Dealing with and Analyzing Legacy Data

The recent “30 by 30” global initiative to protect 30% of the world’s land and ocean by 2030 has increased the need for marine spatial planning decisions to be grounded in locally relevant empirical evidence. Continental shelves play a key role in the cycling of carbon, where marine sediments can act as an important sink of organic carbon (OC). As a result, marine sediments storing carbon have attracted recent scientific attention to elucidate the amount of OC stored and mechanisms influencing its sequestration. Spatial models of marine sediment OC stocks have previously been developed and provide preliminary estimates of standing stocks over wide geographical scales. However, these broad-scale predictions are derived from models of broad scale environmental regimes, which makes them unlikely to capture local spatial variations in environmental conditions and subsequently local variations in OC, reducing their utility for local scale marine spatial planning decisions. This study aims to determine whether legacy data could be used to produce local scale spatial predictions of OC relevant for policy makers. To achieve this aim, local scale predictors relevant for OC were produced/sourced in order to predict local-scale marine sediment OC in the Irish Sea. Legacy data of bottom water temperature (BWT) and bottom water salinity (BWS) measurements were used to bias correct and downscale global models of BWT and BWS. Recently developed high resolution sediment properties (% mud, % sand and % gravel) and locally developed Sediment Mobility and Sediment Disturbance Indices (SMI and SDI, respectively) were also sourced as potential predictors. Public-good, environmental consultancy and government agency repositories were also searched for OC-content data. A Random Forest model was trained to predict OC-content on localised predictors as well as previously identified important predictors of marine sediment OC. The outputs from the localised model were compared to one that was trained on broad-scale predictors to determine the change in model performance and utility for making local scale predictions. This study highlights the value of legacy data in contributing to locally refined spatial predictions of OC-content relevant for marine spatial planning decisions.

Accurate estimation of forest biomass and carbon stocks at regional to national scales is a key requirement in determining terrestrial carbon sources and sinks on United States (US) forest lands. To that end, comprehensive assessment and testing of alternative volume and biomass models were conducted for individual tree models employed in the component ratio method (CRM) currently used in the US' National Greenhouse Gas Inventory. The CRM applies species-specific stem volume equations along with specific gravity conversions and component expansion factors to ensure consistency between predicted stem volumes and weights, and additivity of predicted live tree component weights to match aboveground biomass (AGB). Data from over 76 600 stem volumes and 6600 AGB observations were compiled from individual studies conducted in the past 115 years – what we refer to as legacy data – to perform the assessment. Scenarios formulated to incrementally replace constituent equations in the CRM with models fitted to legacy data were tested using cross-validation methods, and estimates of AGB were scaled using forest inventory data to compare across 33 states in the eastern US. Modifications all indicated that the CRM in its present formulation underestimates AGB in eastern forests, with the range of underestimation ranging from 6.2 to 17 per cent. Cross-validation results indicated the greatest reductions in estimation bias and root-mean squared error could be achieved by scenarios that replaced stem volume, sapling AGB, and component ratio equations in the CRM. A change in the definitions used in apportioning biomass to aboveground components was also shown to increase prediction accuracy. Adopting modifications tested here would increase AGB estimates for the eastern US by 15 per cent, accounting for 1.5 Pg of C currently unaccounted for in live tree aboveground forest C stock assessments. Expansion of the legacy data set currently underway should be useful for further testing, such as whether similar gains in accuracy can be achieved in estimates of regional or national-scale C sequestration rates.

The Next Generation Linkage Management System (NGLMS) was designed around keeping all data in a graph database. However, this constraint, while easily achievable for greenfield projects and/or new data linkage units, may not be easily met where legacy data exists.
 Objectives and ApproachThe NGLMS was extended to encompassed system where data was held partially or even completely in a relational database. By grouping the data managed by the NGLMS into system metadata, record link data and record data and allowing system metadata and record data to be stored separately and independently in either a relational or a graph database, the NGLMS allows hybrid installations of mixed graph and relational data and, with some loss of functionality, purely relational installations.
 ResultsThe functionality of the NGLMS was expanded to allow review of existing legacy data stored in a relational database system. Through minor changes to the server used by the NGLMS Clerical Review tool (NGLMS-CR) the review tools was able to present the same interface and allow the same integration as project stored completely within the graph database.
 Hybrid projects where link information was stored in a graph could be accommodated with no loss of functionality.Relational-only projects allowed clerical review of identified clusters in a manner identical to the graph-only NGLMS but involved some curtailment of the advanced clustering, time-slicing, compositional and concurrent functionality of the NGLMS due to the loss of the functionality provided by a graph database. They do provide an upgrade pathway to hybrid projects and then graph-only projects.
 Conclusion / ImplicationsAllowing the NGLMS to be configured as a hybrid system enables a gradual adoption of the NGLMS toolset and software.
 
 purely relational data still allows the use of the NGLMS-CR with customisable workpools and workflows
 a hybrid relational/graph system where record data is still kept in a relational store but cluster and linkage information is kept in the graph store allows the use of legacy data without major disruption and provides a pathway to full adoption

Maintaining legacy data: Saving Belfast Harbour (UK) tide-gauge data (1901–2010)

We use legacy data from the Hubble Space Telescope (HST) and the International Ultraviolet Explorer (IUE) to search for a spectral line from the spontaneous decay of axion-like particle (ALP) dark matter. The HST data consist of blank sky observations taken with the Faint Object Spectrograph in the 165–240 nm wavelength range, while the IUE data consist of observations of the Virgo Cluster obtained with the long- and short-wavelength spectrographs, covering 195–325 nm and 123–200 nm, respectively.We set a 95% C.L. upper limit on the ALP-photon coupling gaγ ≲ 10-11 GeV-1 across the whole probed ALP mass range. Notably, we rule out values of gaγ above 2.3 × 10-12 GeV-1 for ALP masses between 12.4 and 14.5 eV, improving upon previous limits by a factor of seven.

This work presents the role of 3D visualization and analysis of monuments and archaeological sites in producing useful data regarding their preservation condition. The progress made in 3D digitization technologies, in combination with the development of new data processing algorithms, has enabled reliable and highly detailed digitization of the characteristics of different parts of the monuments. Due to both the effects of nature and human intervention, monuments and sites all over the world have undergone changes over time. The use of analog documentation data can help significantly towards this direction. In this work, we use as a case study a luxurious residential complex in the Athenian Agora, known as the Omega House. We use a retrospective 3D model, created with photographs taken in the late 60’s and early 70’s, in comparison with a 3D model made with contemporary digital photos, taken in 2017. All models are georeferenced. The old model is derived using analog terrestrial photographs and aerial photos taken by a blimp. The new one is created by terrestrial digital photographs in combination with images taken by an unmanned aerial vehicle, commonly known as a drone. The 3D models have been divided into smaller parts so that we can analyze them with greater accuracy separately, and then the whole models were compared as well. The Constructive Solid Geometry (CSG) modelling scheme is used and Boolean operations are applied to find the difference and intersection of the models. The comparison that is carried out in the current work elaborates on legacy data usefulness and their utility for monitoring the Omega House condition. The type of investigation proposed in this work proves that legacy data can be repurposed and can attain a new role through change detection techniques.

Iterative reengineering process is considered as an effective methodology to organize reengineering projects on degenerated but in-use legacy systems. By replacing legacy components gradually, the functionalities and requirements of legacy system can be satisfied. In practice, scheduling legacy components and data in every iterative cycle correctly is a great challenge to project managers, because of the complexities of dependency relations between legacy components and data. Formal Concept Analysis (FCA), which has strong mathematical capability of processing a group of elements with properties, is introduced to solve this issue. The major contribution of this paper is proposing an original Top-Down Schedule Algorithm (TDSA) based on FCA to compute an Iterative Cycle Reference Table (ICRT), which lists all potential cycles for an iterative reengineering project. The computed iterative cycles can be performed in any order without stopping legacy system.

Digital soil mapping of coarse fragments in southwest Australia: Targeting simple features yields detailed maps

We use new cosmic microwave background (CMB) primary temperature and polarization anisotropy measurements from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) to test foundational assumptions of the standard cosmological model, ΛCDM, and set constraints on extensions to it. We derive constraints from the ACT DR6 power spectra alone, as well as in combination with legacy data from the Planck mission. To break geometric degeneracies, we include ACT and Planck CMB lensing data and baryon acoustic oscillation data from DESI Year-1. To test the dependence of our results on non-ACT data, we also explore combinations replacing Planck with WMAP and DESI with BOSS, and further add supernovae measurements from Pantheon+ for models that affect the late-time expansion history. We verify the near-scale-invariance (running of the spectral index dns /d ln k = 0.0062 ± 0.0052) and adiabaticity of the primordial perturbations. Neutrino properties are consistent with Standard Model predictions: we find no evidence for new light, relativistic species that are free-streaming (N eff = 2.86 ± 0.13, which combined with astrophysical measurements of primordial helium and deuterium abundances becomes N eff = 2.89 ± 0.11), for non-zero neutrino masses (∑mν < 0.089 eV at 95% CL), or for neutrino self-interactions. We also find no evidence for self-interacting dark radiation (N idr < 0.134), or for early-universe variation of fundamental constants, including the fine-structure constant (α EM/α EM,0 = 1.0043 ± 0.0017) and the electron mass (me /me,0 = 1.0063 ± 0.0056). Our data are consistent with standard big bang nucleosynthesis (we find Yp = 0.2312 ± 0.0092), the COBE/FIRAS-inferred CMB temperature (we find T CMB = 2.698 ± 0.016 K), a dark matter component that is collisionless and with only a small fraction allowed as axion-like particles, a cosmological constant (w = -0.986 ± 0.025), and the late-time growth rate predicted by general relativity (γ = 0.663 ± 0.052). We find no statistically significant preference for a departure from the baseline ΛCDM model. In fits to models invoking early dark energy, primordial magnetic fields, or an arbitrary modified recombination history, we find H 0 = 69.9+0.8 -1.5, 69.1 ± 0.5, or 69.6 ± 1.0 km/s/Mpc, respectively; using BOSS instead of DESI BAO data reduces the central values of these constraints by 1–1.5 km/s/Mpc while only slightly increasing the error bars. In general, models introduced to increase the Hubble constant or to decrease the amplitude of density fluctuations inferred from the primary CMB are not favored over ΛCDM by our data.

ABSTRACTArchaeologists have struggled to combine remotely sensed datasets with preexisting information for landscape-level analyses. In the American Southeast, for example, analyses of lidar data using automated feature extraction algorithms have led to the identification of over 40 potential new pre-European-contact Native American shell ring deposits in Beaufort County, South Carolina. Such datasets are vital for understanding settlement distributions, yet a comprehensive assessment requires remotely sensed and previously surveyed archaeological data. Here, we use legacy data and airborne lidar-derived information to conduct a series of point pattern analyses using spatial models that we designed to assess the factors that best explain the location of shell rings. The results reveal that ring deposit locations are highly clustered and best explained through a combination of environmental conditions such as distance to water and elevation as well as social factors.

Regional till and weathered bedrock geochemical datasets provide a basis for data analysis and modelling in glaciated terrains. These large-scale surface geochemical datasets have great potential in mineral exploration, especially when machine learning and clustering methods are used to reduce the dimensionality of multivariate datasets. Here, self-organizing maps (SOMs) followed by k-means clustering were used to create SOMs of the target areas for initial modelling and prospectivity mapping of gold (Au) prospecting in central Lapland, northern Finland. Because the till and weathered bedrock datasets are legacy data, an effort was made to level the data between the map sheets. The targeting till geochemical dataset did not contain the typical indicator elements for Au. Instead, elements associated with the Au deposits, within the study area, were used. Indicator associations in this study were Ni–Co with possible Cu. Resulting elemental clusters from the SOMs were assigned as interesting clusters according to their distribution of elements. For the till, two potential clusters were identified: Ni–Co–Cr and Cu–V–Co. For the weathered bedrock, three clusters were specified: Ni–Co–Cr, V–Cu and Cu–Co. This study shows the potential of using legacy datasets for early targeting stages of mineral exploration, potentially reducing the footprint of mineral exploration.

This article argues that a holistic approach to documenting and understanding the physical evidence for individual cities would enhance our ability to address major questions about urbanisation, urbanism, cultural identities and economic processes. At the same time we suggest that providing more comprehensive data-sets concerning Greek cities would represent an important contribution to cross-cultural studies of urban development and urbanism, which have often overlooked relevant evidence from Classical Greece. As an example of the approach we are advocating, we offer detailed discussion of data from the Archaic and Classical city of Olynthos, in the Halkidiki. Six seasons of fieldwork here by the Olynthos Project, together with legacy data from earlier projects by the American School of Classical Studies at Athens and by the Greek Archaeological Service, combine to make this one of the best-documented urban centres surviving from the Greek world. We suggest that the material from the site offers the potential to build up a detailed ‘urban profile’, consisting of an overview of the early development of the community as well as an in-depth picture of the organisation of the Classical settlement. Some aspects of the urban infrastructure can also be quantified, allowing a new assessment of (for example) its demography. This article offers a sample of the kinds of data available and the sorts of questions that can be addressed in constructing such a profile, based on a brief summary of the interim results of fieldwork and data analysis carried out by the Olynthos Project, with a focus on research undertaken during the 2017, 2018 and 2019 seasons.

Recently the concept of big data has been employed in archaeology to push forward research on very large-scale legacy datasets, often with a developer-funded component; however, relatively little of this effort has focused on artefact and ecofact data. This paper explores the possibility of using Bowker and Star’s concept of the boundary object to manage the issues caused by data scale, complexity, diversity, and variable information standards when attempting to carry out large-scale research on artefacts and ecofacts. The critique of archaeological data standards as it impacts research into artefacts and ecofacts is reviewed. A methodology for the construction and use of a large database of legacy data is described, and a case study on the regional histories of food production/consumption in southern Britain, using datasets derived from the author’s PhD as part of the English Landscapes and Identities (EngLaId) Project (Gosden et al. 2021) at Oxford University is presented.

Abstract. SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology. The aim of this work was the production of global maps of soil properties, with cross-validation, hyper-parameter selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating state-of-the-art practices and adapting them for global digital soil mapping with legacy data. The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH (water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm). The quantitative evaluation showed metrics in line with previous global, continental and large-region studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high-latitude regions.

An analysis of conventional paper‐based botanical descriptions from Floras was undertaken as part of the development of MultiFlora, a system for the automatic production of a queryable database from such legacy data. The descriptions of five species of Ranunculus L. (buttercups and crowfoots) in six different English language Floras, from Europe and North America, show a surprising lack of uniformity in the suite of properties described. There is also considerable variation in the way property states are recorded. These findings have implications for the automatic production of taxonomic databases. This study is a proof of concept exercise, in which the taxa used are of negligible importance in themselves.