this data set is presented to explore the relationship of learning models to the development of 21st century citizenship skills. the variables are the level of understanding of learning models, ability in developing learning models, the relevance of learning models to the development of 21st century citizenship skills, the contribution of learning models to the development of critical thinking skills, contributions learning models to develop collaborative skills, the contribution of learning models to the development of communication skills, the contribution of learning models to the development of creative skills, the relevance of learning models to the development of aspects of spiritual attitudes, the relevance of learning models to the development of aspects of social attitudes, the relevance of learning models to the development of aspects of knowledge, relevance learning model for the development of aspects of factual knowledge, the relevance of the following learning models for development aspects of knowledge, the relevance of learning models to the development of aspects of procedural knowledge, the relevance of learning models to the development of aspects of metacognition knowledge.

The following dataset has been developed to assist the Groundwater Sustainability Agencies (GSAs) in their water budget development per the Groundwater Sustainability Plan Regulations §354.18. GSAs may choose to use this dataset but are not required. GSAs have the option to develop their own water year types based on best available data. For information on how the SGMA Water Year Type Dataset was developed, please see the SGMA Water Year Type Dataset Development Report.

Questions or comments can be directed to the Sustainable Groundwater Management Office at sgmps@water.ca.gov.

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 Financial Development and Structure dataset updated in November 2013 contains data from 1960 through 2011. Similar to its previous, April 2013 version, the revised dataset covers 203 jurisdictions and it is a subset of the broader Global Financial Development Database.

All indicators have been recalculated for the entire time period to ensure higher quality and consistency over time. The file contains a sheet with definitions and sources. For more detailed definitions and descriptions of the underlying sources, please see the working papers below.

Amin Mohseni-Cheraghlou and Subika Farazi were instrumental in this update.

An additional compressed file contains files with macroeconomic and institutional data averaged over the period 1980-95 that have been used as dependent or controlling variables by some of the authors in recent papers. To access the compressed file please click on this self-extracting zip file: download the file, do a File, Run, and the files will be extracted.

Beck, Demirgüç-Kunt, and Levine (2000) describe the sources and construction of, and the intuition behind, different indicators and present descriptive statistics of the Financial Development and Structure dataset.

Čihák, Demirgüç-Kunt, Feyen, and Levine (2012) discuss the related Global Financial Development Database, which encompasses all the statistics from the Financial Development and Structure dataset, plus several additional series.

The resilience of health care development in countries along the Belt and Road reflects the level of resilience of health care development in the countries along the Belt and Road, and the higher the value of the data, the stronger the resilience of health care development in the countries along the Belt and Road. The World Bank statistical database was used for the preparation of the health resilience data. Based on the year-on-year data of these four indicators, and taking into account the year-on-year changes of each indicator, the product of resilience in the development of healthcare conditions was prepared through comprehensive diagnosis based on sensitivity and adaptability analysis. "The Resilience in Health Care Development dataset for countries along the Belt and Road is an important reference for analysing and comparing the current resilience in health care development in each country.

A Planned Unit Development (PUD) is a large-scale development in which conventional zoning standards (such as setbacks and height limits) are relaxed in order to conserve sensitive areas, promote the creation of public amenities such as parks and plazas, and encourage the mixing of different land uses.

(i) The CPM LCA Database is developed within the Swedish Life Cycle Center, and is a result of the continuous work to establish transparent and quality reviewed LCA data. The Swedish Life Cycle Center (founded in 1996 and formerly called CPM) is a center of excellence for the advance of life cycle thinking in industry and other parts of society through research, implementation, communication and exchange of experience on life cycle management. The mission is to improve the environmental performance of products and services, as a natural part of sustainable development. The Center has been instrumental for the development and adoption the life cycle perspective in Swedish companies and has made important contributions to international standardization in the life cycle field. More information about the Center, see www.lifecyclecenter.se. The Swedish Life Cycle Center owns the CPM LCA Database, which is today maintained by Environmental Systems Analysis at the Department of Energy and Environment at Chalmers University of Technology. (ii) All LCI datasets can be viewed in in three formats: the SPINE format, a format compatible with the ISO/TS 14048 LCA data documentation format criteria, and in the ILCD format. Three impact assessment models: EPS, EDIP, and ECO-Indicator, can be viewed in the IA98 format. Also a simple IA calculator is provided where the environmental impact of each LCI dataset can be calculated based on the three different IA methods. (iii) unknown (iv) unknown

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...

This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data.

Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set.

Exceptions to this are:

• Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence.
• Data from Gateway to Research is made available under an Open Government Licence (Version 3.0).

Contents

• Survey data & analysis: esrc_data-survey-analysis-data.zip
• Other data: esrc_data-other-data.zip
• Transcripts: esrc_data-transcripts.zip
• Data Management Plan: esrc_data-dmp.zip

Survey data & analysis

The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data.

The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled.

The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly.

A pdf copy of the survey questions is available on GitHub.

The survey data has been decoupled into:

• survey-results-key.csv - maps a question number and the responses to the actual question values.
• q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16).
• q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23).
• q17-institutions.csv - the institution/location of the respondent (Q17).
• q18-funding.csv - funding sources within the last 5 years (Q18).

Please note the code that has been used to do the analysis will not run with the decoupled survey data.

Other data files included

• CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered.
• DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021.
• projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence.
• locations.csv - latitude and longitude for the institutions in the cleaned locations.
• subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot.
• topics.csv - topic classification for the ESRC projects for the 24th February data snapshot.

Interview transcripts

The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts:

• 1269794877.md
• 1578450175.md
• 1792505583.md
• 2964377624.md
• 3270614512.md
• 40983347262.md
• 4288358080.md
• 4561769548.md
• 4938919540.md
• 5037840428.md
• 5766299900.md
• 5996360861.md
• 6422621713.md
• 6776362537.md
• 7183719943.md
• 7227322280.md
• 7336263536.md
• 75909371872.md
• 7869268779.md
• 8031500357.md
• 9253010492.md

Data Management Plan

The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

Major Development Sites in York. For further information about major development sites please visit the City of York Council website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Open access or shared research data must comply with (HIPAA) patient privacy regulations. These regulations require the de-identification of datasets before they can be placed in the public domain. The process of image de-identification is time consuming, requires significant human resources, and is prone to human error. Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted. This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms.

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM image information objects were selected from datasets published in TCIA. Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM data elements to mimic typical clinical imaging exams. The evaluation dataset was de-identified by a TCIA curation team using standard TCIA tools and procedures. We are publishing the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (result of TCIA curation) in advance of a potential competition, sponsored by the National Cancer Institute (NCI), for de-identification algorithm evaluation, and de-identification of medical image datasets. The evaluation dataset published here is a subset of a larger evaluation dataset that was created under contract for the National Cancer Institute. This subset is being published to allow researchers to test their de-identification algorithms and promote standardized procedures for validating automated de-identification.

The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by over 1,400 OSDG Community Platform (OSDG-CP) citizen scientists from over 140 countries, with respect to the Sustainable Development Goals (SDGs).

Dataset Information

In support of the global effort to achieve the Sustainable Development Goals (SDGs), OSDG is realising a series of SDG-labelled text datasets. The OSDG Community Dataset (OSDG-CD) is the direct result of the work of more than 1,400 volunteers from over 130 countries who have contributed to our understanding of SDGs via the OSDG Community Platform (OSDG-CP). The dataset contains tens of thousands of text excerpts (henceforth: texts) which were validated by the Community volunteers with respect to SDGs. The data can be used to derive insights into the nature of SDGs using either ontology-based or machine learning approaches.

📘 The file contains 43,0210 (+390) text excerpts and a total of 310,328 (+3,733) assigned labels.

To learn more about the project, please visit the OSDG website and the official GitHub page. Explore a detailed overview of the OSDG methodology in our recent paper "OSDG 2.0: a multilingual tool for classifying text data by UN Sustainable Development Goals (SDGs)".

Source Data

The dataset consists of paragraph-length text excerpts derived from publicly available documents, including reports, policy documents and publication abstracts. A significant number of documents (more than 3,000) originate from UN-related sources such as SDG-Pathfinder and SDG Library. These sources often contain documents that already have SDG labels associated with them. Each text is comprised of 3 to 6 sentences and is about 90 words on average.

Methodology

All the texts are evaluated by volunteers on the OSDG-CP. The platform is an ambitious attempt to bring together researchers, subject-matter experts and SDG advocates from all around the world to create a large and accurate source of textual information on the SDGs. The Community volunteers use the platform to participate in labelling exercises where they validate each text's relevance to SDGs based on their background knowledge.

In each exercise, the volunteer is shown a text together with an SDG label associated with it – this usually comes from the source – and asked to either accept or reject the suggested label.

There are 3 types of exercises:

All volunteers start with the mandatory introductory exercise that consists of 10 pre-selected texts. Each volunteer must complete this exercise before they can access 2 other exercise types. Upon completion, the volunteer reviews the exercise by comparing their answers with the answers of the rest of the Community using aggregated statistics we provide, i.e., the share of those who accepted and rejected the suggested SDG label for each of the 10 texts. This helps the volunteer to get a feel for the platform.

SDG-specific exercises where the volunteer validates texts with respect to a single SDG, e.g., SDG 1 No Poverty.

All SDGs exercise where the volunteer validates a random sequence of texts where each text can have any SDG as its associated label.

After finishing the introductory exercise, the volunteer is free to select either SDG-specific or All SDGs exercises. Each exercise, regardless of its type, consists of 100 texts. Once the exercise is finished, the volunteer can either label more texts or exit the platform. Of course, the volunteer can finish the exercise early. All progress is saved and recorded still.

To ensure quality, each text is validated by up to 9 different volunteers and all texts included in the public release of the data have been validated by at least 3 different volunteers.

It is worth keeping in mind that all exercises present the volunteers with a binary decision problem, i.e., either accept or reject a suggested label. The volunteers are never asked to select one or more SDGs that a certain text might relate to. The rationale behind this set-up is that asking a volunteer to select from 17 SDGs is extremely inefficient. Currently, all texts are validated against only one associated SDG label.

Column Description

doi - Digital Object Identifier of the original document

text_id - unique text identifier

text - text excerpt from the document

sdg - the SDG the text is validated against

labels_negative - the number of volunteers who rejected the suggested SDG label

labels_positive - the number of volunteers who accepted the suggested SDG label

agreement - agreement score based on the formula (agreement = \frac{|labels_{positive} - labels_{negative}|}{labels_{positive} + labels_{negative}})

Further Information

Do not hesitate to share with us your outputs, be it a research paper, a machine learning model, a blog post, or just an interesting observation. All queries can be directed to community@osdg.ai.

The Conservation Practice Effectiveness Database compiles information on the effectiveness of a suite of conservation practices. This database presents a compilation of data on the effectiveness of innovative practices developed to treat contaminants in surface runoff and tile drainage water from agricultural landscapes. Traditional conservation practices such as no-tillage and conservation crop rotation are included in the database, as well as novel practices such as drainage water management, blind inlets, and denitrification bioreactors. This will be particularly useful to conservation planners seeking new approaches to water quality problems associated with dissolved constituents, such as nitrate or soluble reactive phosphorus (SRP), and for researchers seeking to understand the circumstances in which such practices are most effective. Another novel feature of the database is the presentation of information on how individual conservation practices impact multiple water quality concerns. This information will be critical to enabling conservationists and policy makers to avoid (or at least be aware of) undesirable tradeoffs, whereby great efforts are made to improve water quality related to one resource concern (e.g., sediment) but exacerbate problems related to other concerns (e.g., nitrate or SRP). Finally, we note that the Conservation Practice Effectiveness Database can serve as a source of the soft data needed to calibrate simulation models assessing the potential water quality tradeoffs of conservation practices, including those that are still being developed. This database is updated and refined annually. Resources in this dataset:Resource Title: 2019 Conservation Practice Effectiveness (CoPE) Database. File Name: Conservation_Practice_Effectiveness_2019.xlsxResource Description: This version of the database was published in 2019.

The development plan (BPL) contains the legally binding settlements for the urban planning order. In principle, the development plan must be developed from the land use plan. The available data is the development plan “Sauschwanz – Change” of the city of Lauchheim from XPlanung 5.0. Description: The development plan contains the legally binding fixings for the urban planning order.

Abstract

The objectives of the evaluated intervention are to expand access to quality Early Childhood Education (ECE) for 3-5-year-olds (through the construction of facilities, provision of materials, training of staff), as well as to build the demand for Early Childhood Care and Development ECCD services among families from disadvantaged backgrounds.

The study aims to find out whether the provision of simple community preschools increases enrollment and retention rates in ECCD services. Particularly with an eye towards primary school readiness, effects on the cognitive and socio-emotional development of young children are measured. Using longitudinal data, it is tested whether complementary demand-side interventions increased enrollment, especially among the poorest households, and if demand-side interventions have an effect on the impact of the intervention.

Geographic coverage

Rural villages in 13 provinces:

• Kampong Chhnang

• Kampong Speu

• Kampot

• Kandal

• Koh Kong

• Kratie

• Mondulkiri

• Preah Sihanuk

• Prey Veng

• Ratanakiri

• Steung Treng

• Svay Rieng

• Takeo

Analysis unit

The ECCD describes children and caregivers, households and villages.

Universe

Households with at least one child of age 2 - 4 at baseline.

Kind of data

Sample survey data [ssd]

Sampling procedure

The goal was to sample 26 eligible households in each of the 305 assigned villages. Eligibility criterion: at least one child of age 2-4 (at baseline) lives in the household.

Because the household listings held by local authorities are unreliable and often outdated, the data collection firm conducted a complete listing of all households in each village. Before interviewing households, the staff conducted a village mapping exercise. With the cooperation of the local authorities (village chief), the field staff have drawn a map of the village, showing all roads/intersections and households within the village and on its boundaries. Using this list, they managed to calculate the sampling interval (SI).

Field staff then followed a modified version of the EPI-walk methodology to select households: starting from a randomly selected village intersection, the interviewer:

1) Implemented the sampling interval to select the first household

2) Assessed the household eligibility (household includes at least one child aged 2 - 4 years) and

3) Conducted the household and child surveys.

If the household is not eligible for this study, the interviewer will implement a systematic sampling strategy until he/she finds an eligible household. In other words, a sampling interval of “1” was implemented until an eligible household was selected. Whenever an interview was completed with an eligible household, the interviewer implemented the previously calculated SI to reach the next household.

A maximum of 26 eligible households was selected in each targeted village. Interviewers kept following the sampling methodology described here previously until they reached the sample target (26 eligible households) or until all households were approached. If all households in the village were approached but less than 26 of them were found to be eligible, then the field team moved to the next target village.

Sampling deviation

In some village, less than 26 eligible households could be found. The goal of 26 households per village was reached in 67% of the villages. The average number of sampled households per village is 23.1.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Structured household interviews included the:

• Household and caregiver instrument

• Child testing

• Caregiver and child anthropometric data measurement

• Village chief interviews: During the administration of the household survey, the interview with the village chief was conducted.

Cleaning operations

Data management procedures for this study included:

• Testing of all instruments on Survey CTO v2.10. This included checking all of the items validity and logic patterns (skips, relevance rules, etc.). This stage also ensured that items used as key identifiers (for future merging of datasets) are clearly defined.

• Setting up on-field and in-office QC procedures (re-interviews, comparison of key items in different datasets, etc.). Implementing in-office procedures in order to identify and correct data inconsistencies and errors. Error reports were produced to this end.

• Setting up daily upload/download procedures and making sure that data is well received every day. Field work progress reports were produced to this end.

• Cleaning/labeling final dataset

Data validation and quality control took place during every step of the survey process, with mechanisms built into the survey design itself, data collection and data reporting processes. Thus, a range of tools was built into the data entry system to enable validation of data both at point of entry and during reporting stage. Unique identifiers were developed for each questionnaire in advance, to assist with datasets merging and longitudinal tracking.

Quality control procedures: During data collection, frequent checks of the data entry system were undertaken, as well as ongoing comparison of re-interviews, to ensure that data is accurately recorded by interviewers and the system is functioning as designed. After completion of data collection, the complete database was “cleaned” to check for inconsistent or missing values, incorrect skips, and validation errors. In addition to the in-system validation rules mentioned here before, the following QC procedures were implemented:

• Interview Summary: A summary of critically important items was displayed as in-system feature at the end of the interview. These items were mostly used for the dataset merging and needed to be exactly identical between the household/caregiver and the child systems. Interviewers and Field Editors made sure that the same merging information was recorded for both systems.

• Interview observations: The Field Editor or the Field Supervisor randomly selected an Interviewer in their team and joined the interview at any random point. They observed whether the Interviewer actually implemented the interview techniques agreed upon during the training: respect of the question exact and accurate wording, following conversational or standardized techniques on the relevant items (defining or not difficult notions/words to help the respondent answering the question), accurately recording respondent answers into the tablet system (Survey CTO v2.10), following ethical policies, etc.

• Re-interviews: The Field Editor randomly selected a household, from those with who an interview was completed. He/she then re-interviewed this household, asking once again a selection of 15-20 items (chosen because of their critical importance: items used for merging, items used to assess household eligibility, filter questions, etc.) for a maximum of five minutes. Stored in a separate dataset, this data was then compared against the actual initial interview data, back in office, by the Data QC Supervisor. Discrepancies were inquired into and the Data QC Supervisor called the respondent back to obtain the final correct answer. Observations and re-interviews concerned a minimum of 20% of the cases (around 5 households per village).

• Time stamps: In-system section time stamps were analyzed. They allowed the Data QC Supervisor and the Data Manager to identify potential issues (if a section/interview took seemingly significantly more time than the average) and to help assessing fieldwork progress and forecast fieldwork completion.

At completion of the data collection, (potential) data entry of paper forms and reconciliation processes, a draft database was produced, accompanied by a codebook and will be cleaned and labelled using Stata 13.1 do-files. The database was finalized based on feedback from the WB research team.

Qualitative data (as necessary: Concurrent with the quantitative data processing, any qualitative data (open-ended data from household and individual interviews) was translated into English by experienced Khmer-English translators.

Response rate

Within the 305 villages targeted for the baseline study, a total of 7,058 households were selected using the EPI walk and interviews attempted, containing a total of 7,642 children within the target age range (2 - 4 years old). Of these interviews, a total of 6,972 interviews were successfully completed (a completion rate of 98.8%). An additional 78 interviews were reported as “complete with some missing values” by the interviewers (because the respondent did not know how to answer a given question, or refused to answer, etc.).

This is the dataset of the images collected by the S.M.A.R.T Construction Research Group at NYUAD from a construction site on campus. The dataset contains the images used for the manuscript titled 'Transfer-learning and texture features for detailed recognition of the conditions of construction materials with small datasets' by Eyob Mengiste, Karunakar Reddy Mannem, Samuel A. Prieto and Borja García de Soto. For any inquiries, contact the corresponding author (eyob.mengiste@nyu.edu).

This database contains a total of 208 images for 7 construction material conditions broken down as follows: CMU wall - 24 images, Chiseled concrete - 49 images, Concrete - 18 images, Gypsum - 26 images Mesh - 25 images First coat plaster - 37 images Second coat plaster - 29 images

Introducing the Finnish Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Finnish language.

Dataset Contain & Diversity

Containing a total of 2000 images, this Finnish OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Finnish text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native Finnish people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Metadata

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of Finnish text recognition models.

Update & Custom Collection

We're committed to expanding this dataset by continuously adding more images with the assistance of our native Finnish crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

License

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Conclusion:

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the Finnish language. Your journey to enhanced language understanding and processing starts here.

This dataset is about books. It has 1 row and is filtered where the book is Rapid system development using structured techniques and relational technology. It features 7 columns including author, publication date, language, and book publisher.

High resolution land cover data set for New York City. This is the 3ft version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.