A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

Dataset and code for the journal article Progressive Denoising of Monte Carlo Rendered Images, published in Computer Graphics Forum 41, 2 (May 2022), 1–11 (https://doi.org/10.1111/cgf.14454). Cite the journal article, when used in a publication: @inproceedings{firmino2022progressive, title={Progressive Denoising of Monte Carlo Rendered Images}, author={Firmino, Arthur and Frisvad, Jeppe Revall and Jensen, Henrik Wann}, booktitle={Computer Graphics Forum}, volume={41}, number={2}, pages={1--11}, year={2022}, organization={Wiley Online Library} } Dataset contains images in multi-channel EXR format used in the training, validation, and testing of the progressive denoising models trained as part of the publication (also attached). If using the attached code, execute the command, "git submodule update --init --recursive" in the unzipped folder to pull the required dependencies, and see the README.md file for additional instructions. Abstract: Image denoising based on deep learning has become a powerful tool to accelerate Monte Carlo rendering. Deep learning techniques can produce smooth images using a low sample count. Unfortunately, existing deep learning methods are biased and do not converge to the correct solution as the number of samples increase. In this paper, we propose a progressive denoising technique that aims to use denoising only when it is beneficial and to reduce its impact at high sample counts. We use Stein's unbiased risk estimate (SURE) to estimate the error in the denoised image, and we combine this with a neural network to infer a per-pixel mixing parameter. We further augment this network with confidence intervals based on classical statistics to ensure consistency and convergence of the final denoised image. Our results demonstrate that our method is consistent and that it improves existing denoising techniques. Furthermore, it can be used in combination with existing high quality denoisers to ensure consistency. In addition to being asymptotically unbiased, progressive denoising is particularly good at preserving fine details that would otherwise be lost with existing denoisers.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Data set of failure times of 20 mechanical components.

AEROSONICDB 3K: AUDIO DATASET OF LOW-FLYING AIRCRAFT

Version 2.0-beta (July 2024)

This repository is currently restricted, however you can gain access by "requesting access" on this page with your Zenodo account, or by emailing Blake Downward at aerosonicdb@gmail.com, or by submitting the following Google Form.

Created by

Blake Downward

Publication

When using this data in an academic work, please reference the dataset DOI and version. Please also reference the following paper which describes the methodology for collecting the dataset and presents baseline model results.

Downward, B., & Nordby, J. (2023). The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft. ArXiv, abs/2311.06368.

Description

AeroSonicDB 3K is a machine labelled audio dataset of over 3,000 low-flying aircraft events. Samples in this dataset were recorded at locations in close proximity to a flight path approaching or departing Adelaide International Airport’s (ICAO code: YPAD) primary runway, 05/23. Recordings are triggered and labelled based on radio (ADS-B) messages received from the aircraft overhead, then human verified and trimmed to the first and final moments the target aircraft is audible in the sample.

Major changes from version 1

This iteration of AeroSonicDB was developed with the aim to improve accessibility and usability for the end-user, streamline the sample collection and annotation procedures, and minimise memory consumption without losing crucial information. A summary of the major changes include:

• Sample rate for audio files is now 16,000 Hz (previously 22,050 Hz).
• Samples have been trimmed to the audio event onset and offset moments - the entire duration of the clip can be assumed to be of the same class.
• “altitude” has been ommited as a sample feature.
• “engfamily” and “shortdesc” have benn ommitted as aircraft features.
• Samples are split across 5 temporaly disjointed folds. (previously had a sixth fold to act as a hold-out test set)
• Introduction of a PDM microphone embedded in a microcontroller for capturing audio (approximately 20% of all samples)
• A machine learning model trained on the original dataset was deployed to assist with annotation.
• Approximately 60% of newly acquired silence/background samples were annotated by progressive iterations of this model.
• 582 unique aircraft recorded (up 280 from 302 in version 1)
• 3,058 recordings of aircraft audio events for a total of 38 hours (up from 625 recordings and 8.9 hours)
• 32 hours of silence/background audio (up from 3.5 hours)

Audio data

SAMPLE RATE: 16,000 Hz
DATA TYPE: ‘int16’/‘wav’

Class distribution (hours of audio)

Binary Classes

• no aircraft/background (32)
• aircraft (38)

Sub-classes

• no aircraft/background (32)
• Piston aircraft (1.2)
• Turboprop aircraft (5.3)
• Turbofan aircraft (31)
• Rotorcraft/helicopter (0.5)

Data splits/folds

Recordings have been split into 5 folds, giving researchers a common split for cross-validation and ensuring comparable results. Folds are temporaly disjoint to avoid data leakage.

Metadata files

The entire dataset is referenced with labels in the “sample_meta.csv” file. Each row contains a reference to a unique recording, as well as meta information such as; class, duration and fold.

The “aircraft_meta.csv” a file can be used to reference aircraft specific features - such as; manufacturer, engine type, ICAO type designator etc. (see “Columns/Labels” below for all features).

Audio-file naming convention

Audio samples are in WAV format, with some additional metadata stored in the filename.

Basic Convention

“Aircraft ID + Date + Time + Location ID + Microphone ID”

“XXXXXX_YYYY-MM-DD_hh-mm-ss_X_X”

Sample with aircraft

{hex_id} _ {date} _ {time} _ {location_id} _ {microphone_id} . {file_ext}

7C7CD0_2023-05-09_12-42-55_2_1.wav

Sample without aircraft

“Silence” files are denoted with six (6) leading zeros rather than an aircraft hex code. All relevant metadata for “silence” samples are contained in the audio filename, and again in the accompanying “sample_meta.csv”

000000 _ {date} _ {time} _ {location_id} _ {microphone_id} . {file_ext}

000000_2023-05-09_12-30_2_1.wav

Columns/Labels (sample_meta.csv)

filename: The filename of the audio recording

duration: Length of the audio event in seconds.

date: Date of the recording

fold: Digit from 0 to 4 splitting the dataset 5 ways

class: Class label for the recording (eg. 0 = No aircraft/background, 1 = Aircraft audible)

hex_id: Unique ICAO 24-bit address for the aircraft recorded

Columns/Labels (aircraft_meta.csv)

hex_id: Unique ICAO 24-bit address for the aircraft recorded

Manu: Aircraft manufacturer (eg. Boeing, Pilatus, Airbus)

Model: Aircraft model (eg. 737-800, A320-232, DHC-8-315)

engnum: Number of engines

Engmanu: Engine manufacturer (eg. Pratt & Whitney, CFM International, Rolls Royce)

Engtype: Type of engine (eg. Piston, Turboprop, Turbofan, Turboshaft)

Engmodel: Engine model (eg. TRENT XWB, CFM56-7B24E, PT6E-67XP)

Fueltype: Fuel type used in the engine (eg. Gasoline, Kerosene)

Airframe: Describes the mechanical structure of the aircraft (eg. Power Driven Aeroplane, Rotorcraft)

Propmanu: Propeller manufacturer (eg. Hartzell Propellers, Hamilton Standard, “Aircraft Not Fitted With Propeller”)

Propmodel: Propeller model (eg. HC-E5A-3A/NC10245B, 14SF-15, “Not Applicable”)

MTOW: Maximum take off weight (MTOW) in kilograms

ICAOtypedesig: ICAO type designator for make and model of aircraft (eg. PC12, C185, B738)

Conditions of use

Dataset created by Blake Downward.

The AeroSonicDB 3K dataset is offered free of charge for non-commercial use under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

https://creativecommons.org/licenses/by-nc/4.0/

Acknowledgements

Special thanks to Jon Nordby of Soundsensing AS - his contributions were pivotal in maximising the potential of this dataset for open-source release.

Feedback

Please send suggestions, feedback and comments to: Blake Downward: aerosonicdb@gmail.com

Change log

• 2.0-beta: All major changes as described above
• 1.1.2: Added “environment_mappings_raw.csv”. No change to audio from Version 1.1
• 1.1.1: Minor change to “sample_meta.csv” - replaced “6” with “test” in the “fold” column
• 1.1: Replaced truncated aircraft samples with the original full-length files and annotated the beginning and end of each audio event. Added ‘ignore’ statements to aircraft event boundaries in the environmental class mappings file.
• 1.0: Environmental audio and mappings added
• 0.3: locations.json file added, README updated
• 0.2: location information added to README

Context This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles. Content This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework. A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters. The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities. By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles. Technical Details Sonar: Blueprint Oculus M1200d Operating frequency: 1.2 MHz (low frequency mode) Maximum range: 40 m (set to 20 m for this dataset) Horizontal aperture: 130° Vertical aperture: 20° Number of beams: 512 Angular resolution: 0.6° Beam separation: 0.25° Image resolution: 902x497 pixels Coordinate system: Polar Camera: Allied-Vision Manta G-917 Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset) Sensor type: CCD Progressive Sensor bit depth: 12-bit Captured bit depth: 8-bit Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients Focal length (fx, fy): (1638.36157, 1641.95202) Principal point (cx, cy): (1705.03529, 1380.27954) Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000) Tangential distortion coefficients (p1, p2): (0.000259, -0.002945) Navigation: Data format: CSV Contains fused dead reckoning data based on GPS, DVL, and IMU sensors Columns: timestamp: Unix timestamp (seconds) latitude: Latitude (degrees) longitude: Longitude (degrees) altitude: Altitude (meters) yaw: Yaw angle (degrees) pitch: Pitch angle (degrees) roll: Roll angle (degrees) velocity_x: Velocity along the x-axis (meters per second) velocity_y: Velocity along the y-axis (meters per second) velocity_z: Velocity along the z-axis (meters per second) depth: Depth (meters) Frame rate: 2 Hz for both sonar and camera More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page. Acknowledgements The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.

The sample included in this dataset represents children who participated in a cross-sectional study, a smaller cohort of which was followed up as part of a longitudinal study reported elsewhere (Bull et al., 2021). In the original study, 347 children were recruited.

As data was found to be likely missing completely at random (χ2 = 29.445, df = 24, p = .204, Little, 1998), listwise deletion was used, and 23 observations were deleted from the original dataset.

This dataset includes three hundred and twenty-four participants that composed the final sample of this study (162 boys, Mage = 6.2 years, SDage = 0.3 years). Children in this sample were in their second year of kindergarten (i.e., the year before starting primary school) in Singapore.

The dataset includes children's sociodemographic information (i.e., age and sex) and performance on different general cognitive and mathematical skills.

Mathematical tasks:

- Computer-based 0-10 and 0-100 number line task

- Mathematical Reasoning and Numerical Operations subtests from the Wechsler Individual Achievement Test II (WIAT II). Though the Numerical operations subtest is not used in this study.

General cognitive tasks:

- Peabody Picture Vocabulary Test (Vocabulary)

- Raven’s Progressive Matrices Test (Non-verbal reasoning)

The variables included in this dataset are:

Age = Child’s age (in months)

Sex = Boy/Girl (parent reported; boy=1, girl=2)

Ravens = Non-verbal reasoning (Raven’s Progressive Matrices test)

Ppvt = Vocabulary raw score (Peabody Picture Vocabulary Test)

Maths_reason = Mathematical reasoning raw score (Math Reasoning subtest from the Wechsler Individual Achievement Test II)

Num_Ops = Numerical Operations raw score (Numerical Operations subtest from the Wechsler Individual Achievement Test II, not used in this study)

NLE10 = 0-10 number line (Percent absolute error)

NLE100 = 0-100 number line (Percent absolute error)

This dataset overlaps with the dataset that is the basis for: Ruiz, C., Kohnen, S., & Bull, R. (2023) Number Line Estimation Patterns and Their Relationship with Mathematical

Performance. Journal of Numerical Cognition. Advance Online Publication https://doi.org/10.23668/psycharchives.12698

That project’s corresponding OSF page can be found here: https://osf.io/jat5h/ and the dataset is stored under embargo here: https://doi.org/10.25949/22558528.v1

Sample illustration of stratified bootstrapping technique.