Reviewerly/exHarmony huggingface dataset:
FAIR Notebooks: opportunities and challenges for the geoscience community

Making assets in scientific research Findable, Accessible, Interoperable and Reusable (FAIR) is still overwhelming for many scientists. When considered as an afterthought, FAIR research is indeed challenging, and we argue that its implementation is by far much easier when considered at an early stage and focusing on improving the researchers' day to day work practices. One key aspect is to bundle all the research artefacts in a FAIR Research Object (RO) using RoHub (https://reliance.rohub.org/), a Research Object management platform that enables researchers to collaboratively manage, share and preserve their research work (data, software, workflows, models, presentations, videos, articles, etc.). RoHub implements the full RO model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable. This approach provides the technical basis for implementing FAIR executable notebooks: the data and the computational environment can be &amp;#8220;linked&amp;#8221; to one or several FAIR notebooks that can then be executed via EGI Binder Service with scalable compute and storage capabilities. However, the need for defining clear practises for writing and publishing FAIR notebooks that can be reused to build upon new research has quickly arised. This is where a community of practice is required. The Environmental Data Science Book (or EDS Book) is a pan-european community-driven resource hosted on GitHub and powered by Jupyter Book. EDS Book provides practical guidelines and templates that help to translate research outputs into curated, interactive, shareable and reproducible executable notebooks. The quality of the FAIR notebooks is ensured by a collaborative and transparent reviewing process supported by GitHub related technologies. This approach provides immediate benefits for those who adopt it and can feed fruitful discussions to better define a reward system that would benefit Science and scientific communities. All the resources needed for understanding and executing the notebook are gathered into an executable Research Object in RoHub. To date, the community has successfully published ten FAIR notebooks covering a wide range of topics in environmental data science. The notebooks consume open-source python libraries e.g. intake, iris, xarray, hvplot for fetching, processing and interactively visualising environmental research.&amp;#160; While these notebooks are currently python-based, EDS Book supports other programming languages such as R and Julia, and we are aiming at engaging with computational notebooks communities alike towards improving the research practices in environmental science.
Data-RAS A National Digital Soil Radioactivity Map

The Council Agricultural Research and Economics, Research Centre for Agriculture and Environments of Florence, (CREA-AA), National Soil Archive (ARCAN) is an important resource for current and future soil research. ARCAN holds 32,612 archived specimens, collected from 13,156 sites across Italy. The ARCAN is also very relevant from a historical and agroecological point of view, collecting soil samples from surface and subsurface horizons, in some cases taken before Chernobyl and almost all after Chernobyl (1986-2021). In particular, 7,000 agricultural samples are representative of all regions of Italy, collected in the same year (1994), with the same standard sampling and analysis protocol. In this work we show up a new database management system (DATA-RAS) to achieve Vis-Nir spectroscopy data and environment radioactivity data on ARCAN soil samples. Combined use of Vis-Nir and gamma ray spectroscopy to predict soil properties offers a task to populate the database. After that it is possible to obtain a digital soil map of artificial or natural radioactivity of Italian soils. The main purpose of this work is show up an Entity Relationship model of DATA-RAS, that represents other than a database a Smart Data Exchange a system to better managing ARCAN and rapidly exchanging information with other national and European partners. The ultimate goal is to use the most advanced information and communication technologies to create an innovative platform that digitally manages open data with 3D methods and augmented and virtual reality techniques (AR and VR). The DATA-RAS platform will be integrated with scientific information systems and a natural language interface for knowledge communication.
LithoNet: A benchmark dataset for machine learning with digital outcrops

Deep learning techniques are increasingly used to automatically derive geological maps from digital outcrop models, lessening interpretation time and (ideally) reducing bias. Such techniques are especially needed when hyperspectral images are back-projected to create data-rich &amp;#8216;hypercloud&amp;#8217; type digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge, due to the subjective nature of geological mapping and difficulty collecting quantitative validation data. This makes validation of different machine learning approaches for geological applications exceedingly difficult. Furthermore, many state-of-the-art deep learning methods are limited to 2-D image data, making application to 3-D digital outcrops (e.g., hyperclouds) an outstanding challenge.&amp;#160;In this contribution we present LithoNet, a benchmark digital outcrop dataset designed to (1) quantitatively compare learning approaches for geological mapping, and (2) facilitate the development of new approaches that are compatible with non-structured 3-D data (i.e., point clouds). LithoNet comprises two halves: a set of real digital outcrop models acquired at Corta Atalaya (Spain), attributed with different spectral and ground-truth data, and a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground-truth. We have used these datasets to explore the abilities of different machine learning approaches for automated geological mapping. By making it public we hope to foster the development and adaptation of new machine learning tools.
Because it matters: Benefits of using the domain repository GFZ Data Services for Earth System Sciences data

Implementing the FAIR principles is becoming more and more relevant for the scientific community and in particular for the Earth System Sciences. It is already widely acknowledged that research data inhibit not only relevant information for their respective field of study but can open new avenues of research when appropriately re-used and/or combined with other information. At this point research data repositories represent key data access points and especially domain repositories with careful data curation and enrichment of metadata are increasingly relevant and valuable.GFZ Data Services, hosted at the GFZ German Research Centre for Geosciences (GFZ), is a domain repository for geosciences data that assigns digital object identifier (DOI) to data and scientific software since 2004 and is Allocating Agent for the International Generic Sample Number IGSN, the globally unique persistent identifier for physical samples. The repository, on one hand, provides DOI minting services for several global monitoring networks/observatories in geodesy and geophysics (e.g. INTERMAGNET; IAG Services ICGEM, IGETS, IGS; GEOFON), collaborative projects (TERENO, EnMAP, GRACE, CHAMP) and has a strong focus long-tail data on the other hand. All metadata and data are curated by domain scientist.Especially the provision of (i) comprehensive domain-specific standardised and machine-actionable metadata with linked-data vocabularies used in the geosciences domain and (ii) comprehensive technical data descriptions or DOI-referenced data reports complementing the metadata, result in high-quality data publications that are easily discoverable across domains. Furthermore, the provision of cross-references through persistent identifiers (DOI, IGSN, ORCID, Fundref, ROR) to related research products (text, data, software, people and institutions) further increase the visibility and interoperability of research data.Next to curation workflows for data and metadata realised by domain experts, GFZ Data Services offers detailed and thorough user guidance via its website (https://dataservices.gfz-potsdam.de). This website is the central information and access point for the repository and provides the data and sample catalogues, information on metadata, data formats, the data publication workflow, FAQ, links to different versions of our metadata editor, downloadable data description templates and general information on data management practices.
Relationship between coseismic ground deformation and geological structures associated with the 2016 Mw 6.5 central Italy earthquake

&amp;lt;p&amp;gt;In the last three decades, remote sensing techniques, such as Differential Synthetic Aperture Radar Interferometry (DInSAR), have been exploited for investigating, with high accuracy, ground displacement phenomena. Large seismic events can trigger deformations at the surface, which are controlled by the active faults and the intercepted lithologies.&amp;lt;br /&amp;gt;In 2016-2017, a long earthquake sequence struck the Apennines in central Italy, producing impressive surface ruptures attributed to the 24 August Mw 6.0 and 30 October Mw 6.5 mainshocks. These ruptures were investigated and mapped by field geologists soon after the earthquakes, and during the following years also by remote sensing data.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;We present detailed maps of the surface deformation pattern produced by the M. Vettore Fault System (VFS) during the October 2016 earthquakes. The DInSAR analysis has been retrieved from ALOS-2 SAR data, via the Parallel Small BAseline Subsets (P-SBAS) algorithm. At the local scale, we identify a large number of surface ruptures, most of which already observed in the field. At the large scale, we trace a set of five geological cross-sections to inspect a possible link between the coseismic vertical displacement, the lithology distribution and the tectonic structures of the area (i.e., thrusts, normal faults). On these sections, we also project the seismicity distribution recorded during October 2016.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;The integration of such datasets allows the recognition of an important geological control in the overall distribution of the deformation, which shows maximum values in correspondence of the carbonatic multilayer and minimum values within the clastic succession. The distribution of seismicity allows also us to distinguish seismogenic by aseismic slip associated with fault ruptures.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Along the sections, we observe a typical long-wavelength convex curvature of the subsiding block, not directly recognizable in the field.&amp;amp;#160; In the area of maximum subsidence, this curvature is interrupted by an anthitetic fault at which is not associated with any seismicity. In addition, we observe that further deformation is localized at the footwall of the VFS, corresponding to the hangingwall block of an important thrust fault, where shallow seismicity was also recorded. Here, we observe that the coseismic deformation tends to decrease toward the outcropping thrust. In the south sector, instead, we do not observe a control of regional thrusts acting as a barrier to the deformation.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;The results of this work demonstrate that the integration of surface geology, remote sensing data and seismicity, can lead to a better understanding of the influence of geological structures on the distribution of the surface deformation associated with earthquakes.&amp;lt;/p&amp;gt;
Measuring bedload motion time at sub-second scale using Benford's law from long-term acoustic recordings

Bedload transport is a natural process that strongly affects the Earth&amp;#8217;s surface system. An important component of quantifying bedload transport and establishing early warning systems is obtaining the parameters at the onset of bedload motion. Bedload transport can be monitored with passive acoustic methods, e.g., hydrophones. Yet, an efficient method for identifying the onset of bedload transport from long-term continuous acoustic data is still lacking. Benford&amp;#8217;s Law defines the specific frequency distribution of the first digits of datasets that have been used to distinguish stochastic from chaotic processes in nature when this process causes higher energy events than baseline. Here, we apply Benford&amp;#8217;s law to continuous acoustic recordings from Baiyang hydrometric station, a tributary of Liwu River, Taiwan at the frequency of 32 kHz from stationary hydrophones deployed for three years since 2019. We construct a workflow to parse sound combinations of bedload transportation and analyze them in the context of hydrometric sensing constraining the onset, and recession of bedload transportation. We identify two bedload transportation events that lasted 17 and 45 hours, respectively, covering about 0.35% of the time per year. Our workflow allows filtering 99% of background signal and focuses on two events including bedload motions. Given that fluvial seismology has successfully monitored fluvial processes, continuous monitoring in three directions (N-S, W-E, vertical) brings board discussion orientations, e.g., the direction of source or migration of mass movement. Therefore, we suggest that the application of Benford&amp;#8217;s law on seismic data of Earth's surface processes has great potential.
Highlighting the metasomatic impact on mottled UG-2 chromitites from the Bushveld Complex (South Africa) by large-scale µEDXRF mapping.

The use of &amp;#181;EDXRF elemental mapping provides access to data on relatively large core sections within a reasonable time at high spatial resolution down to 20 &amp;#181;m, enabling to link macroscopic to microscopic&amp;#160;information, and providing an objective tool to select areas of interest for more sophisticated data acquisition by EPMA, LA-ICP-MS etc. Textural features can be visualized, that hardly are identified with the naked eye e.g. interstitial silicates in massive chromitites of the Bushveld Complex, South Africa. The application of automated mineralogy provides access to local paragenetic changes and to modal analyses of selected areas. Automated mineralogy based on &amp;#181;EDXRF has to overcome a number of obstacles due to aspects of diffraction, depth of information and grain boundary effects. Tools have been developed to limit these aspects within an acceptable error frame by combining the information of two opposing detectors to reduce the side effects of diffraction. By applying a supervised endmember based classification using the&amp;#160;spectral angle mapper algorithm of the ENVI hyperspectral software, phase distribution maps can be produced. Within a well-known system such as the UG-2 chromitite mineral names can be attributed to the identified phases. Exceptions exist for very fine grained secondary phases which might show mixed signals. Well-identified phases can be segmented and grain size, shape and orientation of individual grains can be obtained supported by&amp;#160;diffraction signals of single grains. Chemical information can further be extracted for individual minerals, individual grains and bulk area corresponding to the modal mineralogy for any selected area. This offers a new approach to interprete (Verb fehlt?) complex textures by comparing chemical and mineralogical aspects of individual textural pattern. The example of mottled UG-2 chromite shows that the hosting silicates of the chromititebasically orthopyroxene and anorthite-rich plagioclase, but within stringers phlogopite, anorthite-poor plagioclase, potassic feldspar, amphibole, quartz with local enrichment of apatite and sulphides, show differences in grain size and chemistry of the chromite. EPMA investigations on chromite show that Cr/(Cr+Al), Mg/(Mg+Fe), and Cr/Fe is controlled by the chemistry of its hosting oikocryst silicates plagioclase or orthopyroxene. The appearance of late inter-oikocryst phlogopites induces a metasomatic loss of titanium in the chromite. By applying several steps of &amp;#181;EDXRF data reduction and phase masking, these changes in chromite chemistry can be visualized despite of the relatively large spot size of 20 &amp;#181;m for large areas. Using this information the metasomatic impact within a continuously &amp;#181;EDXRF mapped half drill core can be visualized and quantified.&amp;#160;
Probabilistic climate emulation with physics-constrained Gaussian processes

Emulators, or reduced complexity climate models, are surrogate Earth system models that produce projections of key climate quantities with minimal computational resources. Using time-series modeling or more advanced machine learning techniques, statistically-driven emulators have emerged as a promising venue, producing spatially resolved climate responses that are visually indistinguishable from state-of-the-art Earth system models. Yet, their lack of physical interpretability limits their wider adoption. In fact, the exploration of future emission scenarios still relies on one-dimensional energy balance models which can lack the flexibility to capture certain climate responses.Here, we propose a statistically-driven emulator that hinges on an energy balance model. Using Gaussian processes, we formulate an emulator of temperature response that exactly satisfies the physical thermal response equations of an energy balance model. The result is an emulator that (1) enjoys the flexibility of statistical machine learning models and can be fitted against observations to produce spatially-resolved predictions (2) has physically-interpretable parameters that can be used to make inference about the climate system. This model shows skilfull prediction of annual mean global distribution of temperature, even over scenarios outside the range of observed emissions of greenhouse gases and aerosols during training &amp;#8212; improvement in RMSE of 27% against the energy balance model and of 60% against a physics-free machine learning model. In addition, the Bayesian nature of our formulation provides a principled way to perform uncertainty quantification on the predictions.We outline how the probabilistic nature of this emulator makes it a natural candidate for detection and attribution studies and discuss extension to a precipitation emulator also incorporating physical constraints. We hope that by combining the ability of machine learning techniques to capture complex patterns with the interpretability of energy balance models, our work will produce a reliable tool for efficiently and accurately exploring future climates.
Earthquake Detection and Location in the Cameroon Temporary Network Data Using Deep Learning

A temporary seismic network consisting of 32 broadband seismic sensors was installed in Cameroon between March 2005 and December 2006 to study the seismic structure of the crust and upper mantle beneath the Cameroon Volcanic Line (CVL). This study aims to re-evaluate the seismicity in this period by processing this database and calculating an updated crustal velocity model for the region incorporating the acquired earthquake bulletin.&amp;#160;The earthquake detection and location procedure applies hybrid deep learning (DL) and phase validation methods. We use an integrated workflow composed of Earthquake Transformer (EqT) and Siamese Earthquake Transformer (S-EqT) for initial earthquake detection and phase picking. Then, PickNet is used as a phase refinement step, and REAL for earthquake association and rough location. A set of thresholding parameters for earthquake detection and P- and S-picking equal to 0.2 and 0.07 are adjusted, respectively. By combining a set of 33282 P and 29251 S-picked phases associated with 743 earthquakes with 1.3 &amp;#8804; ML &amp;#8804; 4.6, we implement a joint inversion for estimating an updated 1D crustal velocity model. The obtained mode comprises thicknesses of 8, 12, 14, 20, and 30km, from the surface to a depth of 45km, with Vp = 6.1, 6.4, 6.6, 7.6, 8.25, and 8.5km/s, respectively. The newly detected events are primarily concentrated in three main clusters, 1) the east flank of Mount Cameroon, 2) an area between Mount Cameroon and Bioko Island, and 3) southern Bioko Island. The compiled catalog for this time interval is 1.7 times larger than the already reported catalog for this data set. Finally, we present a 3D time-lapsed animation of the detected earthquake sequences.Acknowledgements: The authors would like to thank the support of the Instituto de Telecomunica&amp;#231;&amp;#245;es. This work is funded by FCT/MCTES through national funds and, when applicable co-funded EU funds under the project UIDB/50008/2020.
Access to facilities, FAIR data and related services: the ITINERIS project

The Italian Integrated Environmental Research Infrastructures System (ITINERIS) Project started in November, and it will build the Italian Hub of Research Infrastructures in the environmental scientific domain providing access to data and services and supporting the Country to address current and expected environmental challenges. ITINERIS coordinates a network of national nodes from 22 RIs (18 from the environmental domain, 2 from agri-food with strong link with the environment and 2 from the PSE domain, supporting services for the marine domain).ITINERIS has been designed looking at synergy with the European RI framework, and it will support the participation of Italian scientists in pan-European initiatives (ENVRI-FAIR, EOSC) and in HE (Pillar 1, Missions, Partnerships, Clusters). ITINERIS will have significant impact on national environmental research, providing scientific support to the design of actionable environmental strategies. ITINERIS adopts a whole-system, cross-disciplinary approach to the Earth System and its changes, allowing users to benefit from the integrated system of RIs and the knowledge it produces. This broad-scale vision of environmental research, sustained by the main Italian environmental scientists involved in European RIs, is truly innovative and it will support our Country in taking a leading role in European environmental research, designing the framework for the next decades.A specific objective will be focused on the access to facilities, FAIR data and related services connecting the established network of distributed national environmental research infrastructures to as wide as possible user community. Following a user-centric approach and in accordance with the RIs&amp;#8217; network technical capability and mission, access services to the national RIs&amp;#8217; facilities and FAIR resources (data, services and other research outputs) will be set up. Most of the 22 RIs participating in ITINERIS already offer data and services from and across different domains of the Earth system - Atmosphere, Hydrosphere, Terrestrial Biosphere and Geosphere through different systems, protocols, portals and different access and FAIRification procedures.ITINERIS builds upon this current effort to improve Access management and FAIRness, developing the conditions for harmonizing standards, metadata and policies amongst the different RIs. It has to be considered that involved RIs are very diverse and on different levels of maturity but face similar challenges in their operations in regard to FAIR compliance and Access management.