Public Data Archives Research Articles

Atmospheric Escape From Earth and Mars: Response to Solar and Solar Wind Drivers of Oxygen Escape

AbstractHabitability at the surface of a planet depends on having an atmosphere long enough for life to develop. The loss of atmosphere to space is an important component in assessing planetary surface habitability. Current models of atmospheric escape from exoplanets are not well constrained by observations. Atmospheric escape observations from the terrestrial planets are available in public data archives. We recast oxygen escape rates from Earth derived from an instrument on Dynamics Explorer‐1 as function of solar wind and compare them to similar data from Mars. Analysis demonstrates that oxygen escape rates from Mars are not as sensitive to variations in solar power components as those from Earth. Available data from Venus can confirm or refute the assertion that oxygen escape from magnetized planets is more sensitive than that from unmagnetized planets.

Catalogue of BRITE-Constellation targets. I. Fields 1 to 14 (November 2013 – April 2016)

The BRIght Target Explorer (BRITE) mission collects photometric time series in two passbands with the aim of investigating stellar structure and evolution. Since their launches in the years 2013 and 2014, the constellation of five BRITE nano-satellites has observed a total of more than 700 individual bright stars in 64 fields. Some targets have been observed multiple times. Thus, the total time base of the datasets acquired for those stars can be as long as nine years. Our aim is to provide a complete description of ready-to-use BRITE data, to show the scientific potential of the BRITE-Constellation data by identifying the most interesting targets, and to demonstrate how scientists can use these data in their research, and encourage them to do so. We applied a decorrelation process to the automatically reduced BRITE-Constellation data to correct for instrumental effects. We performed a statistical analysis of the light curves obtained for the 300 stars observed in the first 14 fields during the first sim 2.5 years of the mission. We also performed cross-identification with the International Variable Star Index. We present the data obtained by the BRITE-Constellation mission in the first 14 fields it observed, from November 2013 to April 2016. We also describe the properties of the data for these fields and the 300 stars observed in them. Using these data, we detect variability in 64<!PCT!> of the presented sample of stars. Sixty-four stars, or 21.3\,<!PCT!> of the sample, have not yet been identified as variable in the literature and their data have not been analysed in detail. They can therefore provide valuable scientific material for further research. All data are made publicly available through the BRITE Public Data Archive and the Canadian Astronomy Data Centre.

Enhanced Regional Earthquake Catalog with Alaska Amphibious Community Seismic Experiment Data

Abstract The Alaska Amphibious Community Seismic Experiment (AACSE) comprised 75 ocean-bottom seismometers and 30 land stations and covered about 650 km along the segment of the subduction zone that includes Kodiak Island, the Alaska Peninsula and the Shumagin Islands between May 2018 and September 2019. This unprecedented onshore-offshore dataset provided an opportunity to compile a greatly enhanced earthquake catalog for the region by both increasing the number of detected earthquakes and improving the accuracy of their source parameters. We use all available regional and AACSE campaign seismic data to compile an earthquake catalog for the region between Kodiak and the Shumagin Islands including the Alaska Peninsula (51° N–59° N, 148° W–163° W). We apply the same processing and reporting standards to additional picks and events as the Alaska Earthquake Center currently uses for compilation of the authoritative regional earthquake catalog. Over 7200 events (both newly detected and previously reported) have been processed with AACSE data. We added about 30% more events, 60% more phase picks, lowered the magnitude of completeness by about 0.2 on average across the region, and improved location errors. All data have been published in public data archives. In addition, we test the machine-learning earthquake detection and picking algorithm EarthquakeTransformer (EQT) on the AACSE seismic dataset, comparing EQT-determined P and S picks with the new catalog. EQT is entirely trained on land data, whereas AACSE is amphibious. Overall, EQT finds 59% of P and 63% of S arrivals in the catalog within 300 km epicentral distance. The percent of catalog picks detected by EQT varies inversely with earthquake epicentral distance, and EQT performs particularly poorly on data from earthquakes recorded by instruments in the outer rise.

Using 'Big Data' to Disentangle Aging and COVID-19.

Arthur et al. leverage different types of big data, either generated in house from cohorts of healthy aging and COVID-19, or downloaded from the ever-increasing public data archives, to disentangle the distinct cellular and proteomic mechanisms of COVID-19 and aging.

A survey of IRAS young stellar object candidates

Context. Jets and outflows are associated with young stellar objects across the stellar mass spectrum, from brown dwarf protostars to massive Ae/Be stars. Frequently the jet morphology is spatially discontinuous because of the temporal variability of the ejection from the driving source. Images covering a wide field of view around the jet-driving source are useful to map the large-scale jet emission and to explore the mass ejection history. Aims. The aim of our work was to search for large-scale optical Herbig-Haro (HH) objects lying in a wide field around a sample of IRAS sources, which are candidates to trace young stellar objects. Methods. Deep narrowband images through the Hα and [S II] emission lines, and through an off-line continuum filter, covering a wide (~15′) field around the IRAS targets were acquired. The images in the three filters were analyzed to identify shock-excited line emission (i.e., HH) in contrast to scattered line emission. Results. New images of a sample of 15 IRAS sources, obtained in a homogeneous way are presented. HH emission was detected in six fields, and the astrometry of the knot features is given. The nature of the extended emission as scattered emission around nine of the IRAS targets is confirmed. For seven IRAS sources with unclear optical counterparts, a more plausible counterpart is proposed. A refined value of the source distance is reported for seven targets. An update of the main data available for each of the sampled fields, including images from public data archives, is also presented.

Towards Smart Data Selection From Time Series Using Statistical Methods

Transmitting and storing large volumes of dynamic / time series data collected by modern sensors can represent a significant technological challenge. A possibility to mitigate this challenge is to effectively select a subset of significant data points in order to reduce data volumes without sacrificing the quality of the results of the subsequent analysis. This paper proposes a method for adaptively identifying optimal data point selection algorithms for sensor time series on a window-by-window basis. Thus, this contribution focuses on quantifying the effect of the application of data selection algorithms to time series windows. The proposed approach is first used on multiple synthetically generated time series obtained by concatenating multiple sources one after the other, and then validated in the entire UCR time series public data archive.

The Breakthrough Listen Search for Intelligent Life: A 3.95–8.00 GHz Search for Radio Technosignatures in the Restricted Earth Transit Zone

Abstract We report on a search for artificial narrowband signals of 20 stars within the restricted Earth Transit Zone (rETZ) as a part of the ten-year Breakthrough Listen (BL) search for extraterrestrial intelligence (ETI). The rETZ is the region of the sky from which an observer would see Earth transit the Sun with an impact parameter of less than 0.5. This region of the sky is geometrically unique, providing a potential way for an ETI to discover the solar system. The targets were nearby (7–143 pc) and the search covered an electromagnetic frequency range of 3.95–8.00 GHz. We used the Robert C. Byrd Green Bank Telescope to perform these observations with the standard BL data recorder. We searched these data for artificial narrowband (∼Hz) signals with Doppler drift rates of ±20 Hz s−1. We find no evidence for radio technosignatures from ETI in our observations. The observing campaign would have allowed for detections of emissions that were 10−3 to 0.88 times as powerful as the signaling capability of the Arecibo radar transmitter. We conclude that at least 0.6% of the systems in the rETZ within 150 pc do not possess the type of transmitters searched in this survey. To our knowledge, this is the first targeted search for ETI of the rETZ. All data used in this paper are publicly available via the BL Public Data Archive (http://seti.berkeley.edu/bldr2).

The Alliance of Genome Resources: Building a Modern Data Ecosystem for Model Organism Databases.

Model organisms are essential experimental platforms for discovering gene functions, defining protein and genetic networks, uncovering functional consequences of human genome variation, and for modeling human disease. For decades, researchers who use model organisms have relied on Model Organism Databases (MODs) and the Gene Ontology Consortium (GOC) for expertly curated annotations, and for access to integrated genomic and biological information obtained from the scientific literature and public data archives. Through the development and enforcement of data and semantic standards, these genome resources provide rapid access to the collected knowledge of model organisms in human readable and computation-ready formats that would otherwise require countless hours for individual researchers to assemble on their own. Since their inception, the MODs for the predominant biomedical model organisms [Mus sp. (laboratory mouse), Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Danio rerio, and Rattus norvegicus] along with the GOC have operated as a network of independent, highly collaborative genome resources. In 2016, these six MODs and the GOC joined forces as the Alliance of Genome Resources (the Alliance). By implementing shared programmatic access methods and data-specific web pages with a unified “look and feel,” the Alliance is tackling barriers that have limited the ability of researchers to easily compare common data types and annotations across model organisms. To adapt to the rapidly changing landscape for evaluating and funding core data resources, the Alliance is building a modern, extensible, and operationally efficient “knowledge commons” for model organisms using shared, modular infrastructure.

Open Astronomy and Big Data Science

AbstractOpen Astronomy is an important and valuable goal, including the availability of refereed science papers and user-friendly public astronomy data archives. The latter allow and encourage interested researchers from around the world to visualise, analyse and possibly download data from many different science and frequency domains. With the enormous growth of data volumes and complexity, open archives are essential to explore ideas and make discoveries. Open source software is equally important for many reasons, including reproducibility and collaboration. I will present examples of open archive and software tools, including the CSIRO ASKAP Science Data Archive (CASDA), the Local Volume HI Survey (LVHIS), the 3D Source Finding Application (SoFiA) and the Busy Function (BF). Astronomy is international and includes or links to an incredibly wide range of sciences, computing, engineering, and education. Its open nature can serve as an example for world-wide interdisciplinary collaborations.

Black hole mass estimation of ULXs and its dependence on model parameters

In the present work, we have analysed Chandra observational data of 38 galaxies which are obtained from the public data archive of Chandra X-ray Centre. 75 sources with data counts above 200 are considered for the analysis. The spectra of these sources were fitted using the absorbed disk blackbody model. Based on the estimated X-ray luminosity, we have categorized the sources into X-ray binaries (XRBs), ultraluminous X-ray sources (ULXs), extremely luminous X-ray sources (ELXs) and hyperluminous X-ray sources (HLXs). We estimate the black hole (BH) mass of all the point sources detected in the sample galaxies using the disk blackbody model, assuming that the inner disk radius, Rin ∼ 10GM/c2. It is found that most of the ULXs and ELXs harbouring stellar mass black holes are in hard states while those harbouring intermediate mass black holes (IMBHs) are in soft states. In case of HLXs, those harbouring IMBHs are found to be in hard states whereas a small population of soft sources are seen harbouring stellar mass black holes thereby accreting above their Eddington luminosity.

Content-based search of earth observation data archives using open-access multitemporal land cover and terrain products

Public Earth Observation (EO) data archives, e.g., MODIS, Landsat, and Sentinels, are valuable sources of information for a broad range of applications. For decision-supporting applications used in urban planning, land management, and sustainable development, images covering regions similar to the study area are prerequisites for high-accuracy decision making. These desirable images cannot be quickly searched for in the EO data archives via image metadata alone but can be obtained through content-based image retrieval methods. Land cover (LC) information, traditionally obtained through image segmentation or classification processing, is typically used in existing methods. Image processing is time consuming and has various accuracy levels for heterogeneous images, thus decreasing retrieval efficiency and accuracy. Additionally, the monotemporal LC information used has a limited ability to distinguish among confusable regions with different terrain, e.g., forests located on flatlands or mountains, and to obtain regions, e.g., urban regions, with similar growth rates. In this study, we employ free multiple-year 30 m LC products, a terrain product, and the Google Earth Engine (GEE) platform to accurately and efficiently locate the desired heterogeneous moderate spatial resolution images from various public EO data archives. Regions similar to the query region are detected with two-stage similarity calculations: First, monotemporal pixel-based LC and terrain information are used to filter out the most dissimilar regions; second, object-based LC change and terrain information are used to locate similar regions. Then, the desired images covering these detected similar regions are obtained from EO data archives via image metadata, e.g., geographical location and acquisition time. The experimental results of the two representative query regions show that our method can be used to obtain the desired images within several minutes and has higher accuracy than the LandEx method and a simplified method using only monotemporal LC information. The main contribution of our study is to reveal that LC changes and terrain information are helpful for improving the retrieval accuracy achieved from monotemporal LC information alone. Our method has great operability, with no need to perform EO data acquisition, image processing of raw EO images, or management of computational resources. Our method is conducive to making full use of images in various public EO archives to improve the decision making quality of decision-supporting applications.

Consistent dust and gas models for protoplanetary disks

Context. Consistent modeling of protoplanetary disks requires the simultaneous solution of both continuum and line radiative transfer, heating and cooling balance between dust and gas and, of course, chemistry. Such models depend on panchromatic observations that can provide a complete description of the physical and chemical properties and energy balance of protoplanetary systems. Along these lines, we present a homogeneous, panchromatic collection of data on a sample of 85 T Tauri and Herbig Ae objects for which data cover a range from X-rays to centimeter wavelengths. Datasets consist of photometric measurements, spectra, along with results from the data analysis such as line fluxes from atomic and molecular transitions. Additional properties resulting from modeling of the sources such as disk mass and shape parameters, dust size, and polycyclic aromatic hydrocarbon (PAH) properties are also provided for completeness. Aim. The purpose of this data collection is to provide a solid base that can enable consistent modeling of the properties of protoplanetary disks. To this end, we performed an unbiased collection of publicly available data that were combined to homogeneous datasets adopting consistent criteria. Targets were selected based on both their properties and the availability of data. Methods. Data from more than 50 different telescopes and facilities were retrieved and combined in homogeneous datasets directly from public data archives or after being extracted from more than 100 published articles. X-ray data for a subset of 56 sources represent an exception as they were reduced from scratch and are presented here for the first time. Results. Compiled datasets, along with a subset of continuum and emission-line models are stored in a dedicated database and distributed through a publicly accessible online system. All datasets contain metadata descriptors that allow us to track them back to their original resources. The graphical user interface of the online system allows the user to visually inspect individual objects but also compare between datasets and models. It also offers to the user the possibility to download any of the stored data and metadata for further processing.

MIReAD, a minimum information standard for reporting arthropod abundance data

Arthropods play a dominant role in natural and human-modified terrestrial ecosystem dynamics. Spatially-explicit arthropod population time-series data are crucial for statistical or mathematical models of these dynamics and assessment of their veterinary, medical, agricultural, and ecological impacts. Such data have been collected world-wide for over a century, but remain scattered and largely inaccessible. In particular, with the ever-present and growing threat of arthropod pests and vectors of infectious diseases, there are numerous historical and ongoing surveillance efforts, but the data are not reported in consistent formats and typically lack sufficient metadata to make reuse and re-analysis possible. Here, we present the first-ever minimum information standard for arthropod abundance, Minimum Information for Reusable Arthropod Abundance Data (MIReAD). Developed with broad stakeholder collaboration, it balances sufficiency for reuse with the practicality of preparing the data for submission. It is designed to optimize data (re)usability from the “FAIR,” (Findable, Accessible, Interoperable, and Reusable) principles of public data archiving (PDA). This standard will facilitate data unification across research initiatives and communities dedicated to surveillance for detection and control of vector-borne diseases and pests.

Early Career Researchers Embrace Data Sharing

A request for raw data from the corresponding authors of 771 animal biotelemetry-focused manuscripts, published between 1995 and 2015, highlighted a difference in data sharing practices across researcher career levels. Responses were positive in only 11% of requests made to corresponding authors (CAs) that were senior researchers, while 72% of responses were positive when CAs were early career researchers (ECRs), demonstrating that the majority of senior researchers perceived little benefit from the public data archiving of their published research, while they often remain the data custodian.

IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes.

The Integrated Microbial Genomes & Microbiomes system v.5.0 (IMG/M: https://img.jgi.doe.gov/m/) contains annotated datasets categorized into: archaea, bacteria, eukarya, plasmids, viruses, genome fragments, metagenomes, cell enrichments, single particle sorts, and metatranscriptomes. Source datasets include those generated by the DOE’s Joint Genome Institute (JGI), submitted by external scientists, or collected from public sequence data archives such as NCBI. All submissions are typically processed through the IMG annotation pipeline and then loaded into the IMG data warehouse. IMG’s web user interface provides a variety of analytical and visualization tools for comparative analysis of isolate genomes and metagenomes in IMG. IMG/M allows open access to all public genomes in the IMG data warehouse, while its expert review (ER) system (IMG/MER: https://img.jgi.doe.gov/mer/) allows registered users to access their private genomes and to store their private datasets in workspace for sharing and for further analysis. IMG/M data content has grown by 60% since the last report published in the 2017 NAR Database Issue. IMG/M v.5.0 has a new and more powerful genome search feature, new statistical tools, and supports metagenome binning.

Structural biology data archiving - where we are and what lies ahead.

For almost 50 years, structural biology has endeavoured to conserve and share its experimental data and their interpretations (usually, atomistic models) through global public archives such as the Protein Data Bank, Electron Microscopy Data Bank and Biological Magnetic Resonance Data Bank (BMRB). These archives are treasure troves of freely accessible data that document our quest for molecular or atomic understanding of biological function and processes in health and disease. They have prepared the field to tackle new archiving challenges as more and more (combinations of) techniques are being utilized to elucidate structure at ever increasing length scales. Furthermore, the field has made substantial efforts to develop validation methods that help users to assess the reliability of structures and to identify the most appropriate data for their needs. In this Review, we present an overview of public data archives in structural biology and discuss the importance of validation for users and producers of structural data. Finally, we sketch our efforts to integrate structural data with bioimaging data and with other sources of biological data. This will make relevant structural information available and more easily discoverable for a wide range of scientists.

Curation After the Fact: Practical and Ethical Challenges of Archiving Legacy Evaluation Data

Over a 12-year period, the Atlantic Philanthropies invested more than €127m in agencies and community groups, running 52 prevention and early intervention (PEI) programmes and services in the children and youth sector throughout Ireland. As a condition of this funding, each PEI programme was evaluated by a university-based research team, resulting in a substantial collection of metric and qualitative information about ways to improve the lives of vulnerable Irish families. In 2016, the Atlantic Philanthropies funded the Prevention and Early Intervention Research Initiative at the Children’s Research Network of Ireland and Northern Ireland (hereafter, the Initiative) to gather, prepare and share this evaluation data through the public data archives.
 The Initiative faces several challenges in its objective to archive this extensive collection of legacy data, and this paper will present two of the more salient challenges: how to share this data so that it is both (1) meaningful and (2) ethical. The paper pays particular attention to the challenges of safely sharing evaluation data through anonymisation and restricted access conditions; and also, the practical and ethical challenges of retroactively preparing these datasets for the archive.
 A series of publicly available documents that guide each stage of the Initiative are in development, and are emerging as a key output. This paper will describe two pivotal documents, namely the CRN-PEI Guiding Principles, and the CRN-PEI Protocols for preparing and archiving evaluation data. The CRN-PEI Guiding Principles outline the key legal and ethical obligations of archiving this legacy evaluation data, and act as moral compass to steer our progress through these uncharted waters. The CRN-PEI Protocols define the standards for how data included in the Initiative is prepared for deposition in the public data archives, so they are easily located, interpretable and comparable in the long term. This protocol is based upon best practice documentation from a number of international sources and our primary aim is to generate ‘safe, useful data’ (Elliot at al., 2016).

IMG/M: integrated genome and metagenome comparative data analysis system.

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support for examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review (ER) companion system (IMG/M ER: https://img.jgi.doe.gov/mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.

Gauging the Purported Costs of Public Data Archiving for Long-Term Population Studies

It was recently proposed that long-term population studies be exempted from the expectation that authors publicly archive the primary data underlying published articles. Such studies are valuable to many areas of ecological and evolutionary biological research, and multiple risks to their viability were anticipated as a result of public data archiving (PDA), ultimately all stemming from independent reuse of archived data. However, empirical assessment was missing, making it difficult to determine whether such fears are realistic. I addressed this by surveying data packages from long-term population studies archived in the Dryad Digital Repository. I found no evidence that PDA results in reuse of data by independent parties, suggesting the purported costs of PDA for long-term population studies have been overstated.

Public Data Archiving in Ecology and Evolution: How Well Are We Doing?

Policies that mandate public data archiving (PDA) successfully increase accessibility to data underlying scientific publications. However, is the data quality sufficient to allow reuse and reanalysis? We surveyed 100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. We suggest that cultural shifts facilitating clearer benefits to authors are necessary to achieve high-quality PDA and highlight key guidelines to help authors increase their data’s reuse potential and compliance with journal data policies.