can be shared for future research projects and the data were approved to be shared publicly by IRB review. However, considering the fact that this is a machine-learning dataset containing identifiable crowdsourced human subjects data, the research team has decided that potential secondary users of the data must meet the following criteria for the access request to be granted: 1. Agreement to three usage terms: - I will not redistribute the contents of the BAD Dataset - I will not use videos for purposes outside of human interaction research (broadly defined as any project that aims to study or develop improvements to human interactions with technology to result in a better user experience) - I will not use the videos to identify, defame, or otherwise negatively impact the health, welfare, employment or reputation of human participants 2. A description of what you want to use the BAD dataset for, indicating any applicable human subjects protection measures that are in place. (For instance, "Me and my fellow researchers at University of X, lab of Y, will use the BAD dataset to train a model to detect when our Nao robot interrupts people at awkward times. The PI is Professor Z. Our protocol was approved under IRB #.") 3. A copy of the IRB record or ethics approval document, confirming the research protocol and institutional approval. Data Analysis To test the viability of the collected data, we used the Bystander Reaction Dataset as input to a deep-learning model, BADNet, to predict failure occurrence. We tested different data labeling methods and learned how they affect model performance, achieving precisions above 90%. Shared Data Organization This data project consists of 54 zipped folders of recorded video data organized by participant, totaling 2,452 videos. The accompanying documentation includes a file containing the text of the consent form used for the research project, an inventory of the stimulus videos used, aggregate survey data, this data narrative, and an administrative readme file. Special Notes The data were approved to be shared publicly by IRB review. However, considering the fact that this is a machine-learning dataset containing identifiable crowdsourced human subjects data, the research team has decided that potential secondary users of the data must meet specific criteria before they qualify for access. Please consult the Terms tab below for more details and follow the instructions there if interested in requesting access.

Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.

This data was collected using a Hamby rig as a high-throughput proximal plant phenotyping platform.

The Hamby 6000 rig
Ellis W. Chenault, & Allen F. Wiese. (1989). Construction of a High-Clearance Plot Sprayer. Weed Technology, 3(4), 659–662. http://www.jstor.org/stable/3987560

Dr. Bronson modified an old high-clearance Hamby 6000 rig, adding a tank and pump with a rear boom, to perform precision liquid N applications. A Raven control unit with GPS supplied variable rate delivery options.

The 12 volt Holland Scientific GeoScoutX data recorder and associated CropCircle ACS-470 sensors with GPS signal, was easy to mount and run on the vehicle as an attached rugged data acquisition module, and allowed the measuring of plants using custom proximal active optical reflectance sensing. The HS data logger was positioned near the operator, and sensors were positioned in front of the rig, on forward protruding armature attached to a hydraulic front boom assembly, facing downward in nadir view 1 m above the average canopy height. A 34-size class AGM battery sat under the operator and provided the data system electrical power supply.

Data suffered reduced input from Conley. Although every effort was afforded to capture adequate quality across all metrics, experiment exterior considerations were such that canopy temperature data is absent, and canopy height is weak due to technical underperformance. Thankfully, reflectance data quality was maintained or improved through the implementation of new hardware by Bronson.

Experimental design and operational details of research conducted are contained in related published articles, however a further description of the measured data signals and commentary is herein offered.

The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 reflectance numbers. Which as derived here, consist of raw active optical band-pass values, digitized onboard the sensor product. Data is delivered as sequential serialized text output including the associated GPS information. Typically, this is a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. We used this optical reflectance sensor technology to investigate plant agronomic biology, as the ACS-470 is a unique performance product being not only rugged and reliable but illumination active and filter customizable.

Individualized ACS-470 sensor detector behavior and subsequent index calculation influence can be understood through analysis of white-panel and other known target measurements. When a sensor is held 120 cm from and flush facing a titanium dioxide white painted panel, a normalized unity value of 1.0 can be set for each detector. To generate this dataset, we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalize) on each sensor individually, before each data collection, and without using any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.

Noting that this type of active proximal multi-spectral reflectance data may be perceived as inherently “noisy”; however basic analytical description consistently resolves a biological patterning, and more advanced statistical analysis is suggested to achieve discovery. Sources of polychromatic reflectance are inherent in the environment; and can be influenced by surface features like wax or water, or presence of crystal mineralization; varying bi-directional reflectance in the proximal space is a model reality and directed energy emission reflection sampling is expected to support physical understanding of the underling passive environmental system.

Soil in view of the sensor does decrease the raw detection amplitude of the target color returned and can add a soil reflection signal component. Yet that return accurately represents a largely two-dimensional cover and intensity signal of the target material present within each view. It does not represent a reflection of the plant material solely, because it can contain additional features in view. Expect NDVI values greater than 0.1 when sensing plants and saturating more around 0.8, rather than the typical 0.9 of passive NDVI.

The active signal does not transmit energy to penetrate, perhaps past LAI 2.1 or less, compared to what a solar induced passive reflectance sensor would encounter. However, the focus of our active sensor scan is on the uppermost expanded canopy leaves, and they are positioned to intercept the major solar energy. Active energy sensors are easier to direct, and in our capture method we target a consistent sensor height that is 1 m above the average canopy height, and maintaining a rig travel speed target around 1.5 mph, with sensors parallel to earth ground in a nadir view.

We consider these CropCircle raw detector returns to be more “instant” in generation, and “less-filtered” electronically, while onboard the “black-box” device, than are other reflectance products which produce vegetation indices as averages of multiple detector samples in time.

It is known through internal sensor performance tracking across our entire location inventory, that sensor body temperature change affects sensor raw detector returns in minor and undescribed yet apparently consistent ways.

Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors, that were measured on the GeoScout digital propriety serial data logger, have a stable output format as defined by firmware version.

Different numbers of csv data files were generated based on field operations, and there were a few short duration instances where GPS signal was lost, multiple raw data files when present, including white panel measurements before or after field collections, were combined into one file, with the inclusion of the null value placeholder -9999. Two CropCircle sensors, numbered 2 and 3, were used supplying data in a lined format, where variables are repeated for each sensor, creating a discrete data row for each individual sensor measurement instance.

We offer five high-throughput single pixel spectral colors, recorded at 530, 550, 590, 670, 730, and 800nm (NIR). The filtered band-pass was 10nm, except for the NIR, which was set to 20 and supplied an increased signal (including increased noise). Importantly, two green frequencies are available in this study, which is different from the alternate focus on the other side of the spectrum in the first two Bronson Files datasets measuring cotton.

Dual, or tandem, CropCircle sensor paired usage empowers additional vegetation index calculations such as:
DATT = (r800-r730)/(r800-r670)
DATTA = (r800-r730)/(r800-r590)
MTCI = (r800-r730)/(r730-r670)
CIRE = (r800/r730)-1
CI = (r800/r590)-1
CCCI = NDRE/NDVIR800
PRI = (r590-r530)/(r590+r530)
CI800 = ((r800/r590)-1)
CI780 = ((r780/r590)-1)

On collection 7/28/2014 and thereafter, a new HS logger, the GeoScoutX, or GSX, was initiated. The upgraded data recorder increased operational reliability by eliminating recording stops and subsequent multiple data files. The new data outputs were defined by the operating system configuration version where the data variables column headers were changed to be named SF00, SF01, SF02, SF03 and SF04. The raw reflectance columns are the first three, SF00-02, and the last two columns are the onboard calculated VIs, which we did not consider.

The Campbell Scientific (CS) environmental data recording of small range (0 to 5 v) voltage sensor signals are accurate and largely shielded from electronic thermal induced influence, or other such factors by design. They were used as was descriptively recommended by the company. A high precision clock timing, and a recorded confluence of custom metrics, allow the Campbell Scientific raw data signal acquisitions a high research value generally, and have delivered baseline metrics in our plant phenotyping program. Raw electrical sensor signal captures were recorded at the maximum digital resolution, and could be re-processed in whole, while the subsequent onboard calculated metrics were often data typed at a lower memory precision and served our research analysis.

Campbell Scientific logger derived data output is structured in a column format, with multiple sensor data values present in each data row. One data row represents one program output cycle recording across the sensing array, as there was no onboard logger data averaging or down sampling. Campbell Scientific data is first recorded in binary format onboard the data logger, and then upon data retrieval, converted to ASCII text via the PC based LoggerNet CardConvert application. Here, our full CS raw data output, that includes a four-line header structure, was truncated to a typical single row header of variable names. The -9999 placeholder value was inserted for null instances.

This second data component, expanding measurement using Campbell Scientific records and additional sensors was added during the season. However unfortunate, the CS data of this dataset is of poor quality. The IRT sensors that were

Abstract (en): The American College Catalog Study Database (CCS) contains academic data on 286 four-year colleges and universities in the United States. CCS is one of two databases produced by the Colleges and Universities 2000 project based at the University of California-Riverside. The CCS database comprises a sampled subset of institutions from the related Institutional Data Archive (IDA) on American Higher Education (ICPSR 34874). Coding for CCS was based on college catalogs obtained from College Source, Inc. The data are organized in a panel design, with measurements taken at five-year intervals: academic years 1975-76, 1980-81, 1985-86, 1990-91, 1995-96, 2000-01, 2005-06, and 2010-11. The database is based on information reported in each institution's college catalog, and includes data regarding changes in major academic units (schools and colleges), departments, interdisciplinary programs, and general education requirements. For schools and departments, changes in structure were coded, including new units, name changes, splits in units, units moved to new schools, reconstituted units, consolidated units, departments reduced to program status, and eliminated units. The American College Catalog Study Database (CCS) is intended to allow researchers to examine changes in the structure of institutionalized knowledge in four-year colleges and universities within the United States. For information on the study design, including detailed coding conventions, please see the Original P.I. Documentation section of the ICPSR Codebook. The data are not weighted. Dataset 1, Characteristics Variables, contains three weight variables (IDAWT, CCSWT, and CASEWEIGHT) which users may wish to apply during analysis. For additional information on weights, please see the Original P.I. Documentation section of the ICPSR Codebook. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: Approximately 75 percent of IDA institutions are included in CCS. For additional information on response rates, please see the Original P.I. Documentation section of the ICPSR Codebook. Four-year not-for-profit colleges and universities in the United States. Smallest Geographic Unit: state CCS includes 286 institutions drawn from the IDA sample of 384 United States four-year colleges and universities. CCS contains every IDA institution for which a full set of catalogs could be located at the initiation of the project in 2000. CCS contains seven datasets that can be linked through an institutional identification number variable (PROJ_ID). Since the data are organized in a panel format, it is also necessary to use a second variable (YEAR) to link datasets. For a brief description of each CCS dataset, please see Appendix B within the Original P.I. Documentation section of the ICPSR Codebook.There are date discrepancies between the data and the Original P.I. Documentation. Study Time Periods and Collection Dates reflect dates that are present in the data. No additional information was provided.Please note that the related data collection featuring the Institutional Data Archive on American Higher Education, 1970-2011, will be available as ICPSR 34874. Additional information on the American College Catalog Study Database (CCS) and the Institutional Data Archive (IDA) database can be found on the Colleges and Universities 2000 Web site.

This dataset provides RF data from software defined ratio (SDR) measurement results of a few cases: A. one 4G LTE link, B. two LTE links, C. one LTE link and one Wi-Fi link. The LTE links were emulatedby USRP B210 units and an open-source software (srsRAN), and the Wi-Fi link was emulated by a pair ofWi-Fi commercial development boards. This dataset includes metadata and performance results (ina spreadsheet format) and I/Q baseband sample data (in a binary float point format). Though specifictrade names are mentioned, they should not be construed as an endorsement of that product. Other productsmay work as well or better.The spreadsheet files provide the mapping among some system parameters (such as the SDR received powerand SINR) and key performance indicators (KPIs), such as throughput and packet drop rate. The I/Q datafiles provide the digital samples of the received signals at the receivers (LTE or Wi-Fi).This dataset can be used to support research topics such as multi-cell LTE system performance evaluationand optimization, spectrum sensing and signal classification, and AI and machine learning, beside others.

The COVID-19 Search Trends symptoms dataset shows aggregated, anonymized trends in Google searches for a broad set of health symptoms, signs, and conditions. The dataset provides a daily or weekly time series for each region showing the relative volume of searches for each symptom. This dataset is intended to help researchers to better understand the impact of COVID-19. It shouldn't be used for medical diagnostic, prognostic, or treatment purposes. It also isn't intended to be used for guidance on personal travel plans. To learn more about the dataset, how we generate it and preserve privacy, read the data documentation . To visualize the data, try exploring these interactive charts and map of symptom search trends . As of Dec. 15, 2020, the dataset was expanded to include trends for Australia, Ireland, New Zealand, Singapore, and the United Kingdom. This expanded data is available in new tables that provide data at country and two subregional levels. We will not be updating existing state/county tables going forward. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

This file contains raw data for cameras and wearables of the ConfLab dataset.

./cameras

contains the overhead video recordings for 9 cameras (cam2-10) in MP4 files.

These cameras cover the whole interaction floor, with camera 2 capturing the

bottom of the scene layout, and camera 10 capturing top of the scene layout.

Note that cam5 ran out of battery before the other cameras and thus the recordings

are cut short. However, cam4 and 6 contain significant overlap with cam 5, to

reconstruct any information needed.

Note that the annotations are made and provided in 2 minute segments.

The annotated portions of the video include the last 3min38sec of x2xxx.MP4

video files, and the first 12 min of x3xxx.MP4 files for cameras (2,4,6,8,10),

with "x" being the placeholder character in the mp4 file names. If one wishes

to separate the video into 2 min segments as we did, the "video-splitting.sh"

script is provided.

./camera-calibration contains the camera instrinsic files obtained from

https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters can

be calculated using the existing intrinsic parameters and the instructions in the

multicamera-calibration repo. The coordinates in the image are provided by the

crosses marked on the floor, which are visible in the video recordings.

The crosses are 1m apart (=100cm).

./wearables

subdirectory includes the IMU, proximity and audio data from each

participant at the Conflab event (48 in total). In the directory numbered

by participant ID, the following data are included:

1. raw audio file

2. proximity (bluetooth) pings (RSSI) file (raw and csv) and a visualization

3. Tri-axial accelerometer data (raw and csv) and a visualization

4. Tri-axial gyroscope data (raw and csv) and a visualization

5. Tri-axial magnetometer data (raw and csv) and a visualization

6. Game rotation vector (raw and csv), recorded in quaternions.

All files are timestamped.

The sampling frequencies are:

- audio: 1250 Hz

- rest: around 50Hz. However, the sample rate is not fixed

and instead the timestamps should be used.

For rotation, the game rotation vector's output frequency is limited by the

actual sampling frequency of the magnetometer. For more information, please refer to

https://invensense.tdk.com/wp-content/uploads/2016/06/DS-000189-ICM-20948-v1.3.pdf

Audio files in this folder are in raw binary form. The following can be used to convert

them to WAV files (1250Hz):

ffmpeg -f s16le -ar 1250 -ac 1 -i /path/to/audio/file

Synchronization of cameras and werables data

Raw videos contain timecode information which matches the timestamps of the data in

the "wearables" folder. The starting timecode of a video can be read as:

ffprobe -hide_banner -show_streams -i /path/to/video

./audio

./sync: contains wav files per each subject

./sync_files: auxiliary csv files used to sync the audio. Can be used to improve the synchronization.

The code used for syncing the audio can be found here:

https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/audio

Contact Information

If you would like further information about the Facial expression and landmark tracking data set, or if you experience any issues downloading files, please contact us at ravdess@gmail.com.

Facial Expression examples

Watch a sample of the facial expression tracking results.

Commercial Licenses

Commercial licenses for this dataset can be purchased. For more information, please contact us at ravdess@gmail.com.

Description

The Facial Expression and Landmark Tracking (FELT) dataset dataset contains tracked facial expression movements and animated videos from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) [RAVDESS Zenodo page]. Tracking data and videos were produced by Py-Feat 0.6.2 (2024-03-29 release) (Cheong, J.H., Jolly, E., Xie, T. et al. Py-Feat: Python Facial Expression Analysis Toolbox. Affec Sci 4, 781–796 (2023). https://doi.org/10.1007/s42761-023-00191-4) and custom code (github repo). Tracked information includes: facial emotion classification estimates, facial landmark detection (68 points), head pose estimation (yaw, pitch, roll, x, y), and facial Action Unit (AU) recognition. Videos include: landmark overlay videos, AU activation animations, and landmark plot animations.

The FELT dataset was created at the Affective Data Science Lab.

This dataset contains tracking data and videos for all 2452 RAVDESS trials. Raw and smoothed tracking data are provided. All tracking movement data are contained in the following archives: raw_motion_speech.zip, smoothed_motion_speech.zip, raw_motion_song.zip, and smoothed_motion_song.zip. Each actor has 104 tracked trials (60 speech, 44 song). Note, there are no song files for Actor 18.

Total Tracked Files = (24 Actors x 60 Speech trials) + (23 Actors x 44 Song trials) = 2452 CSV files.

Tracking results for each trial are provided as individual comma separated value files (CSV format). File naming convention of raw and smoothed tracked files is identical to that of the RAVDESS. For example, smoothed tracked file "01-01-01-01-01-01-01.csv" corresponds to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4". For a complete description of the RAVDESS file naming convention and experimental manipulations, please see the RAVDESS Zenodo page.

Landmark overlays, AU activation, and landmark plot videos for all trials are also provided (720p h264, .mp4). Landmark overlays present tracked landmarks and head pose overlaid on the original RAVDESS actor video. As the RAVDESS does not contain "ground truth" facial landmark locations, the overlay videos provide a visual 'sanity check' for researchers to confirm the general accuracy of the tracking results. Landmark plot animations present landmarks only, anchored to the top left corner of the head bounding box with translational head motion removed. AU activation animations visualize intensity of AU activations (0-1 normalized) as a heatmap over time. The file naming convention of all videos also matches that of the RAVDESS. For example, "Landmark_Overlay/01-01-01-01-01-01-01.mp4", "Landmark_Plot/01-01-01-01-01-01-01.mp4", "ActionUnit_Animation/01-01-01-01-01-01-01.mp4", all correspond to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4".

Smoothing procedure

Raw tracking data were first low-pass filtered with a 5th order butterworth filter (cutoff_freq = 6, sampling_freq = 29.97, order = 5) to remove high-frequency noise. Data were then smoothed with a Savitzky-Golay filter (window_length = 11, poly_order = 5). Scipy.signal (v 1.13.1) was used for both procedures.

Landmark Tracking models

Six separate machine learning models were used by Py-Feat to perform various aspects of tracking and classification. Video outputs generated by different combinations of ML models were visually compared, with final model choice determined by voting of first and second authors. Models were specified in the call to Detector class (described here). Exact function call as follows:

Detector(face_model='img2pose',
landmark_model='mobilenet',
au_model='xgb',
emotion_model='resmasknet',
facepose_model='img2pose-c',
identity_model='facenet',
device='cuda',
n_jobs=1,
verbose=False,
)

Default Py_feat parameters to each model were used in most cases. Non-defaults were specified in the call to detect_video function (described here). Exact function call as follows:

(video_path,
skip_frames=None,
output_size=(720, 1280),
batch_size=5,
num_workers=0,
pin_memory=False,
face_detection_threshold=0.83,
face_identity_threshold=0.8
)

Tracking File Output Format

This data set retained Py-Feat's data output format. The resolution of all input videos was 1280x720. Tracking output units are in pixels, their range of values is (0,0) (top left corner) to (1280,720) (bottom right corner).

Column 1 = Timing information

• 1. frame - The number of the frame (source videos 29.97 fps), range = 1 to n

Columns 2-5 = Head bounding box

• 2-3. FaceRectX, FaceRectY - X and Y coordinates of top-left corner of head bounding box (pixels)
• 4-5. FaceRectWidth, FaceRectHeightF - Width and Height of head bounding box (pixels)

Column 6 = Face detection confidence

• FaceScore - Confidence level that a human face was deteceted, range = 0 to 1

Columns 7-142 = Facial landmark locations in 2D

• 7-142. x_0, ..., x_67, y_0,...y_67 - Location of 2D landmarks in pixels. A figure describing the landmark index can be found here.

Columns 143-145 = Head pose

• 143-145. Pitch, Roll, Yaw - Rotation of the head in degrees (described here). The rotation is in world coordinates with the camera being located at the origin.

Columns 146-165 = Facial Action Units

Facial Action Units (AUs) are a way to describe human facial movements (Ekman, Friesen, and Hager, 2002) [wiki link]. More information on Py-Feat's implementation of AUs can be found here.

• 145-150, 152-153, 155-158, 160-165. AU01, AU02, AU04, AU05, AU06, AU09, AU10, AU12, AU14, AU15, AU17, AU23, AU24, AU25, AU26, AU28, AU43 - Intensity of AU movement, range from 0 (no muscle contraction) to 1 (maximal muscle contraction).
• 151, 154, 159. AU07, AU11, AU20 - Presence or absence of AUs, range 0 (absent, not detected) to 1 (present, detected).

Columns 166-172 = Emotion classification confidence

• 162-172. anger, disgust, fear, happiness, sadness, surprise, neutral - Confidence of classified emotion category, range 0 (0%) to 1 (100%) confidence.

Columns 173-685 = Face identity score

Identity of faces contained in the video were classified using the FaceNet model (described here). This procedure generates at 512 dimension Euclidean embedding space.

• 173. Identity - Predicated individual identifyed in the RAVDESS video. Note, value is always Person_0, as each video only contains a single actor at all times (categorical).
• 174-685. Identity_1, ..., Identity_512 - Face embedding vector used by FaceNet to perform facial identity matching.

Column 686 = Input video

• 686. frame - The number of the frame (source videos 29.97 fps), range = 1 to n

Columns 687-688 = Timing information

• 687. frame.1 - The number of the frame (source videos 29.97 fps), duplicated column, range = 1 to n
• 688. approx_time - Approximate time of current frame (0.0 to x.x, in seconds)

Tracking videos

Landmark Overlay and Landmark Plot videos were produced with plot_detections function call (described here). This function generated invidual images for each frame, which were then compiled into a video using the imageio library (described here).

AU Activation videos were produced with plot_face function call (<a

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

With the rapid development of deep learning techniques, the generation and counterfeiting of multimedia material are becoming increasingly straightforward to perform. At the same time, sharing fake content on the web has become so simple that malicious users can create unpleasant situations with minimal effort. Also, forged media are getting more and more complex, with manipulated videos (e.g., deepfakes where both the visual and audio contents can be counterfeited) that are taking the scene over still images. The multimedia forensic community has addressed the possible threats that this situation could imply by developing detectors that verify the authenticity of multimedia objects. However, the vast majority of these tools only analyze one modality at a time. This was not a problem as long as still images were considered the most widely edited media, but now, since manipulated videos are becoming customary, performing monomodal analyses could be reductive. Nonetheless, there is a lack in the literature regarding multimodal detectors (systems that consider both audio and video components). This is due to the difficulty of developing them but also to the scarsity of datasets containing forged multimodal data to train and test the designed algorithms.

In this paper we focus on the generation of an audio-visual deepfake dataset. First, we present a general pipeline for synthesizing speech deepfake content from a given real or fake video, facilitating the creation of counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS) and Dynamic Time Warping (DTW) techniques to achieve realistic speech tracks. Then, we use the pipeline to generate and release TIMIT-TTS, a synthetic speech dataset containing the most cutting-edge methods in the TTS field. This can be used as a standalone audio dataset, or combined with DeepfakeTIMIT and VidTIMIT video datasets to perform multimodal research. Finally, we present numerous experiments to benchmark the proposed dataset in both monomodal (i.e., audio) and multimodal (i.e., audio and video) conditions. This highlights the need for multimodal forensic detectors and more multimodal deepfake data.

For the initial version of TIMIT-TTS v1.0

Arxiv: https://arxiv.org/abs/2209.08000

TIMIT-TTS Database v1.0: https://zenodo.org/record/6560159

U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km2 in the Northern Basin and Range Ecoregion to a high of 78,782 km2 in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it is collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format. U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 square km in the Northern Basin and Range Ecoregion to a high of 78,782 square km in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it’s collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format.

The Food Insecurity Hotspots Data Set consists of grids at 250 meter (~7.2 arc-seconds) resolution that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events. The gridded data are based on subnational food security analysis provided by FEWS NET (Famine Early Warning Systems Network) in five (5) regions, including Central America and the Caribbean, Central Asia, East Africa, Southern Africa, and West Africa. Based on the Integrated Food Security Phase Classification (IPC), food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7