Open Data Resources Research Articles

Mindat.org – the open access mineralogy database to accelerate data-intensive geoscience research

Abstract The mindat.org website (Mindat) has been operating since October 2000 as a free, crowd-sourced, and expert-curated database particularly focused on mineral species and their occurrences worldwide. The project has transformed from a hobbyist site in the beginning into a resource that has found use in various scientific research projects and educational programs. Together with other open data resources, Mindat has helped accelerate scientific discoveries in many fields, such as mineral evolution, mineral ecology, and the co-evolution of the geosphere and biosphere. Recently, through open data efforts, machine interfaces and software packages have been established to enable flexible data discovery and download from Mindat. We assume the data access and usage will further scale up in the next years. Although Mindat is curated by a team of geoscience and database experts across the world, the crowd-sourced records in Mindat still pose some bias. In this paper we first present an overview of the primary data subjects in Mindat, and then we give extensive details about the characteristics and bias of three most popular data subjects: locality, mineral species, and mineral occurrence. In the discussion we also give an outlook on appropriate data usage and future extension of data records. We hope users can obtain a more comprehensive view of the Mindat database through this paper and thus better plan their data use. We also hope more people will be inspired to contribute to the data curation work to make Mindat a sustained data ecosystem for geoscience research.

Maastrichtian palaeoenvironments and palaeoclimate reconstruction in southern South America (Patagonia, Argentina) based on fossil fungi and algae using open data resources

ABSTRACT The use of non-pollen palynomorphs (NPP), particularly fossil fungi and algae, as palaeobiological proxies for Late Cretaceous palaeoenvironmental and palaeoclimatic reconstructions of warm-to-hot greenhouse conditions, can enhance our understanding of climate change impacts on modern Patagonian environments. This study aimed to reconstruct the Maastrichtian palaeoenvironment and palaeoclimate in the Cañadón Asfalto Basin (CAB, Chubut Province) by testing these NPPs as proxies using the Nearest Living Relative method (NLR). Moreover, using modern ecological requirements from open-source databases, such as GBIF and processing it with an open-source, cross-platform tool like QGIS, linked with Köppen-Geiger shapefiles, provided evidence of climate-driven palaeo-distribution patterns of fungal and algal diversity at CAB. Applying modern ecological requirements and biogeographic distribution data, we reconstructed the palaeoclimate as temperate with evenly distributed precipitation and warm summers, corresponding to the Cfb climate zone in Köppen-Geiger classifications. Additionally, our methodology produced reliable results regarding Cenozoic floras’ physiognomies based on fossil fungi, revealing a transition from sparsely wooded areas with palms and prairies to complex forest ecosystems with palms, deciduous trees, and shrubland. Furthermore, testing Cretaceous algae with the NLR method, for the first time, provided comprehensive insights into past water body characteristics, including trophic state and water quality.

The DoGA consortium expression atlas of promoters and genes in 100 canine tissues

The dog, Canis lupus familiaris, is an important model for studying human diseases. Unlike many model organisms, the dog genome has a comparatively poor functional annotation, which hampers gene discovery for development, morphology, disease, and behavior. To fill this gap, we established a comprehensive tissue biobank for both the dog and wolf samples. The biobank consists of 5485 samples representing 132 tissues from 13 dogs, 12 dog embryos, and 24 wolves. In a subset of 100 tissues from nine dogs and 12 embryos, we characterized gene expression activity for each promoter, including alternative and novel, i.e., previously not annotated, promoter regions, using the 5’ targeting RNA sequencing technology STRT2-seq. We identified over 100,000 promoter region candidates in the recent canine genome assembly, CanFam4, including over 45,000 highly reproducible sites with gene expression and respective tissue enrichment levels. We provide a promoter and gene expression atlas with interactive, open data resources, including a data coordination center and genome browser track hubs. We demonstrated the applicability of Dog Genome Annotation (DoGA) data and resources using multiple examples spanning canine embryonic development, morphology and behavior, and diseases across species.

Implementing national performance indicator ontology: A case study of Botswana's development agendas

This paper presents the National Performance Indicator Ontology (NPIOnto), a knowledge model designed for the semantic representation of national performance indicators (NPIs) and related performance variables to enhance the semantic capabilities of open data. The research addresses the lack of semantic representation for NPIs, particularly within Botswana's development agendas, which impedes the effective use, accessibility, and integration of performance data essential for monitoring and achieving sustainable development goals. NPIOnto was developed through five steps: discovering the ontology purpose, studying the development agenda knowledge domain, developing the conceptual knowledge model, formalizing the conceptual model, and evaluating the formal model's effectiveness in enhancing the semantic capabilities of open data resources. The evaluation, based on practical application in Botswana, demonstrated that NPIOnto significantly improves the accessibility, understandability, interoperability, integrability, and intelligibility of open data. Insights from the Botswana case study indicate that while NPIOnto is tailored to meet Botswana's development agenda needs, it can be adapted for use in other developing countries.

3-generation family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions associated with autism: An open-source catalog of findings.

The relatively few conditions and family member types (e.g., sibling, parent) considered in investigations of family health history in autism spectrum disorder (ASD, or autism) limits understanding of the role of family history in autism etiology. For more comprehensive understanding and hypothesis-generation, we produced an open-source catalog of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are cataloged in interactive heat maps and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/app/profile/diana.schendel/viz/ASDPlots_16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and nongenetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity.

Diversity and inclusion: A hidden additional benefit of Open Data.

The recent imperative by the National Institutes of Health to share scientific data publicly underscores a significant shift in academic research. Effective as of January 2023, it emphasizes that transparency in data collection and dedicated efforts towards data sharing are prerequisites for translational research, from the lab to the bedside. Given the role of data access in mitigating potential bias in clinical models, we hypothesize that researchers who leverage open-access datasets rather than privately-owned ones are more diverse. In this brief report, we proposed to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence (AI) for critical care. Specifically, we compared the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators (e.g., electronic health records from ICU patients accessible only to Mayo Clinic analysts). To measure the extent of author diversity, we characterized gender balance as well as the presence of researchers from low- and middle-income countries (LMIC) and minority-serving institutions (MSI) located in the United States (US). Our comparative analysis revealed a greater contribution of authors from LMICs and MSIs among researchers leveraging open critical care datasets (treatment group) than among those relying exclusively on private data resources (control group). The participation of women was similar between the two groups, albeit slightly larger in the former. Notably, although over 70% of all articles included at least one author inferred to be a woman, less than 25% had a woman as a first or last author. Importantly, we found that the proportion of authors from LMICs was substantially higher in the treatment than in the control group (10.1% vs. 6.2%, p<0.001), including as first and last authors. Moreover, we found that the proportion of US-based authors affiliated with a MSI was 1.5 times higher among articles in the treatment than in the control group, suggesting that open data resources attract a larger pool of participants from minority groups (8.6% vs. 5.6%, p<0.001). Thus, our study highlights the valuable contribution of the Open Data strategy to underrepresented groups, while also quantifying persisting gender gaps in academic and clinical research at the intersection of computer science and healthcare. In doing so, we hope our work points to the importance of extending open data practices in deliberate and systematic ways.

A panoptic segmentation dataset and deep-learning approach for explainable scoring of tumor-infiltrating lymphocytes

Tumor-Infiltrating Lymphocytes (TILs) have strong prognostic and predictive value in breast cancer, but their visual assessment is subjective. To improve reproducibility, the International Immuno-oncology Working Group recently released recommendations for the computational assessment of TILs that build on visual scoring guidelines. However, existing resources do not adequately address these recommendations due to the lack of annotation datasets that enable joint, panoptic segmentation of tissue regions and cells. Moreover, existing deep-learning methods focus entirely on either tissue segmentation or cell nuclei detection, which complicates the process of TILs assessment by necessitating the use of multiple models and reconciling inconsistent predictions. We introduce PanopTILs, a region and cell-level annotation dataset containing 814,886 nuclei from 151 patients, openly accessible at: sites.google.com/view/panoptils. Using PanopTILs we developed MuTILs, a neural network optimized for assessing TILs in accordance with clinical recommendations. MuTILs is a concept bottleneck model designed to be interpretable and to encourage sensible predictions at multiple resolutions. Using a rigorous internal-external cross-validation procedure, MuTILs achieves an AUROC of 0.93 for lymphocyte detection and a DICE coefficient of 0.81 for tumor-associated stroma segmentation. Our computational score closely matched visual scores from 2 pathologists (Spearman R = 0.58–0.61, p < 0.001). Moreover, computational TILs scores had a higher prognostic value than visual scores, independent of TNM stage and patient age. In conclusion, we introduce a comprehensive open data resource and a modeling approach for detailed mapping of the breast tumor microenvironment.

METB-12. GIFT FROM A CHILD POSTMORTEM SPECIMEN DATASET EXTENSION OF THE PEDIATRIC BRAIN TUMOR ATLAS EMPOWERS PEDIATRIC BRAIN TUMOR RESEARCH

Abstract BACKGROUND Pediatric brain and spinal cord tumors are the leading cause of cancer-related mortality in children. An incomplete understanding of brain tumor biology and associated limited access to high-quality biological samples for research are the main factors driving the lack of clinical therapeutic development for pediatric brain tumors that recur or progress. Post-mortem tissue donation provides an unprecedented resource for addressing some of these limitations. METHODS The Gift from a Child (GFAC) program by the Swifty Foundation has a unique mission to increase post-mortem pediatric brain tissue donations through advocacy as well as the education of clinicians and families. Through GFAC’s strategic collaboration with the Children’s Brain Tumor Network (CBTN), CBTN has leveraged postmortem tissue to expand the Pediatric Brain Tumor Atlas (PBTA), a cross-histology multi-omics atlas resource. As part of the effort CBTN has sequenced and released data for over 350 post-mortem pediatric brain tumor specimens including multiple brain region sampling cases with specimen and sequencing quality metrics. RESULTS Here we present an assessment of postmortem samples and available multi-omic data on postmortem samples within the PBTA dataset. Data have been harmonized and released with no publication embargo. To access data, researchers can utilize existing open source data resources and platforms including PedCbioPortal and OpenPedCan to: (1) Identify tumor spatial and temporal specific alterations (2) Establish tumor evolution trajectory leading to therapeutic resistance and tumor progression; (3) Understand tumor heterogeneity longitudinally across multiple ‘omics layers; and (4) Identify and request specimens and derived tumor models. CONCLUSIONS Together, we present the largest deeply characterized cohort of postmortem pediatric brain tumor samples as powerful expansion of the PBTA cohort of &amp;gt;3,000 pediatric brain tumors. CBTN’s open-science model supported by the GFAC mission highlights the value and utility of autopsy-based specimen collection on behalf of improving outcomes for children with brain tumors.

3-generation family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions associated with autism: an open-source catalogue of findings.

The relatively few conditions and family members investigated in autism family health history limits etiologic understanding. For more comprehensive understanding and hypothesis-generation we produced an open-source catalogue of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6,462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are catalogued in interactive heat maps and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/views/ASDPlots_16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and non-genetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity.

Multi - criteria analysis based on GIS and open data resources on the internet to establish the landslide susceptibility area of Lam Dong province

Evaluating the risk of landslides to implement preventative measures and aid in development planning is crucial, particularly in regions prone to such occurrences. Employing a landslide susceptibility map proves beneficial in managing land in landslide-prone zones. In this research, we introduce a GIS technology-based approach along with the multi-criteria analysis (MCA) method, incorporating the analytical hierarchy process (AHP) to determine optimal weight allocation for the criteria influencing landslide risk and compute landslide risk assessment indices. Eight criteria affecting landslide risk were chosen from publicly available data sources on the internet: elevation, slope, terrain moisture index (TWI), precipitation, land cover, normalized difference vegetation index (NDVI), soil type, and drainage density. Subsequently, overlaying simple maps facilitated the calculation of the Landslide Susceptibility Index (LSI), leading to the partitioning of the study area into five levels of landslide risk: very high, high, medium, average, low, and very low, covering approximately 8.6%, 24.3%, 32.0%, 23.9%, and 11.2% of the study area, respectively.

Exploring the Allen Brain Atlas with medical students in an official program of scientific training in neuropsychiatric genetics

Objective: The objective of this study is to illustrate how we introduced medical students to scientific training through the utilization of the Allen Brain Atlas (ABA) and how this endeavor resulted in significant scientific contributions presented at academic conferences, congresses, and published articles from 2013 to 2021.Methods: In this brief communication, we describe and analyze the papers authored by medical students from the Federal University of Pernambuco that were published in peer-reviewed journals using the ABA from 2013 to 2021 Results: ten papers at various medical conferences and events. Conclusion: We acknowledge the value of this initiative, which relies on open data resources and bioinformatics analysis, and we encourage others to explore similar opportunities for medical students to engage in research training.

The Pelagic Species Trait Database, an open data resource to support trait-based ocean research

Trait-based frameworks are increasingly used for predicting how ecological communities respond to ongoing global change. As species range shifts result in novel encounters between predators and prey, identifying prey ‘guilds’, based on a suite of shared traits, can distill complex species interactions, and aid in predicting food web dynamics. To support advances in trait-based research in open-ocean systems, we present the Pelagic Species Trait Database, an extensive resource documenting functional traits of 529 pelagic fish and invertebrate species in a single, open-source repository. We synthesized literature sources and online resources, conducted morphometric analysis of species images, as well as laboratory analyses of trawl-captured specimens to collate traits describing 1) habitat use and behavior, 2) morphology, 3) nutritional quality, and 4) population status information. Species in the dataset primarily inhabit the California Current system and broader NE Pacific Ocean, but also includes pelagic species known to be consumed by top ocean predators from other ocean basins. The aim of this dataset is to enhance the use of trait-based approaches in marine ecosystems and for predator populations worldwide.

Offensive language in user-generated comments in Lithuanian

Abstract The aim of the current research is to investigate the feasibility of identifying offensive language in Lithuanian by utilising the Simplified Offensive Language Taxonomy (SOLT). The key principle behind this taxonomy is its ability to complement existing offensive language ontologies and tagset systems, with the ultimate goal of integrating it into publicly accessible Linguistic Linked Open Data (LLOD) resources. The dataset used in the current study is a publicly available corpus of user-generated comments collected from a Lithuanian portal (Amilevičius et al. 2016). The study identified that offensive language predominantly focuses on collective derogatory language rather than individuals. The most common category of offensive language is related to physical and mental disabilities, followed by ideological offenses, xenophobic and sexist remarks, and less frequent categories like ageism, classism, homophobia, and religious discrimination. These results highlight the diverse range of offensive language online and underscore the need to combat discrimination and promote respectful discourse, particularly concerning marginalised groups.

Energy Performance Certificate Estimation at Large Scale Based on Open Data

This paper presents an innovative methodology for enhancing energy efficiency assessment procedures in the built environment, with a focus on the Switzerland’s Energy Strategy 2050. The current methodology necessitates intensive expert surveys, leading to substantial time and cost implications. Also, such a process can’t be scaled to a large number of buildings.Using machine learning techniques, the estimation process is augmented and exploit open data resources. Utilizing a robust dataset exceeding 70’000 energy performance certificates (CECB), the method devises a two-stage ML approach to forecast energy performance. The first phase involves data reconstruction from online repositories, while the second employs a regression algorithm to estimate the energy efficiency.The proposed approach addresses the limitations of existing machine learning methods by offering finer prediction granularity and incorporating readily available data. The results show a commendable degree of prediction accuracy, particularly for single-family residences. Despite this, the study reveals a demand for further granular data, and underlines privacy concerns associated with such data collection. In summary, this investigation provides a significant contribution to the enhancement of energy efficiency assessment methodologies and policy-making.

Providing open imaging data at scale: An EMBL-EBI perspective

Biological imaging is one of the primary tools by which we understand living systems across scales from atoms to organisms. Rapid advances in imaging technology have increased both the spatial and temporal resolutions at which we examine those systems, as well as enabling visualisation of larger tissue volumes. These advances have huge potential but also generate ever increasing amounts of imaging data that must be stored and analysed. Public image repositories provide a critical scientific service through open data provision, supporting reproducibility of scientific results, access to reference imaging datasets and reuse of data for new scientific discovery and acceleration of image analysis methods development. The scale and scope of imaging data provides both challenges and opportunities for open sharing of image data. In this article, we provide a perspective influenced by decades of provision of open data resources for biological information, suggesting areas to focus on and a path towards global interoperability.

Leveraging open data for reference services delivery in academic libraries

PurposeLibrarians play a critical role in curating, organizing and facilitating access to open data (OD) resources, supporting research, learning and information dissemination. This study aims to explore the use of OD for reference services delivery in academic libraries. It is believed that the concept of OD in librarianship refers to the practice of making publicly available data freely accessible, usable and shareable by anyone.Design/methodology/approachThis research examined how the utilization of OD may affect library services and operations. This paper used the systematic review of literature to answer the research questions; hence, it was a desk study.FindingsGiven that there has been recently a research shift in linking OD to library metadata, this study revealed some interesting findings. More specifically, it discovered that incorporating OD into reference services can provide several benefits and enhance the quality and relevance of the information provided to library users. In addition, this paper reported that OD enriches reference services by providing comprehensive, current and interdisciplinary information, supporting evidence-based research, enabling data visualization and analysis, fostering community engagement and promoting innovation.Originality/valueThis paper proposes that libraries that possess extensive collections are in a favorable position to embrace the shift toward becoming OD and open big data libraries. It is also believed that academic libraries, through reference services, meet the challenge of providing access to their institutional repositories by holding many sets of data in various formats and providing various interfaces, developing metadata systems for various data streams, supporting all ecosystems of software and data products for reformatting and reusing large and complex data, and support for data sets identifiers. By actively engaging with OD, librarians can play a vital role in facilitating access to information and promoting the use of open knowledge for academic pursuits.

GOOGLE EARTH ENGINE AND THE USE OF OPEN BIG DATA FOR ENVIRONMENTAL AND CLIMATE-CHANGE ASSESSMENTS: A KOSOVO CASE STUDY

Abstract. This paper presents a framework for utilizing available open data resources, such as Google Earth Engine, to assess environmental conditions and climate change vulnerability in Kosovo. This paper lays the groundwork for academic, government, and private sector institutions to develop strategies for addressing climate change and environmental issues in Kosovo and for other developing nations to understand the extent of change occurring. The study demonstrates the value of open data for environmental sustainability and resilience building. The framework is based on the analysis of air pollution, groundwater monitoring, urban environments, and deforestation in Kosovo using publicly available models. The model is re-coded for Kosovo in JavaScript using open datasets to create an environmental assessment of the scopes and scales of the environmental issues that plague Kosovo. The results show that there is a lack of institutional frameworks for assessing climate change impacts in Kosovo, with limited capacity for conducting environmental assessments, and with limited data capacity for resource and data scarce environments with community managed frameworks. By leveraging extensive amounts of data and applying analytical frameworks, this paper contributes to the knowledge base of environmental conditions in Kosovo with emphasis on open data and technological advancements.

Evidence-Based Assessment in Special Education Research: Advancing the Use of Evidence in Assessment Tools and Empirical Processes

Evidence-based assessment (EBA) requires that investigators employ scientific theories and research findings to guide decisions about what domains to measure, how and when to measure them, and how to make decisions and interpret results. To implement EBA, investigators need high-quality assessment tools along with evidence-based processes. We advance EBA in three sections in this article. First, we describe an empirically grounded framework, the Operations Triad Model (OTM), to inform EBA decision-making in the articulation of relevant educational theory. Originally designed for interpreting mental health assessments, we describe features of the OTM that facilitate its fusion with educational theory, namely its falsifiability. In turn, we cite evidence to support the OTM's ability to inform hypothesis generation and testing, study design, instrument selection, and measurement validation. Second, we describe quality indicators for interpreting psychometric data about measurement tools, which informs both the development and selection of measures and the process of measurement validation. Third, we apply the OTM and EBA to research in special education in two contexts: (a) empirical research for causal explanation and (b) implementation science research. We provide open data resources to advance measurement validation and conclude with future directions for research.

The use of Digital Twin in the sustainable development of the city on the example of managing parking resources

Sustainable city development is an approach to city planning and management that takes into account the balance between economic, social and environmental aspects in order to ensure sustainable social development, environmental protection and ensuring a high quality of life for residents.The aim of the article is to draw attention to the problems in the field of parking vehicles in a smart city, which should be solved as soon as possible. Problems related to parking in the city are a common challenge that can affect the comfort of living of residents, traffic, natural environment and transport efficiency. Many cities lack parking spaces in relation to the number of vehicles, leading to difficulties in finding a parking space. This can lead to traffic jams that increase the time it takes to find a parking space, increase traffic-related emissions and reduce the quality of life for residents.The research method to solve the problem related to the management of parking spaces is focused on the analysis of the city's problems and guidelines resulting from the sustainable development of the European Union. In the application part, open data resources were used to carry out the simulation in the proprietary application solution. The result of the article, which is a scientific research, is the presentation of the author's application in the Digital Twin concept for the simulation of parking space management.The simulation is the result of research conducted using open data published on the Internet.The conclusion of the research is that in the process of effective management of parking spaces, simulation in the Digital Twin concept can be used. There is great potential in this approach.

Thermal Tide Detection: A Case Study to Introduce Open Data Analysis in High School

Data analysis and interpretation has always played a fundamental role in the scientific curricula of high school students. The spread of digitalization has further increased the number of learning environments whereby this topic can be effectively taught: as a matter of fact, the ever-growing diffusion of data science across diverse sectors of academic research and economy has profoundly affected the orientation of the educational systems in many countries. In addition to this, the number and effectiveness of learning activities using data analysis and interpretation have been considerably enhanced by the diffuse availability of open data. In this paper, we propose a learning activity to introduce high school students to data analysis and interpretation; the aim is to detect the presence of atmospheric thermal tides starting from the analysis of surface pressure data obtained from open data sets available online. The activity is focused on three main educational goals: Pedagogical goal: developing a stronger awareness of the importance of scientific data literacy.Citizenship goal: fostering the access to free open data resources, hosted by national and international institutions.Learning goal: contributing to the acquisition of the basic skills to analyze experimental data, with particular regard to the time series analysis of environmental data.