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The dimension of open science entitled open data requires actions from all actors of scientific communication and scientific publishers have a responsibility to increase the editorial policy by providing the minimum requirements to guide authors and reviewers in this process. For this reason, it investigated the situation of the policies of journals in the field of Communication and Information regarding the aspects of publication of scientific data. From the universe of 1804 journals in the Qualis Periodicals (2016), 46 journals in the A1 stratum were selected to be analyzed. The result showed that 52% of the journals have adopted editorial policies for sharing research data, however, less than half have guidelines for authors on how to cite and credit scientific data. It is concluded that despite policy incongruence with author guidelines regarding data citation, persistent identifier and licenses for use, reuse, sharing and distribution, journals are trying to keep up with open publishing trends., La dimensión de la ciencia abierta titulada datos abiertos requiere acciones de todos los actores de la comunicación científica y los editores científicos tienen la responsabilidad de aumentar la política editorial proporcionando los requisitos mínimos para guiar a los autores y revisores en este proceso. Por ello, se investigó la situación de las políticas de las revistas del ámbito de la Comunicación y la Información en lo que respecta a los aspectos de la publicación de datos científicos. Del universo de 1804 revistas de Qualis Periódicos (2016), se seleccionaron 46 revistas de estrato A1 para ser analizadas. El resultado mostró que el 52% de las revistas han adoptado políticas editoriales para compartir los datos de investigación, sin embargo, menos de la mitad tienen directrices para los autores sobre cómo citar y acreditar los datos científicos. Se concluye que, a pesar de la incongruencia de la política con las directrices para los autores en cuanto a la citación de datos, el identificador persistente y las licencias de uso, reutilización, intercambio y distribución, las revistas tratan de seguir las tendencias de la publicación abierta., A dimensão da ciência aberta intitulada de dados abertos requer ações de todos os atores da comunicação científica e os editores científicos possuem uma responsabilidade de incrementar a política editorial prevendo os requisitos mínimos para orientar os autores e avaliadores nesse processo. Por esse motivo, investigou a situação das políticas dos periódicos no campo da Comunicação e Informação quanto aos aspectos de publicação de dados científicos. Do universo de 1804 revistas no Qualis Periódicos (2016), 46 revistas do estrato A1 foram selecionadas para serem analisadas. O resultado demonstrou que 52% das revistas adotaram políticas editoriais para o compartilhamento de dados de pesquisa, no entanto, menos da metade possui orientações para autores sobre como citar e dar créditos aos dados científicos. Conclui-se que apesar da incongruência da política com as diretrizes para autores referente a citação de dados, identificador persistente e licenças uso, reuso, compartilhamento e distribuição, as revistas estão procurando acompanhar as tendências de publicações abertas.

Cryptoassets are becoming essential in the digital economy era. XRP is one of the large market cap cryptoassets. Here, we develop a novel method of correlation tensor spectra for the dynamical XRP networks, which can provide an early indication for XRP price. A weighed directed weekly transaction network among XRP wallets is constructed by aggregating all transactions for a week. A vector for each node is then obtained by embedding the weekly network in continuous vector space. From a set of weekly snapshots of node vectors, we construct a correlation tensor. A double singular value decomposition of the correlation tensors gives its singular values. The significance of the singular values is shown by comparing with its randomize counterpart. The evolution of singular values shows a distinctive behavior. The largest singular value shows a significant negative correlation with XRP/USD price. We observe the minimum of the largest singular values at the XRP/USD price peak during the first week of January 2018. The minimum of the largest singular value during January 2018 is explained by decomposing the correlation tensor in the signal and noise components and also by evolution of community structure., 取引ネットワークの相関テンソルによる暗号資産の価格バーストの予測 --安心して使える暗号資産とデジタル経済の実現にむけて--. 京都大学プレスリリース. 2023-03-27.

Ancient brass (aurichalcum) was a valued commodity in the Antiquity, notably because of its gold-like appearance. After mastering brass fabrication using the cementation procedure in the first century BC in the Mediterranean, this material became widely used by the Romans for coins, jewellery and other artefacts. Because of its visual qualities, it is believed that since this period, brass played an important role in diplomatic and economic contacts with indigenous communities, notably Celtic and Germanic tribes north of Danube and west of Rhine. To test this hypothesis, we performed for the first time the advanced statistical multivariate analysis based on chemical composition and lead isotope systematics, coupled with informed typo-chronological categorisation, of a suite of late Iron Age and Early Roman period (first century BC – first century AD) brass and other copper-alloy artefacts from the territory of Bohemia. In order to to discuss their provenance, the results were compared to known contemporary sources of material. The new results for brass artefacts from this early phase of the massive occurrence of Roman aurichalcum in the Barbarian territories point to the ore deposits in the western Mediterranean or the Massif Central area in Gaul, consistent with historical events. These new findings underscore the great economic and political importance of the new and rich mineral resources in the Transalpine Gaul acquired due to Caesar's military campaigns.

Tipping points (TPs) in Earth’s climate system have been the subject of increasing interest and concern in recent years, given the risk that anthropogenic forcing could cause abrupt, potentially irreversible, climate transitions. Paleoclimate records are essential for identifying past TPs and for gaining a thorough understanding of the underlying nonlinearities and bifurcation mechanisms. However, the quality, resolution, and reliability of these records can vary, making it important to carefully select the ones that provide the most accurate representation of past climates. Moreover, as paleoclimate time series vary in their origin, time spans, and periodicities, an objective, automated methodology is crucial for identifying and comparing TPs. To address these challenges, we introduce the open-source PaleoJump database, which contains a collection of carefully selected, high-resolution records originating in ice cores, marine sediments, speleothems, terrestrial records, and lake sediments. These records describe climate variability on centennial, millennial and longer time scales and cover all the continents and ocean basins. We provide an overview of their spatial distribution and discuss the gaps in coverage. Our statistical methodology includes an augmented Kolmogorov–Smirnov test and Recurrence Quantification Analysis; it is applied here, for illustration purposes, to selected records in which abrupt transitions are automatically detected and the presence of potential tipping elements is investigated. These transitions are shown in the PaleoJump database along with other essential information about the records, including location, temporal scale and resolution, as well as temporal plots. This open-source database represents, therefore, a valuable resource for researchers investigating TPs in past climates.

Motivated by the detection of cascades of defaults in economy, we developed a detection framework for an endogenous spreading based on causal motifs we define in this paper. We assume that the change of state of a vertex can be triggered by an endogenous or an exogenous event, that the underlying network is directed and that times when vertices changed their states are available. In addition to the data of company defaults, we also simulate cascades driven by different stochastic processes on different synthetic networks. We show that some of the smallest motifs can robustly detect endogenous spreading events. Finally, we apply the method to the data of defaults of Croatian companies and observe the time window in which an endogenous cascade was likely happening., Comment: 12 pages, 4 figures

The only martian rock samples on Earth are meteorites ejected from the surface of Mars by asteroid impacts. The locations and geological contexts of the launch sites are currently unknown. Determining the impact locations is essential to unravel the relations between the evolution of the martian interior and its surface. Here we adapt a Crater Detection Algorithm that compile a database of 90 million impact craters, allowing to determine the potential launch position of these meteorites through the observation of secondary crater fields. We show that Tooting and 09-000015 craters, both located in the Tharsis volcanic province, are the most likely source of the depleted shergottites ejected 1.1 million year ago. This implies that a major thermal anomaly deeply rooted in the mantle under Tharsis was active over most of the geological history of the planet, and has sampled a depleted mantle, that has retained until recently geochemical signatures of Mars’ early history., The ejection sites of the martian meteorites are still unknown. Here, the authors build a database of 90 million craters and show that Tharsis region is the most likely source of depleted shergottites ejected 1.1 Ma ago, thus confirming that some portions of the mantle were recently anomalously hot.

This study applied multiple machine learning algorithms to classify the performance levels of professional goalkeepers (GK). Technical performances of GK’s competing in the elite divisions of England, Spain, Germany, and France were analysed in order to determine which factors distinguish elite GK’s from sub-elite GK’s. A total of (n = 14,671) player-match observations were analysed via multiple machine learning algorithms (MLA); Logistic Regressions (LR), Gradient Boosting Classifiers (GBC) and Random Forest Classifiers (RFC). The results revealed 15 common features across the three MLA’s pertaining to the actions of passing and distribution, distinguished goalkeepers performing at the elite level from those that do not. Specifically, short distribution, passing the ball successfully, receiving passes successfully, and keeping clean sheets were all revealed to be common traits of GK’s performing at the elite level. Moderate to high accuracy was reported across all the MLA’s for the training data, LR (0.7), RFC (0.82) and GBC (0.71) and testing data, LR (0.67), RFC (0.66) and GBC (0.66). Ultimately, the results discovered in this study suggest that a GK’s ability with their feet and not necessarily their hands are what distinguishes the elite GK’s from the sub-elite.

We introduce here SOIL-WATERGRIDS, a new dataset of dynamic changes in soil moisture and depth of water table over 45 years from 1970 to 2014 globally resolved at 0.25 × 0.25 degree resolution (about 30 × 30 km at the equator) along a 56 m deep soil profile. SOIL-WATERGRIDS estimates were obtained using the BRTSim model instructed with globally gridded soil physical and hydraulic properties, land cover and use characteristics, and hydrometeorological variables to account for precipitation, ecosystem-specific evapotranspiration, snowmelt, surface runoff, and irrigation. We validate our estimates against independent observations and re-analyses of the soil moisture, water table depth, wetland occurrence, and runoff. SOIL-WATERGRIDS brings into a single product the monthly mean water saturation at three depths in the root zone and the depth of the highest and lowest water tables throughout the reference period, their long-term monthly averages, and data quality. SOIL-WATERGRIDS can therefore be used to analyse trends in water availability for agricultural abstraction, assess the water balance under historical weather patterns, and identify water stress in sensitive managed and unmanaged ecosystems., Measurement(s)volumetric water content • water table depthTechnology Type(s)computational modeling techniqueFactor Type(s)hydrometeorology • soil properties • land useSample Characteristic - Environmentsoil Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15982578

Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models., Measurement(s)Vestibular SchwannomaTechnology Type(s)Magnetic Resonance Imaging • image segmentationSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16528977

To examine conservative–liberal differences in the extent to which partisan tweets reach less partisan moderate users in a nonwestern context, we analyzed a network of retweets about former Japanese Prime Minister Shinzo Abe. The analyses consistently demonstrated that partisan tweets originating from the conservative cluster reach a wider range of moderate users than those from the liberal cluster. Network analyses revealed that while the conservative and the liberal clusters’ internal structures were similar, the conservative cluster reciprocated the follows from moderate accounts at a higher rate than the liberal cluster. In addition, moderate accounts reciprocated the conservative cluster’s following at a higher rate than they did for the liberal cluster. The analysis of tweet content showed no difference in the frequency of hashtag use between conservatives and liberals, but there were differences in the use of emotion words and linguistic expressions. In particular, emotion words related to the propagation of messages, such as those expressing “dislike”, were used more frequently by conservatives, while the use of adjectives by conservatives was closer to that of moderate users, indicating that conservative tweets are more palatable for moderate users than liberal tweets.

The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R2 in the range of 0.798–0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R2 in the range of 0.841–0.920. The sphericity of the refractions varied in the range of 0.52–0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R2 in the range of 0.845–0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.

Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms.

Mosquitoes transmit several infectious diseases that pose significant threat to human health. Temperature along with other environmental factors at breeding and resting locations play a role in the organismal development and abundance of mosquitoes. Accurate analysis of mosquito population dynamics requires information on microclimatic conditions at breeding and resting locations. In this study, we develop a regression model to characterize microclimatic temperature based on ambient environmental conditions. Data were collected by placing sensor loggers at resting and breeding locations such as storm drains across Houston, TX. Corresponding weather data was obtained from National Oceanic and Atmospheric Administration website. Features extracted from these data sources along with contextual information on location were used to develop a Generalized Linear Model for predicting microclimate temperatures. We also analyzed mosquito population dynamics for Aedes albopictus under ambient and microclimatic conditions using system dynamic (SD) modelling to demonstrate the need for accurate microclimatic temperatures in population models. The microclimate prediction model had an R2 value of ~ 95% and average prediction error of ~ 1.5 °C indicating that microclimate temperatures can be reliably estimated from the ambient environmental conditions. SD model analysis indicates that some microclimates in Texas could result in larger populations of juvenile and adult Aedes albopictus mosquitoes surviving the winter without requiring dormancy.

Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).

The Gutenberg Gait Database comprises data of 350 healthy individuals recorded in our laboratory over the past seven years. The database contains ground reaction force (GRF) and center of pressure (COP) data of two consecutive steps measured - by two force plates embedded in the ground - during level overground walking at self-selected walking speed. The database includes participants of varying ages, from 11 to 64 years. For each participant, up to eight gait analysis sessions were recorded, with each session comprising at least eight gait trials. The database provides unprocessed (raw) and processed (ready-to-use) data, including three-dimensional GRF and two-dimensional COP signals during the stance phase. These data records offer new possibilities for future studies on human gait, e.g., the application as a reference set for the analysis of pathological gait patterns, or for automatic classification using machine learning. In the future, the database will be expanded continuously to obtain an even larger and well-balanced database with respect to age, sex, and other gait-specific factors., Measurement(s)ground reaction force • centre of pressure • walking behavior • gait measurement • Normal GaitTechnology Type(s)force plate • Sensor DeviceFactor Type(s)age • sex • walking speedSample Characteristic - OrganismHomo sapiensSample Characteristic - Environmentlaboratory environment Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15112488

This presentation introduces the community guidelines for sharing dataset quality information, developed by members of multiple, international groups. The guidelines follow the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). The FAIR dataset quality information guidelines are intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to achieve the following: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR guiding principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information., Presented at the FAIR-Points webinar on November 30, 2022, {"references":["Peng, G., Lacagnina, C., Ivánová, I., Downs, R. R., Ramapriyan, H., Ganske, A., and others, 2021. International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets. OSF, https://doi.org/10.31219/osf.io/xsu4p. Updated: March 26, 2022","Peng, G., Lacagnina, C., Downs, R.R., Ganske, A., Ramapriyan, H.K., Ivánová, I., Wyborn, L., Jones, D., Bastin, L., Shie, C.-. lin . and Moroni, D.F., 2022. Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets. Data Science Journal, 21, http://doi.org/10.5334/dsj-2022-008","Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., and others, 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, https://doi.org/10.1038/sdata.2016.18"]}

With the recent acceleration of climate change impacts throughout the world, it is getting even more important and urgent to provide climate data in a way that it can be used seamlessly and easily. This can really make a difference in making climate data actionable: data that can lead to stakeholders and policy actions. Climate data users often face real challenges when dealing with climate data because of data volumes, distributed datasets in a collection of hundreds of files, and very long processing time in order to create useful products. Providing ready-made products is an important part of making climate data more accessible to end users. Such tools like icclim1, a flexible python software package to calculate climate indices and indicators, is very valuable in order to pre-compute end users’ products. Those tools must adhere as much as possible to metadata conventions such as CF, implementing also provenance information, and be as close to FAIR. In the context of the EGI-ACE H2020 project, a dataset of standard climate indices is being created, using data from CMIP6 climate simulations stored on the ESGF. The calculations are performed using the 5.x version of icclim1. The icclim software is also integrated with the C3S toolbox2 to provide tutorials on how to calculate climate indices. This tool and dataset are in the process to be integrated in the IS-ENES C4I 2.0 platform3. A Jupyter notebook collection is provided in a SWIRRL3 environment (Software for Interactive Reproducible Research Labs). Having access to this type of complex analysis tool and ready-made products in an extensive dataset are crucial for end users and to make climate data more readily usable. The interfaces and front-ends to access those tools and datasets are also an important part of making data more actionable for end users. In C4I there is also documentation and scientific information on climate data and climate simulations, as well as guidance. This project (IS-ENES3) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084. REFERENCES [1] https://github.com/cerfacs-globc/icclim [2] https://cds.climate.copernicus.eu/cdsapp#!/toolbox [3] https://dev.climate4impact.eu/ [4] https://gitlab.com/KNMI-OSS/swirrl

Across domains massive amounts of scientific data are generated which are useful beyond their original purpose. Yet, discoverability of these data is often hard especially for researchers and students from other domains. As part of the NSF-funded National Science Data Fabric (NSDF) initiative (http://nationalsciencedatafabric.org/) we developed a testbed to demonstrate that these boundaries can be overcome. As part of our effort, we identified the need for indexing large-amounts of scientific data across scientific domains. Instead of focusing and waiting on the development of a metadata convention across domains, we propose to build a lightweight indexing service with minimal metadata that complements existing domain-specific and rich-metadata collection efforts. The NSDF-Catalog is designed to facilitate multiple related objectives within a flexible microservice to: 1) coordinate data movements and replication of data from origin repositories within the NSDF federation 2) build an inventory of existing scientific data to inform the design of next-generation cyberinfrastructure and 3) provide a suite of tools for discovery of datasets for cross-disciplinary research. Our service indexes at a fine-granularity both at the file or object level to inform data distribution strategies and to improve the experience for users from the consumer perspective, with the goal of allowing end-to-end workflow optimizations.