Introduction

Background Information

System Design

The main areas of design criteria for the MDDB-UI system are the conceptual data design, the user interactive design and the technical design. These principles define the mapping of the MDDB back-end to the front-end interactive tools as illustrated in Fig. 1. The first tool we developed and describe in this paper is the Biodiversity Search tool (Fig. 1D). This tool provides the user with direct access to fungal taxa from DNA barcoding sequence data. It is also meaningful to determine where the classified DNA barcode sequence is detected and under what environmental conditions it occurs. This scenario defines the fundamental components that constitute the inference of fungal biodiversity amongst public data collections. The data contained in MDDB (Fig. 1C) rely on the unprocessed and unclassified metabarcoding data acquired from the studies deposited in sequence read archives (Fig. 1A). It is, however, through the curation and the integration with data from reference public databases (Canese and Weis 2013, Nilsson et al. 2018b, Geonames 2021) that we can classify and reveal it as significant barcoding data for use in fungal biodiversity assessments (Fig. 1B). The interaction with the tool provides such meaningful data for assisting users in the research and analysis of fungal biodiversity (Fig. 1E). In the following sections, we exhibit the main criteria of the Biodiversity Search tool of the MDDB-UI.

Figure 1.  

Workflow of the MDDB for the exposure of the front-end Biodiversity Search tool.

Conceptual Data Design

The main conceptual data design criteria of MDDB-UI are the availability, dimensionality and connectivity of the fungal biodiversity components.

Availability of the main components

The elements of the Biodiversity Search tool displayed to the user are the Location, Taxonomy and Environment. These represent the key components for the user to interact and initiate the search (Fig. 2a). Thus, the user's interaction with one or more of the main elements allows unlocking the connection of the components. It establishes access to the curated DNA barcode data component (Fig. 2b), which is associated with metabarcoding studies.

Figure 2.  

Illustrations of the available elements used for unlocking DNA barcode data. (a) Location, Taxonomy and Environment are the key elements of the Biodiversity Search tool made available to the user for initiating the search in MDDB; (b) The interaction with one of the elements unlocks and reveals the DNA barcode data element.

The taxonomy element utilises the scientific standard names to obtain the DNA barcode information. The interaction with the location element enables observation of where the DNA barcode is collected and the environment is used to inspect under which environmental conditions the DNA barcode has been detected.

Dimensionality of the main information components

Due to the complex nature of the data and their multiple dimensions, it is essential that the system supports various data views. The Taxonomy, Location, Environment and the uncovered DNA barcode components are containers of structured information (Fig. 3). For each component, navigation through these structures is permitted, enabling direct access to every group of items (i.e. a highlighted box display for each block of the Fig. 3.x-axis, labelled 'Types') at each hierarchical level (i.e. the Fig. 3.y-axis, labelled 'Levels').

Figure 3.  

Structure of the main information components of the Biodiversity Search Tool of MDDB-UI is illustrated as cubicle containers.

The Taxonomy (Fig. 3.Taxonomy) provides the classification of fungal taxa, based on fungal reference scientific names (Abarenkov et al. 2021). Navigating to the lower levels of this component allows for alternative data aggregations, collecting more defined sections of the taxonomic ranking. It is possible to directly observe either a group of species belonging to a high-level group (e.g. a phylum) or a more targetted group (e.g. family level).

The Location compartment (Fig. 3.Location) follows the same principles as the Taxonomy hierarchy, whereas layers obey the partonomy classification of the geographical area names (Geonames 2021). Using this component enables navigation from a large-scale territorial area (i.e. continental level) to a more refined geospatial region of interest, provided as a specific position where a referenced sample is collected. In this context, we will use the term "plot" for the sample site in a geographical space. The identifiers of the original samples are also associated with their spatial values representing their GPS coordinates.

The DNA Barcode (Fig. 3.DNA-Barcode) also contains organised structured information, as DNA barcodes are specific informative regions of the organisms' genome. Most current metabarcoding studies on fungi that are accessible in sequence archives, provide DNA sequence fragments (i.e. reads) belonging to specific ITS2 or ITS1 segments of the ITS barcode region (Schoch et al. 2012). While not as frequent, other studies also use SSU and LSU (e.g. archive studies SRP015917, ERP010906, SRP042134) as DNA barcodes. It is laborious to identify the type of barcode fragment sequences in SRA studies related to fungi, as this annotation term is rarely provided in the HTS experimental records of these studies (SRA 2022) and is infrequently found in a standard way. One needs to recognise that HTS technologies are also evolving (Ambardar et al. 2016, Amarasinghe et al. 2020). At present, Heeger et al. (2018) show that longer stretches of sequence barcode regions can be sequenced by novel HTS platforms. Given these reasons and considering that MDDB continues to incorporate more studies over time, the DNA barcode container is designed to accommodate the inclusion of the genomic data segments that represent the barcodes used for the various phylogenetic groups of species.

The latter component (Fig. 3.Environment) contains two data types; the Environmental Term and the Environmental Measurement. The Environmental Term value can constitute either a Feature, which is a term used to define the habitat (e.g. biome term) or the Material, that is a physical term that defines the environmental sample collected (e.g. soil). Both these types of descriptors are defined by the Environment Ontology controlled terms (ENVO) (Buttigieg et al. 2013). The Environmental Measurements are quantities, where the levels correspond to intervals of ordinal values (e.g. acidity levels, organic carbon concentration). These values rely on Units Ontology (UO) (Gkoutos et al. 2012) and Ontology of Units of Measure (OM) (Rijgersberg et al. 2013).

Connectivity of the components

The relationships amongst the main block containers form journey paths in which terms of the various components and of different levels connect (Fig. 4). Points of these exploration paths originate from any of the initial top-levels (e.g. phylum rank of the Taxonomy, the x-axis) of the main containers and navigation may continue along the different hierarchical levels (i.e. the y-axis). The connection of the terms belonging to the different containers (i.e. the dashed coloured lines) is enabled when one or more of the main elements is initiated during the interaction with the search tool.

Figure 4.  

Connection of the items belonging to the main containers.

For simplicity, only one term for each tree-map level of the main containers is revealed. Path 1 (the dark blue label) exhibits the choice of focusing on a specific taxonomic rank (term genus Lactarius) observed at the sub-continental level (term Northern Europe) in a temperate grassland biome context. The connection to the ITS barcode of the DNA barcode container is by default, but the annotation for the type of ITS (i.e. barcode ITS2 or ITS1) is provided. For path 3 (pink label), a more specific selection emerges; the DNA barcode sequence variants of ITS2 belonging to Lactarius that have been observed in the samples collected in Sweden, independently of the contextual aspect, are collected. Conversely, path 2 (cyan label) reveals a more generic search of interest, which considers the higher level of taxonomy (Basidiomycota phylum) that has been observed on a broader scale, the continental level (term Europe). This data selection can be sequentially refined, as shown by path 4 (orange), where only the DNA sequence variants belonging to the Russulales order level and detected in countries belonging to northern Europe are selected. Path 5 (the green label) suggests a simple yet a novel, powerful approach for exploring fungal biodiversity data when the Taxonomy component is not initiated. This case demonstrates how to directly observe a community at a small-scale level of diversity (e.g. the selection of a single specific sample), for which all sequence variants can be provided, regardless of their assigned taxonomic reference name.

User Interface Design

The web-based MDDB front-end application for the Biodiversity Search tool is developed using the criteria for user-centred design (Nielsen 1994, Jaspers 2009, Nielsen and Norman 2023). For the design, the conceptual data model (Fig. 3) is mapped to the interaction and interface design (Fig. 5.A). Experts evaluated the initial designs and the Nielsen usability heuristics (Nielsen 2012) guided the process. This method allowed the development of an efficient and clear front-end interface (Fig. 5). Nevertheless, the interface can continue to adapt and improve and, therefore, suggestions from researchers that are integrating the resources in their research workflows are always considered.

Figure 5.  

Browser display of Biodiversity Search Tool (A) alongside the Results window display of the query results (B).

Usability

The interface uses the graphical basics and widgets provided by modern browsers and APIs. In graphical user interfaces, the elements such as icons and pictograms are included that have a clear perceived affordance (Norman 1988). Moreover, researchers are accustomed to these standard visual elements and their perceived affordance. Importantly, the widgets in the design are placed corresponding to the logical visual flow of the system. A colour coding of the page facilitates quick separation of relevant parts in the interface and the selection items used for the elements. Likewise, the intuitive iconography guidelines help users expand hierarchical data and perform tasks. The visibility, or feedback, from the interactive elements is immediate. The group of researchers we engage include mycologists, taxonomists, evolutionary biologists, ecologists, as well as field amateurs, climate change scientists, conservationists, engineers, and researchers from other domains, along with first-time explorers, like students. For this reason, the interface provides familiar and intuitive visual icons and text descriptors to enhance usability for a broad range of users. The philosophy behind MDDB is based on the principle of learning by doing. The logic of presenting results is optimised for memorability.

Evaluations and Consistency

The relation of the usability aspects (Norman 1988, Nielsen 2012, Harrison et al. 2013) and the design are given in Table 1. The design considerations are written after expert evaluation and user consultation (Kurniawan 2004). New elements can be added to the interface after evaluating them with the same usability heuristics.

Table 1.

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XLSX

Usability attributes used for the User Interaction design of the Biodiversity search tool.

Usability attribute	Biodiversity search tool UI design choices
Learnability First impression with a system and learning how to use a system as quickly and as easily as possible.	The search tool window in Fig. 5A provides intuitive functional data access and a rapid dynamic view for interacting with the results (Fig. 5B). The output results window offers table and map views and displays icons that are universally recognisable. These choices emphasise the “learn-by-doing” concept, where users can effortlessly grasp the fungal biodiversity navigation.
Efficiency How efficiently and smoothly the user performs a task.	Efficient interaction with the data is achieved through exploration of the dynamic tree. Terms of interest are suggested in a free search box (Fig. 5A), enhancing user productivity and facilitating navigation of the hierarchical data structure. Experts using standard terms for their predefined queries find this method efficient for locating terms and ensuring consistent results. This aspect aligns with leading to the interoperability principle of FAIR.
Effectiveness How a user can complete a task with a high degree of accuracy.	Error reduction is the design criterion applied to aid in effectively controlling the construction of predefined queries. The implementation choice is controlled by the autocomplete function of the free text search box and the use of controlled terms that belong to dictionaries of reference names. This solution ensures precision and accuracy in data retrieval.
Satisfaction How a system influences user motivation and effectiveness of use.	For the minimalistic and functional design, we chose blue and white colours because they are aesthetically pleasing. These colours help segment the screen quickly, supporting visual search and focusing attention to the main elements. Likewise, the choice of colours enhances satisfaction by aiding users in identifying different functions. We have also used the “TAB” view to increase visibility and simplify the views.

Technical Design and Implementation

Open Access

The MDDB-UI is a browser front-end that provides free open access to information stored in MDDB. The home main page (Fig. 6) provides direct access to the Biodiversity Search Tool (Fig. 6C).

Figure 6.  

Main web page design of the MDDB interface showing redirection of the Biodiversity Search Tool from the home page and Menu window.

On the MENU option of the home page (Fig. 6B), we provide links to methods developed for collecting and processing the information from the studies. These repositories are made available to enhance data reusability (Github repository 2021, GitHub repository 2021) and improve data reproducibility (Github repository 2020). The front-end, defined by the main page, headers, footers and the menu, is developed using Joomla, a content management system (CMS) (Joomla 2021) (version 3.10). Whereas, access to the data is enabled by the Microsoft Open Database Connectivity (ODBC) driver (Engel et al. 2021), through which the web server connects to the database server hosting the monetDB MDDB.

Selection of data: queries

The data access to MDDB is granted in an intuitive manner, considering both the nature of the data and the diversity of the users. The usability attributes (Table 1) and the visibility principle, defined in the section User Interface Design, guided the implementation of the search display functions of the Biodiversity tool. As the Location and Taxonomy components contain hierarchical data, the accordion content search allows viewing targetted terms of the tree. The numerical values next to the names of the layers represent the number of items contained in each layer. Clicking on the value allows expansion of the tree to reveal lower levels (i.e. subclasses) (Fig. 7a). Users can employ the search box to jump directly to items of interest (Fig. 7b). The tree is primarily used for exploration, whereas the autocomplete search option is more useful for directly accessing known taxa.

Figure 7.  

Navigation of items of the Location and Taxonomy compartments. a Display of a tree view selection. One subclass (i.e. subcontinent) of the continent 'Africa' is selected. b The Taxonomy autocomplete search. At least three characters are needed to initiate the filter and suggestions containing the characters are displayed.

A value is selected when the user clicks on an item and it is displayed as a checkbox (Fig. 8a). According to the effectiveness attribute in Table 1, all the children of the layer are included for a selected item. The same method applies to the autocomplete search filter box; the selected item is appended in the list displayed above the search (Fig. 8b). The deselection of the items is allowed by clicking on the item name or on the checkbox.

Figure 8.  

Query construction using the search tool. (a) The Location subclasses of the continent 'Africa' are displayed. The country and the sample site, shown as checked selected terms, are the items used for the query; (b) The Taxonomy autocomplete selection: illustration of focusing on a specific taxa group. The autocomplete selection is useful for directly accessing the lower levels of the taxonomic tree.

The Environment selection provides environmental terms used to describe habitats and ranges of numerical values. The selected items (Fig. 9a) belong to a controlled list of environmental feature terms and are displayed with checkboxes. Measurement values are controlled through range sliders (Fig. 9b). To increase the accuracy with which users achieve their task, the values selected are controlled and corrected if they do not fall within the proper unit range of the measurement (e.g. pH values >14). The latter choice is based on the fact that most fungal community environmental studies and fungal research investigations involve comparing species richness and diversity across different measurement ranges (Geml et al. 2014, Carrino-Kyker et al. 2016, Liu et al. 2018). We intend to implement the Environment features to follow the hierarchical strategy displayed for the Location and Taxonomy elements, as we defined in our design (section Conceptual Design). When the user is satisfied with the data selection, the 'Update Results' button provides the results in the adjacent window. The 'Reset' button allows users to cancel the data selection (Fig. 6C).

Figure 9.  

Environment filter types. a The ENVO terms 'mediterranean forest' and 'temperate coniferous' biomes are selected; b Range slider used for filtering soil samples with pH values in the range of 3.5 to 5.9.

The search implementations are embedded in iframes and use PHP (version 7.4.0) (PHP 2021) and the UIkit (version 3.7.5), a PHP JavaScript framework (UIkit 2021). The language HTML5 (HTML 2021) structures the interface web pages, while JavaScript (Javascript 2021) is used for the expansion of the layers and the JQuery libraries (jQuery 2021) support HTML manipulation.

Presentation of results: multiple data views

The query results appear in the dynamic output window on Table Tab and the Map Tab (Fig. 10B). Both tabs initialise when the 'Update Results' button of the Search Tool (Fig. 10A) is selected. A JavaScript function controls switching on the data tabs.

Figure 10.  

Illustration of the data table view alongside the Biodiversity Search tool. Most of the attributes displayed redirect to the integrated reference public records.

Table view

The table view is the common method for displaying a dataset output based on a query. Each row of the results shown on the table corresponds to a unique record of a DNA sequence variant hit (i.e. ZOTU) detected from a selection of the filtering options. This hit represents the occurrence and its attributes display the annotation associated with the other components (Fig. 10).

Most attributes in the table appear as hyperlink values, which redirect to URLs, such as the corresponding mappings to the reference taxonomic name or to the provenance sample it originates from (e.g. NCBI BioSample record). These are reliable standards and data sources that follow the FAIR findability data principle (Wilkinson et al. 2016, Stall et al. 2019). These results provide data credibility and allow for extending relations to this knowledge domain. Above the output table display of the Table tab, two important values are displayed. The Total number of ZOTUs (Fig. 10B1) represents the total number of distinct ZOTU sequences obtained as a result of the query. The Total search occurrences (Fig. 10B2) is the record count of ZOTU sequences detected hits for the condition chosen by the user.

Map view

The map view is an associated function of the table view and initiates when the user clicks the Map Tab button (Fig. 11B).

Figure 11.  

Illustration of the data map view alongside the Biodiversity Search tool (Fig 11A). The map view is initiated by clicking on the Map Tab (Fig 11B). Two legends displayed on the map guide the visualisation (Fig 11B1).

All samples stored in the MDDB include a geographical location specified as a decimal numerical value (GPS) (Sinnige and Polen 2020). Two important measurements derive from the occurrence query display of the table view; these are visible in the legend, represented by two different colour gradients. These gradients provide the dimensionality of the data visualised on the map (Fig. 11B1). The first measurement relates to the physical location and matches the number of clustered samples where a DNA sequence has been detected (Fig. 12a). This measurement utilises an orange to yellow to green gradient to represent the quantity of samples in a spatial area, with orange indicating an area denser with samples. The second measurement connects to taxonomic labelling, inspecting the fungal groups. Through interaction with the map, this measurement grants the observation of the sequence richness within a community comprising the sample (Fig. 12b). The values of this richness appear through a dark purple to red gradient, with darker colours indicating richer communities.

Figure 12.  

Map view example of a geographical region. (a) Number of sample representation of the map view; (b) Sequence variant diversity (ZOTU richness) display for one soil plot (sample SRS651251).

This latter information is perceived on the interactive map, where the marker icon embedded over each sample indicates the amount of DNA barcode sequences, represented as ZOTU numbers, observed in the original sample plot. The total numbers of plots and ZOTUs shown on the map reflect the aggregated results of the occurrence hits displayed on the table view (Fig. 10B). The interactive map view is built in JavaScript by using the Leaflet package (version 1.7.1) (Leaflet 2021). Using controlled terms to describe data enhances data aggregation and extends data visualisation functionalities. Our front-end design considers additional implementations of query results, such as dynamic charts, to facilitate the data aggregation display. This Tab, as shown in Fig 10, is only for a User Interface Design display.

Data Download

The data selected by the Biodiversity Search tool can be downloaded by selecting the Download Tab (Fig. 13) displayed above the Output Result window, as shown in Fig. 10.

Figure 13.  

Download Tab window displays the options for retrieving the selected data.

The Download tab provides two options for obtaining the selected data: either the occurrence metadata or the set of distinct ZOTUs sequences detected by the selection. The total occurrence data, indicated by the number of occurrences records displayed in Fig. 10B2, can be downloaded in a CSV format and includes contextual information about the data selection. This information includes the complete metadata related to the location, taxonomy, environment and its associated reliable data standards. The sequence variants of interest, corresponding to the unique count of ZOTU sequences (Fig. 10B1), can be downloaded separately in a FASTA format file. The group list in the FASTA file contains distinct DNA sequences found in the associated site plots or within the chosen taxonomic groups and represent the ZOTU richness. Alternatively, individual ZOTUs from each record of the table view (IV attribute column of Fig. 10B) can be downloaded in individual FASTA files. The user can then remap the occurrence records to the FASTA file as both files refer and contain ZOTUs identifiers, provided by using the MycoDiversity DataBase unique refsequence_pk labels references in the file's headers. It is part of our next implementations to make these primary keys be persistent identifiers (i.e. PIDs).

Scalability of the MDDB system

The scalability requirement takes into account the nature and the volume of the data. The data comprise large datasets of DNA sequence barcodes and annotations from literature and metabarcoding studies. Extensive queries (Martorelli et al. 2020) that incorporate aggregations of DNA sequence data into hierarchical data and geographical spatial components are efficiently executed by the large-scale processing architecture of the MonetDB system (Boncz et al. 2008, Cijvat et al. 2015). Users can experience this by accessing the large data volumes provided by the MDDB Biodiversity search tool. The system's remarkable response in accessing the data and providing the results to the end-user enhances both the utility and satisfaction with the tool.

Biodiversity search use case: Fusarium in regions of Asia

We present a comprehensive scenario that demonstrates a potential use case for interacting with the Biodiversity search tool. This interaction enables access to and acquisition of informative data from the MDDB for a specific fungal biodiversity investigation. The learnability and efficiency principles covered in the Usability subsection of User Interface Design, are useful for extending the utility of the interactive tool for further fungal biodiversity inspections.

Bananas are the world’s fourth most important food crop after rice, wheat and maize and are grown in more than 130 countries (Voora et al. 2020). Asia is the largest banana producing region and Southeast Asia is known as the primary diversity centre of bananas (Debnath et al. 2019). There is a long history of banana cultivation in this region, with the main focus on selecting more fruitful varieties. During the initial banana export trades of the late 19^th century, Panama disease was first reported. This disease is the common name for the Fusarium genus (Ploetz 2015). (Stover and Simmonds 1987) considered Fusarium to be one of the most destructive of all plant diseases, significantly affecting the production of bananas. Studies (Dita et al. 2018, Bubici et al. 2019, Nadiah Jamil et al. 2020) describe potential resistant banana crops by applying biological, chemical and cultural methods to manage the reduction of the pathogen. Most of these methods can be invasive and labour-intensive. However, there is still the need to better understand and study the factors responsible for disease development. For example, abiotic factors can also influence disease intensity (Dita et al. 2018), particularly the pH of the soil. Evidence shows higher levels of the pathogen in banana crops, with Fusarium disease being most severe in those with lower pH values (Domı́nguez et al. 2001, Nasir et al. 2003, Deltour et al. 2017, Pegg et al. 2019). Nevertheless, there is still the lack of comparative analysis that allows a better understanding of how diverse abiotic factors, such as pH levels, may influence infections.

Here, we illustrate how the Biodiversity search tool can be used to collect data from other studies to enhance the investigation of environmental factors that may influence the occurrence and diversity of Fusarium species. The Location and Taxonomy components are initiated for this use case. For the Location, the tree view is preferably used, as this permits visualisation of the available subclasses of Asia, defined as sub-continents. These include two regions: southern Asia and south-eastern Asia. For the Taxonomy selection, the user focuses on a specific group, with the taxa known. Thus, the autocomplete search box is used to directly obtain the term Fusarium (Fig. 14A). The top hits of the taxonomic names displayed to the user also provide the ranking level, which helps to know the taxonomic level being referred to. The Result button is pressed (Fig. 14B) and the output query result is directly displayed in the Table view (Fig. 14C).

Figure 14.  

Window A. shows the Location and Taxonomy Search terms selected by the user and displayed by the system. When the user is satisfied with the search selection, the Update Result button is clicked (Window B). Window C presents the Table view of the immediate results of the submitted query.

The query result (displayed in Appendix) of this use case returns 71 ZOTU records that correspond to a distinct set of 53 Fusarium sequence variants hits detected for the two regions of Asia. The interactive map displayed on the Map Tab option displays the number of sequence variants for each sample (Fig. 15). The number of sequences is reasonable as the search is refined to a very low taxonomic rank (i.e. genus level). To validate the sequence richness (i.e. number of ZOTUs variants) for one sample, we replicate the search for a single sample hit from the data selected. For example, Fig. 16a exhibits the reference NCBI Sample SRS651474 containing six unique ZOTU sequences belonging to the Fusarium genus group. These are simultaneously provided in the Table view (Fig. 16b).

Figure 15.  

Illustration of the Map View of the 21 samples obtained from the query result, corresponding to the user selection region of interest.

Figure 16.  

Example of occurrence data display using the map view and data table view simultaneously. a. Map view: The markup of plot MDDBSRS000485 (SRA Sample ID SRS651474) displaying the number of ZOTUs observed; b. Table View: The six records belonging to the Fusarium genus associated with the SRA Sample SRS651474.

The purpose of the use case is specifically illustrative, displaying the usability and utility of the tool. The scenario illustrates that it can provide informative below-ground specific regional information, such as ZOTU diversity for a targetted fungal group and an important characteristic of the habitat, acidity, which can be measured. This valuable information should be included to predict Fusarium richness for further sampling and to build upon ecological assessments that associate the above-ground layer. This helps prevent the invasion of the disease on future crop plots.

The Biodiversity Search tool makes an important contribution to scientific research in agriculture. It allows for the observation of fungal communities where Fusarium has been detected, based on reliable existing published data, as described in the design criteria in the Connectivity of the Components subsection of the Conceptual Data Design. Studies analysing Fusarium soils have identified other fungal genera in these soils that play a key role in suppressing Fusarium (El-Baky and Amara 2021). In general, observing groups within microbe communities permits the detection of specific antagonists that can be correlated with the disease suppression (Bubici et al. 2019). MDDB enables the observation of community levels in samples, rather than focusing only on specific fungal groups. This approach can significantly support farmers and community organisations in controlling diseases that invade their crops. For example, farmers could choose to cultivate their new banana crops in fields where antagonists of Fusarium are present in the soils, as observed within these communities.

Discussion

Conclusion and future perspectives

Appendix: Query and data selection used for the biodiversity use case

Acknowledgements

Conflicts of interest

References

Abarenkov K, Zirk A, Piirmann T, Pohonen R, Ivanov F, R. Henrik N, Koljalg U (2021)
UNITE general FASTA release for Fungi 2
.
UNITE Community
. URL: https://doi.org/10.15156/BIO/1280089
Amarasinghe S, Su S, Dong X, Zappia L, Ritchie M, Gouil Q (2020)
Opportunities and challenges in long-read sequencing data analysis
.
Genome Biology
21
(
1
). https://doi.org/10.1186/s13059-020-1935-5
Ambardar S, Gupta R, Trakroo D, Lal R, Vakhlu J (2016)
High Throughput Sequencing: An Overview of Sequencing Chemistry
.
Indian Journal of Microbiology
56
(
4
):
394
‑
404
. https://doi.org/10.1007/s12088-016-0606-4
Barker M, Chue Hong N, Katz D, Lamprecht A, Martinez-Ortiz C, Psomopoulos F, Harrow J, Castro LJ, Gruenpeter M, Martinez PA, Honeyman T (2022)
Introducing the FAIR Principles for research software
.
Scientific Data
9
(
1
). https://doi.org/10.1038/s41597-022-01710-x
Belien JAM, Kip AE, Swertz MA (2022)
Road to FAIR genomes: a gap analysis of NGS data generation and sharing in the Netherlands
.
BMJ Open Science
6
(
1
). https://doi.org/10.1136/bmjos-2021-100268
Boncz PA, Kersten M, Menegold S (2008)
reaking the memory wall in MonetDB
.
Communications of the ACM
51
(
12
):
77
‑
85
. URL: https://hdl.handle.net/11245/1.295505
Bubici G, Kaushal M, Prigigallo MI, Gómez-Lama Cabanás C, Mercado-Blanco J (2019)
Biological control agents against Fusarium wilt of Banana
.
Frontiers in Microbiology
10
https://doi.org/10.3389/fmicb.2019.00616
Buttigieg P, Morrison N, Smith B, Mungall CJ, Lewis SE (2013)
The environment ontology: contextualising biological and biomedical entities
.
Journal of Biomedical Semantics
4
(
1
). https://doi.org/10.1186/2041-1480-4-43
Canese K, Weis S (2013)
PubMed: The Bibliographic Database, The NCBI Handbook. 2nd edition
.
National Center for Biotechnology Information (US)
URL: https://www.ncbi.nlm.nih.gov/books/NBK153385/
Carrino-Kyker S, Kluber L, Petersen S, Coyle K, Hewins C, DeForest J, Smemo K, Burke D (2016)
Mycorrhizal fungal communities respond to experimental elevation of soil pH and P availability in temperate hardwood forests
.
FEMS Microbiology Ecology
92
(
3
). https://doi.org/10.1093/femsec/fiw024
Churko J, Mantalas G, Snyder M, Wu J (2013)
Overview of high throughput sequencing technologies to elucidate molecular pathways in cardiovascular diseases
.
Circulation Research
112
(
12
):
1613
‑
1623
. https://doi.org/10.1161/circresaha.113.300939
Cijvat R, Manegold S, Kersten M, Klau G, Schönhuth A, Marschall T, Zhang Y (2015)
Genome sequence analysis with MonetDB
.
Datenbank-Spektrum
15
(
3
):
185
‑
191
. https://doi.org/10.1007/s13222-015-0198-x
Courtot M, Gupta D, Liyanage I, Xu F, Burdett T (2021)
BioSamples database: FAIRer samples metadata to accelerate research data management
.
Nucleic Acids Research
50
https://doi.org/10.1093/nar/gkab1046
Dataverse (2023)
The Dataverse Project, an open source web application to share, preserve, cite, explore, and analyze research data
. https: //dataverse.org. Accessed on: 2023-12-01.
Debnath S, Khan AA, Das A, Murmu I, Khan A, Mandal KK (2019)
Genetic diversity in banana
.
Sustainable Development and Biodiversity
217
‑
241
. https://doi.org/10.1007/978-3-319-96454-6_8
Deltour P, C. França S, Liparini Pereira O, Cardoso I, De Neve S, Debode J, Höfte M (2017)
Disease suppressiveness to Fusarium wilt of banana in an agroforestry system: Influence of soil characteristics and plant community
.
Agriculture, Ecosystems & Environment
239
:
173
‑
181
. https://doi.org/10.1016/j.agee.2017.01.018
Dita M, Barquero M, Heck D, Mizubuti EG, Staver C (2018)
Fusarium wilt of banana: Current knowledge on epidemiology and research needs toward sustainable disease management
.
Frontiers in Plant Science
9
https://doi.org/10.3389/fpls.2018.01468
Domı́nguez J, Negrı́n MA, Rodrı́guez CM (2001)
Aggregate water-stability, particle-size and soil solution properties in conducive and suppressive soils to Fusarium wilt of banana from Canary Islands (Spain)
.
Soil Biology and Biochemistry
33
:
449
‑
455
. https://doi.org/10.1016/s0038-0717(00)00184-x
Edgar RC (2018)
Updating the 97% identity threshold for 16S ribosomal RNA OTUs
.
Bioinformatics
34
(
14
):
2371
‑
2375
. https://doi.org/10.1093/bioinformatics/bty113
El-Baky NA, Amara AAAF (2021)
Recent approaches towards control of fungal diseases in plants: An updated review
.
Journal of Fungi
7
(
11
). https://doi.org/10.3390/jof7110900
Engel D, Roth J, Guyer C, Rabeler C (2021)
Microsoft Open Database Connectivity (ODBC)
. https://docs.microsoft.com/en-us/sql/odbc/microsoft-open-database-connectivity-odbc
FAIR (2023)
Annotation of Bioassay Metadata – Pistoia Alliance, FAIR Toolkit
. https://fairtoolkit.pistoiaalliance.org/ use-cases/fair-annotation-of-bioassay-metadata/. Accessed on: 2023-12-01.
Finstad AG, Andersson A, Bissett A, Fossoy F, Grosjean M, Hope M, Koljalg U, Lundin D, Nilsson H, Prager M, Jeppesen TS, Svenningsen C, Schigel D (2020)
Publishing sequence-derived data through biodiversity data platforms
. https://docs.gbif.org/publishing-dna-derived-data/1.0/en/. Accessed on: 2022-10-01.
GBIF (2021)
The Global Biodiversity Information Facility (2021) Infrastructure
. https://www.gbif.org. Accessed on: 2022-10-01.
Geml J, Gravendeel B, van der Gaag K, Neilen M, Lammers Y, Raes N, Semenova T, de Knijff P, Noordeloos M (2014)
The contribution of DNA metabarcoding to fungal conservation: Diversity assessment, habitat partitioning and mapping red-listed fungi in protected coastal Salix repens communities in the Netherlands
.
PLOS One
9
(
6
). https://doi.org/10.1371/journal.pone.0099852
Geonames (2021)
GeoNames: The GeoNames geographical database
. http://www.geonames.org. Accessed on: 2023-10-17.
Geonames (2023)
Web services documentation and Shuttle Radar Topography Mission (SRTM) data - Online documentation
. http://www.geonames.org/export/web-services.html. Accessed on: 2023-10-07.
Github repository (2020)
- naturalis/mycodiversity: Pipelines for analyzing fungal diversity. Data acquisition, SRA-SRR (HTS) data processing, ZOTU reference generator
. https://github.com/naturalis/mycodiversity
Github repository (2021)
imartorelli/metadata_curation: Metadata curation and enrichment from GeoNames database
. URL: https://github.com/imartorelli/metadata_curation
GitHub repository (2021)
imartorelli/table_generator: Reference table generator for Study compartment
. https://github.com/imartorelli/table_generator
Gkoutos GV, Schofield PN, Hoehndorf R (2012)
The Units Ontology: a tool for integrating units of measurement in science
.
Database
2012
https://doi.org/10.1093/database/bas033
Glassman S, Wang I, Bruns T (2017)
Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales
.
Molecular Ecology
26
(
24
):
6960
‑
6973
. https://doi.org/10.1111/mec.14414
Harrison R, Flood D, Duce D (2013)
Usability of mobile applications: literature review and rationale for a new usability model
.
Journal of Interaction Science
1
(
1
). https://doi.org/10.1186/2194-0827-1-1
Heeger F, Bourne E, Baschien C, Yurkov A, Bunk B, Spröer C, Overmann J, Mazzoni C, Monaghan M (2018)
Long‐read DNA metabarcoding of ribosomal RNA in the analysis of fungi from aquatic environments
.
Molecular Ecology Resources
18
(
6
):
1500
‑
1514
. https://doi.org/10.1111/1755-0998.12937
Holstein J (2001)
GBIF: Global Biodiversity Information Facility.
University of Ulm
URL: https://books.google.nl/books?id=7KSyswEACAAJ.
HTML (2021)
HTML - Living Standard
. https://html.spec.whatwg.org
Jaspers MM (2009)
A comparison of usability methods for testing interactive health technologies: Methodological aspects and empirical evidence
.
International Journal of Medical Informatics
78
(
5
):
340
‑
353
. https://doi.org/10.1016/j.ijmedinf.2008.10.002
Javascript (2021)
JavaScript.com is a resource for the JavaScript community.
https://www.javascript.com
Jetz W, McGeoch M, Guralnick R, Ferrier S, Beck J, Costello M, Fernandez M, Geller G, Keil P, Merow C, Meyer C, Muller-Karger F, Pereira H, Regan E, Schmeller D, Turak E (2019)
Essential biodiversity variables for mapping and monitoring species populations
.
Nature Ecology & Evolution
3
(
4
):
539
‑
551
. https://doi.org/10.1038/s41559-019-0826-1
Joomla (2021)
joomla/joomla-cms: Home of the Joomla! Content Management System
. https://github.com/joomla/joomla-cms
jQuery (2021)
JS Foundation-js. foundation
. https://jquery.com
Jurburg S, Konzack M, Eisenhauer N, Heintz-Buschart A (2020)
The archives are half-empty: an assessment of the availability of microbial community sequencing data
.
Communications Biology
3
(
1
). https://doi.org/10.1038/s42003-020-01204-9
Katz K, Shutov O, Lapoint R, Kimelman M, Brister J, O’Sullivan C (2021)
The Sequence Read Archive: a decade more of explosive growth
.
Nucleic Acids Research
50
https://doi.org/10.1093/nar/gkab1053
Kirk JL, Beaudette LA, Hart M, Moutoglis P, Klironomos JN, Lee H, Trevors JT (2004)
Methods of studying soil microbial diversity
.
Journal of Microbiological Methods
58
(
2
):
169
‑
188
. https://doi.org/10.1016/j.mimet.2004.04.006
Kõljalg U, Abarenkov K, Zirk A, Runnel V, Piirmann T, Pöhönen R, Ivanov F (2019)
PlutoF: Biodiversity data management platform for the complete data lifecycle
.
Biodiversity Information Science and Standards
3
https://doi.org/10.3897/biss.3.37398
Kurniawan S (2004)
Interaction design: Beyond human-computer interaction by Preece, Sharp and Rogers (2001), ISBN 0471492787
.
Universal Access in the Information Society
3
:
289
‑
289
. https://doi.org/10.1007/s10209-004-0102-1
Leaflet (2021)
Leaflet - an open-source JavaScript library for interactive maps
. https://leafletjs.com
Leinonen R, Sugawara H, Shumway M (2010)
The Sequence Read Archive
.
Nucleic Acids Research
39
https://doi.org/10.1093/nar/gkq1019
Liu D, Liu G, Chen L, Wang J, Zhang L (2018)
Soil pH determines fungal diversity along an elevation gradient in Southwestern China
.
Science China Life Sciences
61
(
6
):
718
‑
726
. https://doi.org/10.1007/s11427-017-9200-1
Martorelli I, Helwerda L, Kerkvliet J, Gomes SF, Nuytinck J, van der Werff CA, Ramackers G, Gultyaev A, Merckx VFT, Verbeek F (2020)
Fungal metabarcoding data integration framework for the MycoDiversity DataBase (MDDB)
.
Journal of Integrative Bioinformatics
17
(
1
). https://doi.org/10.1515/jib-2019-0046
Martorelli I, Ramackers G, Verbeek F (2023)
Methods for making fungal biodiversity data accessible: Linking open publications to public sequence archive sources for the MycoDiversity DataBase User Interface (MDDB-UI).
1.0
.
Zenodo
. Release date:
2023-12-10
. URL: https://doi.org/10.5281/zenodo.10492047
Meiser A, Bálint M, Schmitt I (2013)
Meta‐analysis of deep‐sequenced fungal communities indicates limited taxon sharing between studies and the presence of biogeographic patterns
.
New Phytologist
201
(
2
):
623
‑
635
. https://doi.org/10.1111/nph.12532
Nadiah Jamil F, Tang C, Baity Saidi N, Lai K, Akmal Baharum N (2020)
Fusarium wilt in banana: Epidemics and management strategies
.
Horticultural Crops
https://doi.org/10.5772/intechopen.89469
Nasir N, Pittaway PA, Pegg KG (2003)
Effect of organic amendments and solarisation on Fusarium wilt in susceptible banana plantlets, transplanted into naturally infested soil
.
Australian Journal of Agricultural Research
54
(
3
). https://doi.org/10.1071/ar02099
Nielsen J (1994)
Usability engineering
.
Morgan Kaufmann Publishers
URL: https://www.google.nl/books/edition/Usability_Engineering/ DBOowF7LqIQC?hl=en&gbpv=1&dq=usability+engineering [ISBN
0125184069 9780125184069
]
Nielsen J (2012)
Usability 101: Introduction to Usability
. https://www.nngroup.com/articles/usability-101-introduction-to-usability/. Accessed on: 2023-12-01.
Nielsen J, Norman D (2023)
Nielsen Norman Group - Online, documentation
. https://www.nngroup.com. Accessed on: 2023-12-01.
Nilsson RH, Anslan S, Bahram M, Wurzbacher C, Baldrian P, Tedersoo L (2018a)
Mycobiome diversity: high-throughput sequencing and identification of fungi
.
Nature Reviews Microbiology
17
(
2
):
95
‑
109
. https://doi.org/10.1038/s41579-018-0116-y
Nilsson RH, Larsson K, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, Kennedy P, Picard K, Glöckner FO, Tedersoo L, Saar I, Kõljalg U, Abarenkov K (2018b)
The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications
.
Nucleic Acids Research
47
https://doi.org/10.1093/nar/gky1022
Norman DA (1988)
The psychology of everyday things.
Basic books
URL: https://books.google.it/books?id=OlNSRAAACAAJ
OpenScience (2023)
European Open Science Cloud (EOSC)
. https://digital-strategy.ec.europa.eu/en/policies/open-science-cloud. Accessed on: 2023-12-01.
Orgiazzi A, Singh B, Wall D, Barrios E, Kandeler E, Moreira F, Deyn GD, Chotte J, Six J, Hedlund K, Briones M, Miko L, Johnson N, Ramirez K, Fierer N, Kaneko N, Lavelle P, Eggleton P, Lemanceau P, Bardgett R, Jeffery S, Fraser T, Pelletier VB, Putten WVD, Montanarella L, Jones A, et al. (2016)
Global Soil Biodiversity Atlas
.
European Commission, Publications Office of the European Union
,
Luxembourg
,
176
pp. URL: https://op.europa.eu/en/publication-detail/-/publication/c54ece8e-1e4d-11e6-ba9a-01aa75ed71a1
Pegg K, Coates L, O’Neill W, Turner D (2019)
The epidemiology of Fusarium wilt of Banana
.
Frontiers in Plant Science
10
https://doi.org/10.3389/fpls.2019.01395
PHP (2021)
PHP: Hypertext Preprocessor
. https://www.php.net.
Ploetz R (2015)
Fusarium wilt of banana
.
Phytopathology®
105
(
12
):
1512
‑
1521
. https://doi.org/10.1094/phyto-04-15-0101-rvw
PMC (1988)
PubMed Central [Online repository]. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information (NCBI)
. https://www.ncbi.nlm.nih.gov/pmc/. Accessed on: 2023-10-22.
Popkin G (2019)
Data sharing and how it can benefit your scientific career
.
Nature
569
(
7756
):
445
‑
447
. https://doi.org/10.1038/d41586-019-01506-x
QGIS (2023)
Documentation Projections Support - Coordinate Reference System (CRS), A free and open source geographic information system, the QGIS Server Guide/Manual (QGIS 3.28) - Online, documentation, QGIS Developer Cookbook
. https://docs.qgis.org/3.28/en/docs/gentle_gis_introduction/coordinate_reference_systems.html. Accessed on: 2023-12-01.
Rijgersberg H, van Assem M, Top J (2013)
Ontology of units of measure and related concepts
.
Semantic Web
4
(
1
):
3
‑
13
. https://doi.org/10.3233/sw-2012-0069
Rousk J, Bååth E, Brookes PC, Lauber CL, Lozupone C, Caporaso JG, Knight R, Fierer N (2010)
Soil bacterial and fungal communities across a pH gradient in an arable soil
.
The ISME Journal
4
(
10
):
1340
‑
1351
. https://doi.org/10.1038/ismej.2010.58
Schmidt P, Bálint M, Greshake B, Bandow C, Römbke J, Schmitt I (2013)
Illumina metabarcoding of a soil fungal community
.
Soil Biology and Biochemistry
65
:
128
‑
132
. https://doi.org/10.1016/j.soilbio.2013.05.014
Schoch C, Seifert K, Huhndorf S, Robert V, Spouge J, Levesque CA, Chen W, Bolchacova E, Voigt K, Crous P, Miller A, Wingfield M, Aime MC, An K, Bai F, Barreto R, Begerow D, Bergeron M, Blackwell M, Boekhout T, Bogale M, Boonyuen N, Burgaz A, Buyck B, Cai L, Cai Q, Cardinali G, Chaverri P, Coppins B, Crespo A, Cubas P, Cummings C, Damm U, de Beer ZW, de Hoog GS, Del-Prado R, Dentinger B, Diéguez-Uribeondo J, Divakar P, Douglas B, Dueñas M, Duong T, Eberhardt U, Edwards J, Elshahed M, Fliegerova K, Furtado M, García M, Ge Z, Griffith G, Griffiths K, Groenewald J, Groenewald M, Grube M, Gryzenhout M, Guo L, Hagen F, Hambleton S, Hamelin R, Hansen K, Harrold P, Heller G, Herrera C, Hirayama K, Hirooka Y, Ho H, Hoffmann K, Hofstetter V, Högnabba F, Hollingsworth P, Hong S, Hosaka K, Houbraken J, Hughes K, Huhtinen S, Hyde K, James T, Johnson E, Johnson J, Johnston P, Jones EBG, Kelly L, Kirk P, Knapp D, Kõljalg U, Kovács G, Kurtzman C, Landvik S, Leavitt S, Liggenstoffer A, Liimatainen K, Lombard L, Luangsa-ard JJ, Lumbsch HT, Maganti H, Maharachchikumbura SN, Martin M, May T, McTaggart A, Methven A, Meyer W, Moncalvo J, Mongkolsamrit S, Nagy L, Nilsson RH, Niskanen T, Nyilasi I, Okada G, Okane I, Olariaga I, Otte J, Papp T, Park D, Petkovits T, Pino-Bodas R, Quaedvlieg W, Raja H, Redecker D, Rintoul T, Ruibal C, Sarmiento-Ramírez J, Schmitt I, Schüßler A, Shearer C, Sotome K, Stefani FP, Stenroos S, Stielow B, Stockinger H, Suetrong S, Suh S, Sung G, Suzuki M, Tanaka K, Tedersoo L, Telleria MT, Tretter E, Untereiner W, Urbina H, Vágvölgyi C, Vialle A, Vu TD, Walther G, Wang Q, Wang Y, Weir B, Weiß M, White M, Xu J, Yahr R, Yang Z, Yurkov A, Zamora J, Zhang N, Zhuang W, Schindel D (2012)
Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi
.
Proceedings of the National Academy of Sciences
109
(
16
):
6241
‑
6246
. https://doi.org/10.1073/pnas.1117018109
Seifert K (2009)
Progress towards DNA barcoding of fungi
.
Molecular Ecology Resources
9
:
83
‑
89
. https://doi.org/10.1111/j.1755-0998.2009.02635.x
Shumway M, Cochrane G, Sugawara H (2009)
Archiving next generation sequencing data
.
Nucleic Acids Research
38
https://doi.org/10.1093/nar/gkp1078
Sinnige J, Polen Tv (2020)
GPS referencing and data storage manipulation
. https://theses.liacs.nl/pdf/2019-2020-PolenTvan.pdf. Accessed on: 2023-12-01.
Song Z, Schlatter D, Kennedy P, Kinkel L, Kistler HC, Nguyen N, Bates S (2015)
Effort versus reward: Preparing samples for fungal community characterization in high-throughput sequencing surveys of soils
.
PLOS One
10
(
5
). https://doi.org/10.1371/journal.pone.0127234
SRA (2022)
National Center for Biotechnology Information (NCBI) Sequence Read Archive: Get SRA data from the SRA database - Online, browser search page
. https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=run_browser. Accessed on: 2022-10-18.
Stall S, Yarmey L, Cutcher-Gershenfeld J, Hanson B, Lehnert K, Nosek B, Parsons M, Robinson E, Wyborn L (2019)
Make scientific data FAIR
.
Nature
570
(
7759
):
27
‑
29
. https://doi.org/10.1038/d41586-019-01720-7
Stover R, Simmonds NW (1987)
Bananas
.
Longman Sc & Tech
URL: https://books.google.it/books?id=rLjsKAAACAAJ
Tedersoo L, Bahram M, Zinger L, Nilsson RH, Kennedy P, Yang T, Anslan S, Mikryukov V (2022)
Best practices in metabarcoding of fungi: From experimental design to results
.
Molecular Ecology
31
(
10
):
2769
‑
2795
. https://doi.org/10.1111/mec.16460
UIkit (2021)
UIkit, a lightweight and modular front-end framework for developing fast and powerful web interfaces.
https://getuikit.com/
van der Velde KJ, Singh G, Kaliyaperumal R, Liao X, de Ridder S, Rebers S, Kerstens HD, de Andrade F, van Reeuwijk J, De Gruyter F, Hiltemann S, Ligtvoet M, Weiss M, van Deutekom HM, Jansen AL, Stubbs A, Vissers LLM, Laros JJ, van Enckevort E, Stemkens D, ‘t Hoen PC, Beliën JM, van Gijn M, Swertz M (2022)
FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research
.
Scientific Data
9
(
1
). https://doi.org/10.1038/s41597-022-01265-x
Větrovský T, Kohout P, Kopecký M, Machac A, Man M, Bahnmann BD, Brabcová V, Choi J, Meszárošová L, Human ZR, Lepinay C, Lladó S, López-Mondéjar R, Martinović T, Mašínová T, Morais D, Navrátilová D, Odriozola I, Štursová M, Švec K, Tláskal V, Urbanová M, Wan J, Žifčáková L, Howe A, Ladau J, Peay KG, Storch D, Wild J, Baldrian P (2019)
A meta-analysis of global fungal distribution reveals climate-driven patterns
.
Nature Communications
10
(
1
). https://doi.org/10.1038/s41467-019-13164-8
Větrovský T, Morais D, Kohout P, Lepinay C, Algora C, Awokunle Hollá S, Bahnmann BD, Bílohnědá K, Brabcová V, D’Alò F, Human ZR, Jomura M, Kolařík M, Kvasničková J, Lladó S, López-Mondéjar R, Martinović T, Mašínová T, Meszárošová L, Michalčíková L, Michalová T, Mundra S, Navrátilová D, Odriozola I, Piché-Choquette S, Štursová M, Švec K, Tláskal V, Urbanová M, Vlk L, Voříšková J, Žifčáková L, Baldrian P (2020)
GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies
.
Scientific Data
7
(
1
). https://doi.org/10.1038/s41597-020-0567-7
Voora V, Larrea C, Bermudez S (2020)
Global Market Report: Bananas
. https://www.iisd.org/system/files/publications/ssi-global-market-report-banana.pdf. Accessed on: 2021-6-12.
Wilkinson M, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J, da Silva Santos LB, Bourne P, Bouwman J, Brookes A, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo C, Finkers R, Gonzalez-Beltran A, Gray AG, Groth P, Goble C, Grethe J, Heringa J, ’t Hoen PC, Hooft R, Kuhn T, Kok R, Kok J, Lusher S, Martone M, Mons A, Packer A, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S, Schultes E, Sengstag T, Slater T, Strawn G, Swertz M, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016)
The FAIR Guiding Principles for scientific data management and stewardship
.
Scientific Data
3
(
1
). https://doi.org/10.1038/sdata.2016.18
Wilson S, Way G, Bittremieux W, Armache J, Haendel M, Hoffman M (2021)
Sharing biological data: why, when, and how
.
FEBS Letters
595
(
7
):
847
‑
863
. https://doi.org/10.1002/1873-3468.14067

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