Reviewerly/exHarmony huggingface dataset:
Reimagining automated mineralogy for the 21st century

The phrase &amp;#8220;automated mineralogy&amp;#8221; has been synonymous with electron microscopy in the geosciences for decades. The use of energy dispersive spectroscopy (EDS) to rapidly map samples and identify phases of interest has gradually seen a shift out of its original industry applications and into the academic research environment. A major issue for academics wishing to take advantage of this powerful tool is the original platforms are rigid in terms of their industry designed outputs, and there has been a lack of development in both the software and hardware capable of providing automated outputs.However, rather than just looking backwards at what automated mineralogy was originally designed for, there is a more forward looking and important conversation as geoscience projects increase in scope. To think about geoscience in the context of topics such as big data, statistical relevance, and the use of increasingly prevalent machine learning techniques, we need to think about how we collect and store data. This naturally requires a greater integration of the problems geoscientists are trying to address with the solutions that microscopy and microanalysis equipment suppliers provide. Greater access to the data acquired on scientific equipment not only provides greater research flexibility but opens much smarter routes towards a future of correlated datasets with minimal user input.Using quantitative chemistry as the basis for automated mineralogy provides unique capabilities for large area analysis such as thin sections. Quantitative textural information can be extracted from the sample such as grain sizes, shapes, and mineral associations, alongside quantitative geochemical data providing mineral classification, including mineral and whole rock/sample compositions. This provides a wealth of information for the petrologist to understand their sample and a one-stop-shop for many geoscience workflows. This is a ready made mechanism to generate large datasets across multiple samples in a consistent fashion &amp;#8211; the key to big data.Here we show one example of greater integration of data acquisition with user generated computational software showing the power of large area quantitative EDS mapping with geoscience-oriented functions of XMapTools. By importing calibrated, quantitative EDS maps XMapTools can be used to rapidly perform a variety of petrological calculations without the need for a separate, long-winded calibration step using microprobe data. Here we use quantitative EDS from high grade metamorphic rocks to obtain mineral and bulk compositions alongside textural information such as modal abundances. These mapped data are imported directly into XMapTools and can be used to generate oxide values, cation per formula unit (cpfu), end member proportions, and perform thermodynamic calculations.
Short and long-term size changes of calcareous nannofossil Watznaueria barnesiae across the latest Barremian- late Cenomanian interval (Cretaceous) in the western Tethys.

Calcareous nannofossil Watznaueria barnesiae is a resistant and tolerant species and previous morphometric studies have evidenced minor (Erba et al., 2010; Lubke et al.2016) or no size changes during Oceanic Anoxic Events (OAEs) (Bornemann et al. 2006; and Faucher et al. 2017b) as well as during the Cenomanian to Maastrichtian (Linnert et al. 2014). Here, we present the morphometric record from the western Tethys (Cismon core from the Belluno Basin; Piobbico core and the Monte Petrano section from Umbria-Marche Basin, Italy) across the latest Barremian- late Cenomanian which represents a relatively long-time interval (ca. 26 Myrs) marked by interludes of profound changes in the ocean and climatic conditions named (OAE 1a, 1b and 1d). Our study shows that indeed W. barnesiae experienced coccolith and central unit variations during the studied interval. Specifically, smaller coccoliths with also smaller central unit marked the early Aptian to early Albian. The middle Albian to the early Cenomanian was characterized by larger and rounded coccoliths without significant changes in the central unit dimension. Short-term size changes are detected in correspondence of OAE 1a and OAE 1b marked by W. barnesiae size decrease. Particularly, OAE 1a was characterized by the smallest and most elliptical specimens of the studied interval. During the OAE 1b W. barnesiae coccoliths were characterized by a moderate decrease in the mean size, because specimens were already smaller before the event. The morphometric data were correlated with calcareous nannofossil temperature and nutrient indices calculated on the same samples investigated for morphometry. Long-term size changes do not show a clear correspondence with temperature and nutrient variations. W. barnesiae size changes across the early Aptian to early Albian may be related to large igneous province activity. Short-term size variations during OAE 1a and OAE 1b were influenced by the combination of large CO2 emissions, temperature and fertility increase. In conclusion, although W. barnesiae is a resistant and tolerant species it shows morphometric changes both in short- and long-term, probably in response to partially different causes.&amp;#160;&amp;#160;
Open Earth Engine Toolbox, code goodies and extension for Google Earth Engine

The Open Earth Engine Toolbox (OEET) is an innovative and highly effective suite of tools that makes it easier than ever to work with Google Earth Engine. Comprised of two main components - the Open Earth Engine Library (OEEL) and the Open Earth Engine Extension (OEEex) - the OEET is a true game-changer for anyone working in the field of geospatial analysis and data processing.The OEEL is a set of JavaScript code libraries that provide a wide range of functions and capabilities for working with Earth Engine. From advanced filtering techniques such as the Savitzky-Golay and Otsu algorithms, to powerful visualization tools like north arrows, map scales, mapshots&amp;#8230; the OEEL has everything you need to get the most out of Earth Engine. And with a convenient Python wrapper, it's easy to integrate the OEEL into your existing workflow, scripts or notebooks.But the OEEex is where the OEET really shines. This Chrome extension is designed you to work in tandem with the OEEL, unlocking a host of additional features and capabilities. One of the standout features of the OEEex is its ability to all run tasks in a single step. This is particularly useful for those working with large datasets or for those who need to perform the same tasks repeatedly. With the OEEex, you can simply set up your tasks and then let the extension handle the rest, saving you time and effort of clicking on each. The OEEex also offers a range of customization options for the interface. These include the ability to switch to a dark mode, which can be easier on the eyes during long work sessions, as well as the ability to adjust font sizes to suit your personal preference. It allows to utilize Plotly within Earth Engine to get beautiful figure, and much more.Overall, the OEET is an essential tool for anyone looking to get the most out of Google Earth Engine. Its powerful JavaScript libraries, convenient Python wrapper, and feature-rich Chrome extension make it the go-to choice for geospatial analysis and data processing.
Implementasi Agile Scrum Pembuatan Game Visual Novel Cerita Asal Usul Kota Surabaya

Surabaya is the second largest city in Indonesia after Jakarta. The city of Surabaya has a distinctive designation, namely "the city of heroes" because of the great support from the people of Surabaya in fighting for Indonesian independence. The lack of interest of Indonesia's younger generation in Indonesian folklore and their preference for video games and other advanced technology, does not rule out the possibility that history will be forgotten and slowly extinct because it is only passed down orally. The purpose of this research is to implement the agile-scrum method in making visual novel games as an effort to attract the interest of the younger generation to know and preserve the history of the origins of the city of Surabaya with a modern approach and according to the target audience. The visual elements used in this game are costume details, props, poses and various expressions according to modified references. In addition, the narration/storyline used has also been developed with a story style and choice of words that are relevant to the target user. The method used in this final project is agile-scrum which has 8 stages of development, starting from the stage of exploring problems that can become product ideas to the final testing stage. From the results of the final test, the creation of a visual novel game based on the story of the origins of the city of Surabaya can be used properly by users.
Analisis Pengaruh Komposisi Data Training dan Data Testing pada Penggunaan PCA dan Algoritma Decision Tree untuk Klasifikasi Penderita Penyakit Liver

Liver disease is one of the diseases that is difficult to detect and becomes the largest contributor to deaths because it is considered a silent killer without symptoms. Liver disease can be detected based on abnormalities in the number of contents in the human body. The Indian Liver Patient Dataset (ILPD) dataset has many variables related to content in the body of liver patient data which are used as parameters in the classification of liver disease patients. Previous studies have shown that only two variables influence the ILPD dataset. The purpose of this study is to examine the use of the Principal Component Analysis (PCA) method to determine the optimal number of features in the context of classification of liver disease and examine the percentage distribution of data training and data testing which produces the best accuracy. The ILPD dataset was obtained from the UCI Machine Learning website with a total of 583 rows of data and 11 features. The percentage of training data and testing data used is 50%:50%, 60%:40%, 70%:30%, 73%:27%, 75%:25%, 80%:20%, 83%:17%, 85%:15% and 90%:10%. The use of different training and testing data percentages serves to find the best accuracy. The PCA result feature is used as input for the Decision Tree C4.5 classification algorithm. The experimental results show that using the training and testing data distribution percentage of 90%:10% and after the application of PCA produces the highest accuracy, namely 78.40% which is obtained for the number of PCA components n = 8.
Implementasi Algoritma Term Frequency Inverse Document Frequency (TF-IDF) dalam Menganalisis Sentimen Masyarakat Terhadap Covid-19 Varian Omicron

The latest variant was detected on November 24, 2021, namely the Omicron variant WHO said, Omicron was one of the Covid-19 variants that had mutated, with a very fast spread rate. The Government Republic of Indonesia has officially banned all foreigners from entering Indonesia, both those who have done so travel or come from countries exposed to the Omicron variant. This study uses data that has been processed using Netlytic online website. Netlytic analyzes text and visualizes public online conversations on social media sites. text preprocessing has several stages, namely case folding, tokenizing, stopword, stemming. Data analysis is the stage to classify words into positive, negative, or neutral sentiment classes. the last step is calculating the weights using the tf-idf method. It is proven from the DF value which reaches 628 words in one document, the D/DF value is 0.39 and the log D/DF is -0.41. The TF-IDF method can be taken in outline, namely it is easy to calculate frequency and relevance occurrence of words in a document. The TF-IDF method produces output according to user specifications, but this method takes a long time for large amounts of data.
Prediksi Status Gizi Balita Dengan Algoritma K-Nearest Neighbor (KNN) di Puskemas Cakranegara

Nutritional status is a picture of a person's physical condition as a reflection of the balance of incoming and outgoing energy by the body. Determining the nutritional status of toddlers is useful for knowing the nutritional status of toddlers based on weight/age (weight for age). The system designed is a system for determining the nutritional status of toddlers using the K-Nearest Neighbor (KNN) method, where the KNN method is a method of classifying or grouping test data whose class is unknown to the nearest neighbors using the distance calculation formula. The variables used in this system are based on anthropometric data or measurements of the human body, namely gender, age and weight. This system is designed and built using the PHP programming language and MySQL database. The results of this system are nutritional status based on body weight for age (weight for age), namely malnutrition, undernutrition, good nutrition, over nutrition. Based on the test results, the accuracy of the success rate for determining the nutritional status of toddlers using the KNN method produced by this system reaches 88.06%.
Towards a harmonized data ecosystem in Earth and Environment– a view on the Helmholtz Association’s Data Infrastructures

The interconnectivity of existing data infrastructures (DIS) across national and international initiatives (e.g. NFDI, EOSC and others) is an important goal to create a common interoperable data ecosystem. To achieve this, it is critical to harmonize the existing methods and concepts of research data collection, research data-reuse, among the DIS and along the FAIR principles.Within the Helmholtz Association we maintain more than 50 data infrastructures in the field of Earth and Environment. Procedures of data handling, documentation and storage are hardly coordinated within Helmholtz, even less so within the larger community. To find out about the state of our infrastructures, the different approaches in data management procedures, technical capabilities, and concepts, we conducted a survey among all Helmholtz DIS. We asked questions related to their roles in the community, self-perception, quality control, curation, technology interfaces, data re-use and demands.Based on this data we developed our vision to create a &amp;#8220;Helmholtz data space&amp;#8221;, unifying Earth and Environmental Centres and infrastructures and powering a new wave of large-scale, globally oriented, data driven research. The Helmholtz Metadata Collaboration&amp;#8217;s (HMC) mission is, to federate (meta)data systems across Earth and Environment Centres and infrastructures throughout the Helmholtz Association, continuously aligning Helmholtz capacities to global norms and developments.
ORFEUS Data Services, Products and Actions to Coordinate Access to Seismic Waveform Data in the Euro-Mediterranean Region

ORFEUS (Observatories and Research Facilities for European Seismology, orfeus-eu.org) is a non-profit foundation that coordinates and promotes seismology in the Euro-Mediterranean area and beyond, through harmonized collection, archival and distribution of seismic waveform data, metadata, alongside offering services and products managed at national level by more than 60 participating seismological Institutions. ORFEUS is one of the founding members of EPOS Seismology (www.epos-eu.org/tcs/seismology) and a fundamental partner of EC-funded projects. ORFEUS services are largely integrated in the EPOS Data Access Portal (www.ics-c.epos-eu.org). The key goals of ORFEUS (Bylaws, Article I.3: orfeus-eu.org/documents//ORFEUS_Bylaws_September_2022.pdf) are achieved through the development and maintenance of data services targeted to a broad community of seismological data users. ORFEUS comprises: (i) the European Integrated waveform Data Archive (EIDA; orfeus-eu.org/data/eida); (ii) the European Strong-Motion databases (orfeus-eu.org/data/strong); and iii) the recently established group representing the community of European mobile pools, including amphibian instrumentation (orfeus-eu.org/data/mobile). Products and services for computational seismology are also considered for integration in the ORFEUS domain. Currently, ORFEUS services&amp;#160; provide access to the waveforms acquired by ~18,000 stations in the Euro-Mediterranean region, including dense temporary experiments (e.g., AlpArray, AdriaArray), with strong emphasis on open, high-quality data. Access to data and products is ensured through state-of-the-art information and communication technologies, with strong emphasis on federated web services, clear policies and licenses, and acknowledging the crucial role played by data providers. Significant efforts are underway, by ORFEUS participating institutions,&amp;#160; to enhance the existing services to tackle the challenges posed by the Big Data Era, and to actively encourage interoperability and integration of multidisciplinary datasets in seismological and Earth Science workflows.&amp;#160; ORFEUS also implements Community services that include software and travel grants, webinars, workshops and editorial initiatives. ORFEUS data and services are assessed and improved through the technical and scientific feedback of a User Advisory Group, which comprises European Earth scientists with expertise on a broad range of disciplines.&amp;#160;
What does it mean to be a data researcher and platform facilitator of crowdsourced weather observations?

In 2011 the UK Met Office established the Weather Observations Website (WOW) initiative, a global-coverage project in which users of personal weather stations (PWS) can contribute their weather observations to a central repository. In this decade, more than 10,000 PWS around the world have contributed 2 billion measurements to this project, with a remarkable presence of WOW users in Europe. The Dutch Met Office (KNMI) joined this initiative as partner in 2015. In the past 8 years, 1,000+ PWS located in the Netherlands have collected 250+ million observations of the most relevant weather variables, and the interest of the Dutch public in this network continues growing.&amp;#160; In this context, the KNMI has two main roles with respect to WOW-NL observations: Platform Facilitator and Data Researcher. The KNMI facilitates WOW-NL to the public via the portal http://wow.knmi.nl, which enables visualizing the latest observations in a map, allows querying to inspect the historical data contributed by each station, and provides a space for news. As platform facilitator, the KNMI aims for a measurement system of PWS that provides optimal added value to our science and services. The Data Research teams at KNMI have dedicated continuous efforts to develop quality controls (QC) enabling a full quality assessment of the WOW-NL observations. The latest results show that the application of QC methods yields promising results for air temperature, rainfall, and wind speed measurements. This means that WOW-NL observations may have sufficient quality to be incorporated into successive research or operational workflows and become part of the &amp;#8216;daily business&amp;#8217; of the organization.&amp;#160; These two roles are designed to work independently, but we believe that bringing them together would positively and effectively impact quality of data for the organization&amp;#8217;s science and services. Hence, how can we interlace them most optimally in a feedback loop and take them to the next level? How can we expand the Platform Facilitator role, to stimulate and provide guidance for citizens to obtain quality of crowd sourced data most optimal for our science and services? How to enable the Data Researcher role to deliver peer-reviewed scientific content to a broader audience and in a real-world set up? Last but not least, how to establish a dialogue with the users to create a community ensuring long-term data provision for national meteorological services? In this work we investigate the relationship between the Platform Facilitator and the Data Researcher roles to balance investment in actions &amp;#8220;upstream&amp;#8221; (e.g. network design, PWS location) vs &amp;#8220;downstream&amp;#8221; (e.g. metadata, statistical QC procedures). We also elaborate on how the inclusion of WOW-NL in operational workflows might require revisiting or creating new policies for crowdsourced data or assessing the readiness of the digital infrastructure of the organization.
LithoPlates - a new deep-time reconstruction community service provided by the AusGeochem data platform

AusGeochem, together with the Earthbyte Group and Lithodat, has developed a cloud-based, fully integrated deep time reconstruction tool for geochemistry data based on Earthbyte&amp;#8217;s pyGplates and plate models. This new tool is easy to use and enables researchers to visualise and analyse samples and analytical results in their palinspastic context. On-the-fly analytical tools currently included in AusGeochem such as live contouring and multi-sample selections can now be performed in their paleogeographic locations without any further data preparation. The ease of use will enable researchers to explore the world of deep-time reconstructions and enhance their understanding of the tectonic settings and events of importance to rock, mineral or fluid sample history. The current version enables the user to select between 7 different plate models ranging back to 1 Ga. All images and results can be exported for further analysis and processing. &amp;#160; Here we present how this new tool can be used and how it is integrated into AusGeochem, Australia's public geochemistry data platform for FAIR data. Geochronology data can be reconstructed back in time to help understand when and where rocks were formed or deformed, and how they are related in a paleogeographic context. Filters can be applied to make sure only data of age relevance are shown in any given reconstruction timeslice. The advantage of having the new reconstruction function fully integrated into AusGeochem&amp;#8217;s relational database is that all data and metadata can be analysed using the same on-the-fly tools in both present-day and palinspastic geography. . This new tool is primarily designed for researchers interested in the paleogeographic context of their samples, but also for plate model scientists seeking to integrate all available geochronology and thermochronology data to help better constrain and improve existing plate models. Future enhancements will include the addition of more deep-time plate models, advanced visualisation and filters and a comparison of multiple model outputs.&amp;#160;