During a late 2023 survey among working-age consumers in the United Kingdom, **** percent of respondents stated that they preferred for their data to be collected via interactive surveys. Meanwhile, **** percent of respondents mentioned loyalty cards/programs as their favored data collection method.

Ever need to help a researcher share and archive their research data? Would you know how to advise them on managing their data so it can be easily shared and re-used? This workshop will cover best practices for collecting and organizing research data related to the goal of data preservation and sharing. We will focus on best practices and tips for collecting data, including file naming, documentation/metadata, quality control, and versioning, as well as access and control/security, backup and storage, and licensing. We will discuss the library’s role in data management, and the opportunities and challenges around supporting data sharing efforts. Through case studies we will explore a typical research data scenario and propose solutions and services by the library and institutional partners. Finally, we discuss methods to stay up to date with data management related topics.

The DPW Point of Collection datasets provide comprehensive information essential for managing waste and recycling services. These datasets include detailed geographic locations of trash and recycling collection points, such as street and alley collection sites, as well as the specific routes and scheduled collection days. By offering a digital map representation, the data allows the Department of Public Works to visualize and analyze the distribution of waste management resources. This enables efficient planning and coordination of collection activities, ensuring that waste is picked up in a timely, organized manner while optimizing operational effectiveness.

Data from December 2011

The dataset collection in question is comprised of a series of related tables, which are organized in a systematic manner with rows and columns for the ease of data interpretation. These tables are part of a larger dataset collection that is primarily sourced from the website of Lantmäteriet (The Land Survey of Sweden), located in Sweden. Each table within this collection contains a variety of information and data points, providing a comprehensive overview of the subject matter at hand. The dataset collection as a whole serves as a valuable resource for comprehensive data analysis and interpretation.

The dataset collection in question is a compilation of related data tables sourced from the website of Tilastokeskus (Statistics Finland) in Finland. The data present in the collection is organized in a tabular format comprising of rows and columns, each holding related data. The collection includes several tables, each of which represents different years, providing a temporal view of the data. The description provided by the data source, Tilastokeskuksen palvelurajapinta (Statistics Finland's service interface), suggests that the data is likely to be statistical in nature and could be related to regional statistics, given the nature of the source. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

The global data collector market is experiencing robust growth, driven by increasing automation across diverse sectors and the escalating demand for real-time data analysis. This market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated value of $25 billion by 2033. Key drivers include the expanding adoption of data analytics in precision agriculture, the rising prevalence of IoT devices generating massive datasets in industrial settings, and the growing need for advanced security systems relying on real-time data capture. The market is segmented by data collector type (portable and desktop) and application (agriculture, healthcare, security, industrial, communication, and others). The portable segment holds a significant market share due to its flexibility and ease of use in diverse field applications. North America and Europe currently dominate the market, but the Asia-Pacific region is poised for substantial growth fueled by increasing industrialization and technological advancements. However, factors such as high initial investment costs for advanced data collection systems and the need for skilled professionals to operate and interpret the data could act as market restraints. The competitive landscape features a mix of established technology giants like Microsoft and IBM alongside specialized data collector manufacturers like LUDECA, Inc., and PANalytical. These companies are actively engaged in research and development, focusing on improving data accuracy, speed, and integration capabilities. The increasing convergence of data collection with cloud computing and artificial intelligence is further shaping the market, creating opportunities for innovative solutions that enhance data analysis and decision-making across sectors. The market's future trajectory is closely tied to technological advancements in sensor technology, data storage, and communication networks, promising continued expansion and innovation throughout the forecast period.

The Building Strong Families (BSF) project examined the effectiveness of programs designed to improve child well-being and strengthen the relationships of low-income couples through relationship skills education. It surveyed couples 15 months and 36 months after having applied to and been accepted into a Building Stronger Families (BSF) program at one of eight locations offering services to unwed couples expecting, or having recently had a baby. Major topics included family structure, parental involvement with children, relationships, personal and parental well-being, utilization of services such as workshops to help their relationship and parenting skills, paternity and child support, and family self-sufficiency. Respondents were asked for information on recently born children and relationship status, how much time they spent with their children, their level of satisfaction with their current relationship, substance use, if they had attended relationship and parental counseling, whether they were legally required to provide child support, employment, and family background. Additional information was asked about domestic violence and child abuse, legal trouble, past sexual history, and child development. The 36-month data collection effort also included direct assessments of parenting and child development. The quality of the parenting relationship was assessed for both mothers and fathers and was based on a semi-structured play activity, "the two-bag task." This interaction was videotaped and later coded by trained assessors on multiple dimensions of parenting. During assessments with mothers, the focal child's language development was also assessed using the Peabody Picture Vocabulary Test. Demographic data includes race, education level, age, income, and marital status. The data collection is comprised of seven parts. Part 1: the BSF Eligibility and Baseline Survey Data file; Part 2: the BSF 15-Month Follow-up Survey Data file; Part 3: the program participation data file; Part 4: the BSF 15-month follow-up analysis file; Part 5: the BSF 36-Month Follow-up Survey Data file; Part 6: the mother-child in-home assessment; and Part 7: the BSF 36-Month Follow-up analysis file.

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,...

Characteristics of data collection, abstraction, and management at audit sites A–G.

This dataset contains information on the prices and fees charged by for-hire fishing operations in the Southeastern US.

Atmospheric data collection stations layer created from water management information system (WMIS) sites data. This service is for the Open Data Download application for the Southwest Florida Water Management District.

The United States Environmental Protection Agency (EPA), in cooperation with the States, biennially collects information regarding the generation, management, and final disposition of hazardous wastes regulated under the Resource Conservation and Recovery Act of 1976 (RCRA), as amended. Collection, validation and verification of the Biennial Report (BR) data is the responsibility of RCRA authorized states and EPA regions. EPA does not modify the data reported by the states or regions. Any questions regarding the information reported for a RCRA handler should be directed to the state agency or region responsible for the BR data collection. BR data are collected every other year (odd-numbered years) and submitted in the following year. The BR data are used to support regulatory activities and provide basic statistics and trend of hazardous waste generation and management.

BR data is available to the public through 3 mechanisms. 1. The RCRAInfo website includes data collected from 2001 to present-day (https://rcrainfo.epa.gov/rcrainfoweb/action/main-menu/view). Users of the RCRAInfo website can run queries and output reports for different data collection years at this site. All BR data collected from 2001 to present-day is stored in RCRAInfo, and is accessible through this website. 2. BR data files collected from 1999 - present day may be downloaded directory in zip file format from (https://rcrapublic.epa.gov/rcra-public-export/?outputType=Fixed or https://rcrapublic.epa.gov/rcra-public-export/?outputType=CSV). 3. Historical data collected prior to 1999 may be ordered on CD. Please see contact information in this metadata file to order historical BR data. BR data are typically published in December of the year following their collection. Data must be received by authorized states and EPA regions if a state is not authorized to implement the BR program by March 1st of the year following collection, and are usually published in December of the year following collection. For example, data collected in 2001 would be received by states and EPA regions by March 1, 2002 and states and EPA regions compile the BR data submitted by facilities and load the state data set into RCRAInfo, the system which EPA Headquarters (HQ) manage. Then EPA HQ published the data files around December 2002. Additional information regarding the biennial report data is available here: https://rcrapublic.epa.gov/rcra-public-export/rcrainfo_flat_file_documentation_v5.pdf and here: https://www.epa.gov/hwgenerators/biennial-hazardous-waste-report.

Please note that the update frequency field for this data set indicates annual, but that the true update period is biennial (every other year). There is no selection option for biennial for the update frequency field.

The detailed reports for each CX Data Collection

including 43

This dataset is a LAS (industry-standard binary format for storing lidar point clouds) dataset containing light detection and ranging (lidar) data and sonar data representing the beach and near-shore topography of Lake Superior at Minnesota Point, near the Superior entry, Duluth, Minnesota. Average point spacing of the LAS files in the dataset are as follows: lidar, 0.086 meters (m); multibeam sonar, 0.512 m; single-beam sonar, 1.919 m. The LAS dataset was used to create digital elevation models (DEMs) of 10 m (32.8084 feet) and 1 m (3.28084 feet) resolution, of the approximate 2.15 square kilometer surveyed area. Lidar data were collected September 07, 2022 using a boat mounted Velodyne VLP-16 unit and methodology similar to that described by Huizinga and Wagner (2019). Multibeam sonar data were collected September 06-07, 2022 using a Norbit integrated wide band multibeam system compact (iWBMSc) sonar unit and methodology similar to that described by Richards and Huizinga (2018). Single-beam sonar data were collected September 07, 2022 using a Ceescope echosounder and methodology similar to that described by Wilson and Richards (2006).This project followed similar methods to that of Wagner, Lund, and Sanks (2020), who completed a similar survey in 2019.

These quarterly reports show the number of receipts, dispositions and pending New Court Cases (NCCs) during the defined period. The data shown is by month with quarterly and fiscal year (FY) summaries through the most recently completed quarter.

Title: Regulatory instruments on plastics data collectionSummary: This indicator accounts for the number information instruments on plastics research, data collection, data reporting, or record keeping that are defined in policy documents. This includes the assemblage, analysis, maintenance, management, or dissemination of information related to plastics. Examples include e.g. design of national marine litter monitoring programs or monitoring impacts of plastics on ecosystems or requiring producers or distributors to provide annual reports on their activities.Policy instruments are defined as tools by which governments use power in attempting to ensure support and effect social change, in this case to reduce plastic pollution into the ocean. Information instruments are a type of policy instrument. Entity: Nicholas Institute for Energy, Environment & Sustainability of Duke UniversitySource URL: https://nicholasinstitute.duke.edu/articles/introducing-nicholas-institute-energy-environment-sustainabilityTime Period: 1991-2022Methodology: Researchers at the Nicholas Institute extracted policy documents from global legal databases (e.g. FAOLEX and InforMEA, among others) using a set of keywords, only including policy documents that demonstrate a clear intent on behalf of policy makers to address plastics specifically. See here for more details on methods. A subset of these policy documents written or credibly translated into English were qualitatively coded using qualitative coding analysis software, NVIVO. Using a codebook, researchers coded individual policy instruments within policy documents based on plastic type(s) targeted, lifecycle stage(s) targeted, and instrument(s) used. See pg. 150 in this document for more details.Frequency Update: UnknownLast Update: 24-05-2023Geo-Coverage: GlobalLicensing: Public

This collection contains the following resource data sets for Pennypack Creek watershed in Philadelphia, Pennsylvania, USA: stream chemistry; shape files of watershed delineation.

This data set is a compilation of heat flow data of uncertain origin. References as cited in Global Heat Flow Database were incomplete and thus could not be verified. This data compilation contains: data of unknown origin, unpublished data, data which has no full reference information or data which were extracted from other database. The remaining short citation and its related problem are listed in columns 18 and 19.