The vast majority of scientific articles published to-date have not been accompanied by concomitant publication of the underlying research data upon which they are based. This state of affairs precludes the routine re-use and re-analysis of research data, undermining the efficiency of the scientific enterprise, and compromising the credibility of claims that cannot be independently verified. It may be especially important to make data available for the most influential studies that have provided a foundation for subsequent research and theory development. Therefore, we launched an initiative—the Data Ark—to examine whether we could retrospectively enhance the preservation and accessibility of important scientific data. Here we report the outcome of our efforts to retrieve, preserve, and liberate data from 111 of the most highly-cited articles published in psychology and psychiatry between 2006–2011 (n = 48) and 2014–2016 (n = 63). Most data sets were not made available (76/111, 68%, 95% CI [60, 77]), some were only made available with restrictions (20/111, 18%, 95% CI [10, 27]), and few were made available in a completely unrestricted form (15/111, 14%, 95% CI [5, 22]). Where extant data sharing systems were in place, they usually (17/22, 77%, 95% CI [54, 91]) did not allow unrestricted access. Authors reported several barriers to data sharing, including issues related to data ownership and ethical concerns. The Data Ark initiative could help preserve and liberate important scientific data, surface barriers to data sharing, and advance community discussions on data stewardship.

Data from the IPv4 Routed /24 Topology Dataset are processed by using RouteViews BGP data to identify the Autonomous System (AS) associated with each responding IP address and collapsing the original probed IP paths into a set of links between ASes.

Abstract

The do-file combined_ark_data creates 2 versions of the dataset ark_combined_final, one with MEIRU ids retained (_meiruid) and one without (_anon) which was sent to external collaborators (note that the anon version is not suitable for open access). The do-file also creates a listing of iohexol results from both sites from the South African lab. The combined dataset is the all the data from the ARK study which was carried out in Lilongwe and Karonga sites in 2018/2019. The do-file brings data from several tables to have one record per person. The tables used are listed below:

ark_iohex_results : Iohex results file generated by this do-file using excel results lists sent by Jaya in SA lab
arkk_arkq / arkl_arkq : Data from ARK questionnaire (Karonga / Lilongwe)
arkk_consent / arkl_consent : Data from ARK consent form (Karonga / Lilongwe) arkk_fieldlog / arkl_fieldlog : Data from ARK field log (Karonga / Lilongwe)
arkk_syrweight / arkl_syrweight : Data from syringe weights for iohex (Karonga / Lilongwe)
gen_identity_clean / genl_identity_clean : Identity data (Karonga / Lilongwe) ser_electrolytes / lab_electrolytes : Electrolyte (urine & serum) test result data (Karonga / Lilongwe)
ser_fbc / lab_fbc : Full blood count test results (Karonga / Lilongwe)
ser_genchem / lab_genchem : General chemistry test results data (Karonga / Lilongwe)
ser_glucose / lab_glucose : Glucose test results data (Karonga / Lilongwe) ser_hba1c / lab_hba1c : HBA1c test results data (Karonga / Lilongwe)
ser_hepbhepc / lab_hepbhepc : Hepatitis test results data (Karonga / Lilongwe)
ser_malaria_rt / lab_malaria_rt : Rapid malaria test results data (Karonga / Lilongwe)
ser_rtr / lab_rtr : Rapid HIV test results data (Karonga / Lilongwe)
ser_schisto / lab_schisto : Schistosomiasis test results data (Karonga / Lilongwe)
ser_specimen / lab_specimen : Data on all lab specimens (Karonga / Lilongwe) ser_urinalysis / lab_urinalysis : Urinalysis test results data (Karonga / Lilongwe)

Analysis unit

Individual

Mode of data collection

Face-to-face [f2f]

These are all the Ark IPv4 team-probing data, collected by a globally distributed set of Archipelago (Ark) monitors. IPv4 Routed /24 Topology dataset. It contains information useful for studying the IP and AS topology of the IPv4 Internet.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset provides information about the number of properties, residents, and average property values for Ark Drive cross streets in Semmes, AL.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Agathe1489/Louvre-ARK-data dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset provides information about the number of properties, residents, and average property values for Ark Royal Court cross streets in Ocean Isle Beach, NC.

These are all the Ark IPv6 probing data, collected by a globally distributed set of IPv6-enabled Archipelago (Ark) monitors. These data contain information useful for studying the IP and AS topology of the IPv6 Internet.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The ARK extension for CKAN provides the capability to mint and resolve ARK (Archival Resource Key) identifiers for datasets. Inspired by the ckanext-doi extension, it facilitates persistent identification of data resources within a CKAN instance, ensuring greater stability and longevity of data references. This extension is compatible with CKAN versions 2.9 and 2.10 and supports Python versions 3.8, 3.9, and 3.10. Key Features: ARK Identifier Minting: Generates unique ARK identifiers for CKAN datasets, helping to guarantee long-term access and citation stability. ARK Identifier Resolving: Enables resolution of ARK identifiers to associated CKAN resources. Configurable ARK Generation: Allows customization of ARK generation through configurable templates and shoulder assignments, providing flexibility in identifier structure. ERC Metadata Mapping: Supports mapping of dataset fields to ERC (Encoding and Rendering Conventions) metadata elements, using configurable mappings to extract data for persistent identification. Command-Line Interface (CLI) Tools: Includes CLI commands to manage (create, update and delete) ARK identifiers for existing datasets. Customizable NMA (Name Mapping Authority) URL: Supports setting up a custom NMA URL, providing the ability to customize the resolver to point towards different data sources than ckan.siteurl. Technical Integration: The ARK extension integrates with CKAN through plugins and modifies the read_base.html template (either in a custom extension or directly) to display the ARK identifier within the dataset's view. Configuration settings, such as the ARK NAAN (Name Assigning Authority Number) and other specific parameters, are defined in the CKAN configuration file (ckan.ini). Database initialization is required after installation to ensure proper functioning. Benefits & Impact: Implementing the ARK extension enhances the data management capabilities of CKAN by providing persistent identifiers for datasets. This ensures that data resources can be reliably cited and accessed over time. By enabling the association of ERC metadata with ARKs, the extension promotes better description and discoverability of data. The extension can be beneficial for institutions that require long-term preservation and persistent identification of their data resources.

This dataset provides information about the number of properties, residents, and average property values for Ark Road cross streets in Lumberton, NJ.

The IPv4 Routed /24 DNS Names Dataset provides fully-qualified domain names for IP addresses seen in the traces of the IPv4 Routed /24 Topology Dataset

This dataset is about: Continuous thermosalinograph oceanography along POLARSTERN cruise track PS108 (ARK-XXXI/3).

Browse ARK ETF Trust (ARKK) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Ark Engineering And Solutions Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The Northern Ireland Life and Times Survey (NILT) series began in 1998, and was the successor to the previous Northern Ireland Social Attitudes series, which was discontinued in 1996.
The main aims of the NILT series are:

• to monitor public attitudes towards social policy and political issues in Northern Ireland;
• to provide a time series on attitudes to key social policy areas;
• to facilitate academic social policy analysis;
• to provide a freely available resource on public attitudes for the wider community of users in Northern Ireland.

NILT originally had a companion series which also began in 1998, the Young Life and Times Survey (YLT), although the YLT methodology changed in 2003 and it is conducted separately each year. The Kids' Life and Times (KLT) survey of P7 children (10-11 year olds) is also part of the same suite of surveys as YLT and NILT.

NILT also forms part of the International Social Survey Programme (ISSP), although it does not do so every year. Unfortunately, NILT did not run in 2011 due to funding issues, though YLT ran as normal that year; NILT resumed in 2012 (SN 7408). In addition, several open access teaching datasets were created by ARK (Access Research Knowledge) from various years of NILT, covering different topics such as Lesbian, Gay, Bisexual and Transgender (LGBT) issues, politics and community relations, attitudes to ageing and ageism, and dementia.

Further information about the series may be found on the ARK http://www.ark.ac.uk/nilt/" target="_blank" rel="noopener">NILT webpage.

The Northern Ireland Life and Times Survey, 2012: Lesbian, Gay, Bisexual and Transgender Issues Teaching Dataset is part of a suite of teaching and learning resources created as part of a Higher Education Academy (HEA) strategic project focusing on teaching research methods. The project Learning by numbers: new open educational resources for teaching quantitative methods involved the creation of new teaching datasets from two major surveys focusing on Northern Ireland, with accompanying 'student-friendly' documentation and teaching guidelines. Specifically, two teaching datasets were created using NILT 2012 (see also SN 7547, which covers politics and good relations) as well as a time-series teaching dataset drawing on the 2003-2012 Young Life and Times (YLT) surveys (see SN 7548). Documentation combining an edited technical report and codebook accompanies the teaching datasets. This documentation includes details of all the variables included in the teaching datasets as well as a summary technical report, with the main issues outlines in accessible language, for example, research design, sampling and response rates. Teaching guidelines drawing upon the particular variables included in the datasets are also available.

This dataset focuses on public attitudes to Lesbian, Gay, Bisexual and Transgender (LGBT) issues in Northern Ireland. It is based on NILT 2012, and adapted for the purposes of this project. Some variables have been constructed and/or simplified for this teaching dataset – notes are provided in the codebook. While the teaching datasets contains the same total number of respondents, they are intended for teaching purposes only; it is advisable to use the original NILT 2012 study (see SN 7408) for research. Further information about the teaching datasets may be found on the ARK Teaching datasets: Learning by numbers webpage.

Sea ice surface roughness data were obtained during the ARK-XII campaign in Jul-Sep, 1996 with a Single Beam Laser Altimeter (SBLA) mounted on an/a Helicopter (Mil MI-8) . A method developed by Hibler (1972) was used to a) isolate the surface profile from low-frequency variations associated with the aircraft motion and b) to identify pressure ridge sails. The processing steps are described in https://epic.awi.de/id/eprint/56364/. We applied a ridge detection threshold of 0.6 m, which means that only sails higher 0.6 m are detected. Version and name of the processing routine: Laser_Altimeter_Processing_VS5_06_20.py (vers.5, Feb 22, 2024, https://gitlab.awi.de/sitem/sbla_processing.git). SBLA records (IBEO - PS100E) are provided at a sampling rate of 200 Hz. Sensor accuracy is 3 cm with a beam diameter at surface of 11.4 cm. This specific dataset was obtained on 19960819T1232. It includes recorded altimeter readings, the derived surface elevation and width/height/spacing of detected pressure ridge sails. Note on data quality: 11.4 cm . File name: [DMS/PANGAEA Campaing Identifier] + [DEVICE] + [DATE/TIME] + [LAT/LON] + [Detection Threshold] + [Object] + [Version] + [Format]