File Structure Research Articles

INDE METADATA CONFORMITY INDICATOR

Metadata represents a set of descriptive information about the data, which aims at facilitating the search, access and use of data. The metadata standards specify the minimum set of elements to be informed and the file structure to ensure interoperability among catalogs. The present work aims at analyzing the adherence of the metadata published in the Brazilian Spatial Data Infrastructure (SDI) repository to the MGB Profile (in Portuguese, Perfil de Metadados Geoespaciais do Brasil) through a conformity indicator. Conformity was evaluated for over 30,000 metadata inserted in the INDE catalog (in Portuguese, Infraestrutura Nacional de Dados Espaciais), in June 2018, considering the set of mandatory and conditional elements of the summarized MGB Profile. These elements were collected from a harvester elaborated by the authors capable of scanning all the metadata of the repository and returning the fields organized into a CSV file. After analysis, it was found that only 28% of metadata conforms to the summarized MGB Profile. The low conformity rate suggests the limited understanding of the standards recommended, regarding both the minimum elements to be informed about each product and the structuration of the information in XML.

Docking with SwissDock.

Protein-ligand docking simulation is central in drug design and development. Therefore, the development of web servers intended to docking simulations is of pivotal importance. SwissDock is a web server dedicated to carrying out protein-ligand docking simulation intuitively and elegantly. SwissDock is based on the protein-ligand docking program EADock DSS and has a simple and integrated interface. The SwissDock allows the user to upload structure files for a protein and a ligand, and returns the results by e-mail. To facilitate the upload of the protein and ligand files, we can prepare these input files using the program UCSF Chimera. In this chapter, we describe how to use UCSF Chimera and SwissDock to perform protein-ligand docking simulations. To illustrate the process, we describe the molecular docking of the competitive inhibitor roscovitine against the structure of human cyclin-dependent kinase 2.

AUTOMINv1.0: an automation for minimization of Protein Data Bank files and its usage.

Global minimal structure of protein/enzyme is energetically compromised that maintains an intricate balance between the rigidity and the flexibility. Such a state makes it interactive to its ligand molecules. Although protein data bank files (PDB) may have achieved the state, in many situations minimization has been crucial to overcome unwanted steric clashes, and other conformational strains. It is more so, when orthologous PDB structures that are intended in a given study, show variations in resolution, R-factor, shell-water contents, loop characteristics etc. Here, a fully automated procedure of minimization would be highly useful. AUTOMINv1.0 is such an automation of minimization that runs on any number of structure files with any number of chains in them along with the inclusion of selective/non-selective shell-waters interacting with polar and or non-polar atom-types of protein. Comparison of the mean binaryitems of salt-bridges of minimized and un-minimized structures (chains > 100) of nucleoside diphosphate kinase from mimi virus shows dramatic improvements in the earlier. Again, the mean steric clashes of 2AZ3.pdb are reduced upon minimization. Remarkably, the observed steric clashes between shell-waters and atom-types of protein are seen to be removed upon minimization. Taken together, AUTOMINv1.0 is an automation of minimization that finds applications in structural bioinformatics.

Effective XQuery keyword using XML query processing

<span>The data has structured is determined using the standard is known as XML whereaslarge amount of data has consumed through internet consist of the both structural data format as well as semi structural data format which gets stored and processed whereas XML allow the data of semi-structured and hierarchical data representation not only consist of concept with individual items from various kind of database but also have relationship among data items. The utilized knowledge bed is provided with concise ideas for both structured and semi structured data files, XML document contents and rapid with exact solutions for the queries required at any time. The user can search their resources with the help of queries. Searching the resources with the help of queries is not a simple task, where inaccurate result and complexity would occur. Hence it is not a better way for searching the resources. This paper proposes the query answering system of Linear search usingwild card searchfor extracting the frequent pattern to maximize your search results in library database on XML document to extract the most relevant feeds from the large file directly. It will help the user to find his resources completely.</span>

167. Evaluation of a radiotherapy-dedicated Monte Carlo environment (PRIMO) for verification of clinical VMAT plans for brain and lung metastases

PurposePRIMO is a graphical environment for MonteCarlo (MC) simulations. The Dose Planning Method (DPM) is a fast MC algorithm for the simulation of the deposited dose under radiotherapy conditions. The objective of this work was to validate the DPM-calculated beams against our LINAC EDGE and compare a clinical algorithm (Acuros, Varian) with PRIMO DPM MC in two different clinical scenarios. MethodsThe beams were evaluated in terms of profiles, PDDs, Output Factors (OF), Dosimetric Leaf Gap (DLG) and transmitted dose of the 120 HD MLC. PDDs were also evaluated in a slab-phantom with different materials. A set of 20 patients was selected for this study: 10 patients with brain metastases and 10 with lung metastases. For all patients VMAT plans with 10 MV FFF beam were optimized in Eclipse and calculated with Acuros. The DICOM files (plan, structure and images) were imported in PRIMO. DPM was used to calculate the dose distribution in the patients. The plans were compared in terms of DVHs through Homogeneity (HI) and Gradient (GI) Indexes. ResultsThe agreement between simulated and real beam was good with differences ⩽1.5% for all parameters considered. Some differences were observed in the MLC modeling. PRIMO showed a larger DLG with values ∼2 mm greater than both Acuros and the related experimental measurements (Fig. 1).The analysis of PDDs in the slab-phantom materials showed discrepancies of ∼1.2% between Acuros and PRIMO in cartilage and lung (Fig. 2). The MC dose distributions of the VMAT plans always had mean uncertainties within the PTV < 1.5%. Comparing the VMAT plans an overall good agreement was observed. ConclusionsPRIMO is an interesting tool for the clinical environment, useful to verify and support the commercially available TPS. The agreement with Acuros is good though further analysis should be done.

Comparing psychopathology rates across autism spectrum disorders and intellectual disabilities

PurposeThe purpose of this paper is to compare the rates and patterns of comorbid anxiety, depression and ADHD across people with: autism spectrum disorder (ASD) plus intellectual disability (ID), ASD/no ID (AS/HFA) and ID only.Design/methodology/approachData were obtained using structured file review and two psychometric screening instruments. File reviews used a modified version of the Case Review Protocol from the Diagnostic Criteria for Psychiatric Disorders for Use with Adults with Learning Disabilities. Screening employed the Reiss Screen for Maladaptive Behaviour and the ASD-Comorbidity-Adults scale.FindingsLevels of anxiety, depression and ADHD were high for all three groups, and greater than in general population studies. Between groups, the AS/HFA sample had significantly higher anxiety, while the ASD/ID group had significantly greater ADHD. Groups did not differ on measures of depression or total psychopathology. Levels of ADHD, but not anxiety or depression, increased with severity of ID and ASD symptoms.Research limitations/implicationsMore research is needed on the relationship between ASD and both anxiety and ADHD, particularly the identification of these comorbid conditions and their treatment.Practical implicationsRigorous screening for comorbid mental health conditions is required to achieve the best outcomes for adults with ASD.Originality/valueThe study used a broader community-based sample than previous comorbidity research. Including adults with ID but without ASD enabled a comparison of types and rates of disorder across three distinct but related groups with neurodevelopmental disorders.

IMPROVEMENT OF STEGOANALYSIS METHODS FOR DETERMINING TRAINING IN AUDIO FILES

The paper considers the improved methodology and the software developed for its implementation, which makes it possible to detect attachments, made with the help of the LSB algorithm, in some types of audio files of the WAVE format of any bit rate and sampling frequency. It also provides us with the possibility to determine the presence of stego attachments in certain types of audio files. The testing procedure is based on methods of determining stego attachments using the frequency method and compression method. The suggested methods of searching for attachments in file service areas and developed software in Embarcadero Delphi XE5 environment effectively reveal the fact of the implementation of the message created by the majority of existing programs. The software was also tested on a large number of files without attachments. As a result a large number of false positives were revealed. The reason for this was damaged structure of MP3 files (corrupted frames were taken for possible stego attachments).

Modelling and mitigation of single-event upset in CMOS voltage-controlled oscillator

Single-event effects (SEEs) have been the primary concern in study of radiation effects since late 1970s with the discovery of soft errors in terrestrial and space environments. The interaction of a single ionized particle with electronic devices leads to SEEs. In this paper, single-event upset (SEU) on CMOS devices in designing of a voltage-controlled oscillator (VCO) is analysed. Further, mitigation approaches of SEU are also discussed. To observe the impact of radiation, a VCO was designed in Cadence Virtuoso, and GDSII file of one ring oscillator stage was extracted to incorporate the same design in Silvaco MaskViews. With the help of layer map information file, masks were identified and used to design the CMOS inverter structure file for simulation of SEU condition. The input parameters for SEU simulation were evaluated from linear energy transfer (LET) graph of heavy ion under space conditions. The current profile of CMOS inverter was extracted under influence of a high-energy particle with the help of LET graph of that particle. This current profile was applied to different nodes of VCO and upset conditions were identified. Further, the impact of upset conditions on lock stage of phase-locked loop (PLL) is discussed. Results show that the SEU has significant impact on the logic state of inverters used in ring oscillator stage compared with current starving/biasing stage. The current profile of CMOS device has strong dependence on the energy of ion, its track, angle of incidence and the material. When angle of incidence is very less (\(7^{\circ }-14^{\circ }\)) the channel will be occupied by a funnel of charge and it leads to the maximum degradation of device. This work shows that a device operating at high frequency is more susceptible to SEU. Triple modular redundancy (TMR) and Radiation Hardened By Design (RHBD) can be used to mitigate SEU. TMR consumes more power and is less accurate compared with the RHBD approach.

Exploring the Impact of Code Smells on Fine-Grained Structural Change-Proneness

Code smells are used to describe the bad structures in the source code, which could hinder software maintainability, understandability and changeability. Nowadays, scholars mainly focus on the impact of smell on textual change-proneness. However, in comparison to textual changes, structural changes could better reveal the change nature. In practice, not all code change types are equally important in terms of change risk severity levels, and software developers are more interested in particular changes relevant to their current tasks. Therefore, we investigate the relationship between smells and fine-grained structural change-proneness to solve these issues. Our experiment was conducted on 11 typical open source projects. We first employed Fishers exact test and Mann–Whitney test to explore whether smelly files (affected by at least one smell type) had higher structural change-proneness than other files, and whether files with more smell instances are more likely to undergo structural changes, respectively. Multivariate logistic regression model was built to study the relation between each kind of smell and change-proneness with respect to five change categories. Our results showed that: (1) in most cases, smelly files were more prone to structural changes and files with more smell instances tend to undergo higher structural changes; (2) quite a few smell types were related to structural change-proneness, particularly, Refused Parent Bequest (RPB), Message Chains (MCH), Divergent Change (DIVC), Feature Envy (FE) and Shotgun Surgery (SS) increased structural changes for some change categories. However, when controlling the file size Lines of Code (LOC), significant change-proneness of some smells disappeared or the magnitude of significance decreased more or less.

Bigmelon: tools for analysing large DNA methylation datasets.

MotivationThe datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data.ResultsHere we introduce the bigmelon R package, which provides a memory efficient workflow that enables users to perform the complex, large scale analyses required in epigenome wide association studies (EWAS) without the need for large RAM. Building on top of the CoreArray Genomic Data Structure file format and libraries packaged in the gdsfmt package, we provide a practical workflow that facilitates the reading-in, preprocessing, quality control and statistical analysis of DNA methylation data.We demonstrate the capabilities of the bigmelon package using a large dataset consisting of 1193 human blood samples from the Understanding Society: UK Household Longitudinal Study, assayed on the EPIC micro-array platform.Availability and implementationThe bigmelon package is available on Bioconductor (http://bioconductor.org/packages/bigmelon/). The Understanding Society dataset is available at https://www.understandingsociety.ac.uk/about/health/data upon request.Supplementary information Supplementary data are available at Bioinformatics online.

Contextual modulation of prime response retrieval processes: Evidence from auditory negative priming

Contextual similarity between learning and test phase has been shown to be beneficial for memory retrieval. Negative priming is known to be caused by multiple processes; one of which is episodic retrieval. Therefore, the contextual similarity of prime and probe presentations should influence the size of the negative priming effect. This has been shown for the visual modality. In Experiment 1, an auditory four-alternative forced choice reaction time task was used to test the influence of prime-probe contextual similarity on negative priming and the processes underlying the modulation by context. The negative priming effect was larger when the auditory context was repeated than when it was changed from prime to probe. The modulation by context was exclusively caused by an increase in prime response retrieval errors in ignored repetition trials with context repetition, whereas repeating only the context but not the prime distractor did not lead to an increase in prime response retrieval. This exact pattern of results was replicated in Experiment 2. The findings suggest that contextual information is integrated with prime distractor and response information. Retrieval of the previous episode, including prime distractor, prime response, and context (event file), can be triggered when the former prime distractor is repeated, whereas a context cue alone does not retrieve the event file. This suggests an event file structure that is more complicated than its usually assumed binary structure.

Tooling framework for instantiating natural language querying system

Recent times have seen a growing demand for natural language querying (NLQ) interfaces to retrieve information from the structured data sources such as knowledge bases. Using this interface, business users can directly interact with a database without the knowledge of the query language or the data schema. Our earlier work describes a natural language query engine called ATHENA which has several shortcoming around ease of use and compatibility with data stores, formats and flows. In this demonstration paper, we present a tooling framework to address these challenges so that one can instantiate an NLQ system with utmost ease. Our framework makes it easy and practically applicable to all NLIDB scenarios involving different sources of structured data, file formats, and ontologies to enable natural language querying on top of them with minimal human configuration. We present the tool design and the solution to the challenges towards building such a system and demonstrate its applicability in the medical domain.

The Evidence Trace File: A Data Structure for Virtual Performance Assessments Informed by Data Analytics and Evidence‐Centered Design

AbstractVirtual performance assessments (VPAs), such as game‐ and simulation‐based assessments, provide promising ways for assessing complex and integrated skills. However, the high cost, long development cycle, and complex scoring process significantly hinder the adoption of VPAs, particularly in large‐scale assessments with tight deadlines and limited budgets. One significant impediment is the lack of consensus about how to capture and store evidence‐bearing process data from each VPA session into a format that facilitates quality checks, scoring, and analysis. In current practice, data from VPAs are generally stored in logs, either as standalone files or as a set of entries in a database. Programmers have long used logs to save process information about software systems for monitoring and debugging the software system itself. These purposes are fundamentally different from the purpose of assessment, which aims to capture evidence to support the measurement of a test taker's proficiency with respect to targeted constructs. Collapsing information that might be needed for the two purposes into a single log file can lead to confusion, even confrontation, among VPA designers, software developers, and scoring experts. To mitigate the problem, in this paper we introduce the notion of a structured evidence trace file (ETF) as distinguished from the familiar but idiosyncratic term, log file. The ETF is intended to express captured telemetry data in a way that meets the needs from the assessment perspective. Its specification as a standalone deliverable allows VPA designers and scoring experts to cleanly define and assure the quality of the information they need from VPA sessions in forms they find most useful for assessment purposes without adding additional complexity or structures for the information that software developers need for debugging purposes. In turn, software developers will be clearly informed about what information is expected from the assessment perspective and can manage the data as they choose as long as they can deliver the specified ETF. In addition to its application in VPAs, the ETF structure can also be employed more generally with digitally based assessments (DBAs) in which response‐process data are captured and used for assessment purposes, such as timing information, response changes, and tool usage in multiple‐choice tests. As a known data structure, it can sometimes also be employed for additional purposes in VPA analysis, such as transmitting information across phases of multiple‐stage analysis.

DVH Analytics: A DVH database for clinicians and researchers.

In this study, we build a vendor‐agnostic software application capable of importing and analyzing non‐image‐based DICOM files for various radiation treatment modalities (i.e., DICOM RT Dose, RT Structure, and RT Plan files). Dose‐volume histogram (DVH) and planning data are imported into a SQL database, and methods are provided to manage, edit, view, and download data. Furthermore, the software provides various analytical tools for plan evaluations, plan comparisons, benchmarking, and plan outcome predictions. DVH Analytics is developed using Python, including libraries such as pydicom, dicompyler, psycopg2, SciPy, Statsmodels, and Bokeh for parsing DICOM files, computing DVHs, communicating with a PostgreSQL database, performing statistical analyses, and creating a web‐based user interface. This software is open‐source and compatible with Windows, Mac OS, and Linux. For proof‐of‐concept, a database with over 3,000 DVHs from a single physician's head & neck practice was built. From these data, differences in means, correlations, and temporal trends in dose to multiple organs‐at‐risk (OARs) were observed. Furthermore, an example of the predictive regression tool is reported, where a model was constructed to predict maximum dose to brainstem based on minimum distance from planning target volume (PTV) and treatment beam source‐to‐skin distance (SSD). With DVH Analytics, we have developed a free, open‐source software program to parse, organize, and analyze non‐image‐based DICOM data for use in a radiation oncology setting. Furthermore, this software can be used to generate statistical models for the purposes of quality control or outcome predictions and correlations.

SAFlex: A structural alphabet extension to integrate protein structural flexibility and missing data information.

In this paper, we describe SAFlex (Structural Alphabet Flexibility), an extension of an existing structural alphabet (HMM-SA), to better explore increasing protein three dimensional structure information by encoding conformations of proteins in case of missing residues or uncertainties. An SA aims to reduce three dimensional conformations of proteins as well as their analysis and comparison complexity by simplifying any conformation in a series of structural letters. Our methodology presents several novelties. Firstly, it can account for the encoding uncertainty by providing a wide range of encoding options: the maximum a posteriori, the marginal posterior distribution, and the effective number of letters at each given position. Secondly, our new algorithm deals with the missing data in the protein structure files (concerning more than 75% of the proteins from the Protein Data Bank) in a rigorous probabilistic framework. Thirdly, SAFlex is able to encode and to build a consensus encoding from different replicates of a single protein such as several homomer chains. This allows localizing structural differences between different chains and detecting structural variability, which is essential for protein flexibility identification. These improvements are illustrated on different proteins, such as the crystal structure of an eukaryotic small heat shock protein. They are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility.

PROPAB: Computation of Propensities and Other Properties from Segments of 3D structure of Proteins.

Residues in allelic positions, in the local segment of aligned sequences of proteins show wide variations. Here, we describe PROPAB that computes the propensity tables for helix, strand and coil types from multiple 3D structure files following ab initio statistical procedure. It also classifies them in range specific and chain specific manners. It further computes percentage composition and physicochemical properties along with residues propensities. It also prepares FASTA files for different segments (helix, strand and coil) in the exact order that they follow in the sequence. Representative analyses on orthologous (homologous across species) proteins demonstrate wide segmental variations of physicochemical properties. Such variations provide insights to relate the adaptation of these proteins in a given functional constraint under diverse environmental conditions. Thus, the program finds applications in the structural and evolutionary analysis of proteins.Availability:PROPAB is freely available at http://sourceforge.net/projects/propab/for worldwide user.

The first 3D printed multiple sclerosis brain: Towards a 3D era in medicine

Conventional magnetic resonance imaging (MRI) studies depict disease of the human brain in 2D but the reconstruction of a patient’s brain stricken with multiple sclerosis (MS) in 3D using 2D images has not been attempted. Using 3D reconstruction algorithms, we built a 3D printed patient-specific brain model to scale. It is a first of its kind model that depicts the total white matter lesion (WML) load using T2 FLAIR images in an MS patient. The patient’s images in Digital Imaging and Communications in Medicine (DICOM) format were imported into Mimics inPrint 2.0 (Materialise NV, Leuven, Belgium) a dedicated medical image processing software designed for the purposes of image segmentation and 3D modeling. The imported axial images were automatically formatted to display coronal and sagittal slices within the software. The imaging data were then segmented into regions and surface rendering was done to achieve 3D virtual printable files of the desired structures of interest. Rendering brain tumor(s) in 3D has been attempted with the specific intent of extending the options available to a surgeon but no study to our knowledge has attempted to quantify brain disease in MS that has, for all practical purposes, no surgical options. The purpose of our study was to demonstrate that 3D depiction of chronic neurological diseases is possible in a printable model while serving a fundamental need for patient education. Medical teaching is moored in 2D graphics and it is time to evolve into 3D models that can be life-like and deliver instant impact.

Hierarchical BoW with segmental sparse coding for large scale image classification and retrieval

The bag-of-words (BoW) has been widely regarded as the most successful algorithms for content-based image related tasks, such as large scale image retrieval, classification, and object categorization. Large visual words acquired by BoW quantization through large vocabulary or codebooks have been receiving much attention in the past years. However, not only construction of large vocabulary but also the quantization process impose a heavy burden in terms of time and memory complexities. In order to tackle this issue, we propose an efficient hierarchical BoW (HBoW) to achieve large visual words through quantization by a compact vocabulary instead of large vocabulary. Our vocabulary is very compact since it is only composed of two small dictionaries which is learned through segmental sparse decomposition of local features. To generate the BoW with large size, we first divide the local features into two half parts, and use the two small dictionaries to compute their sparse codes. Then, we map the two indices of the maximum elements of the two sparse codes to a large set of visual words based upon the fact that data with similar properties will share the same base weighted with the largest sparse coefficient. To further make similar patches have higher probability of select the same dictionary base to get similar BoW vectors, we propose a novel collaborative dictionary learning method by imposing the similarity regularization factor together with the row sparsity regularization across data instances during group sparse coding. Additionally, based on index combination of top-2 large sparse codes of local descriptors, we propose a soft BoW assignment method so that our proposed HBoW can tolerate different word selection for similar patches. By employing the inverted file structure built through our HBoW, K-nearest neighbors (KNN) can be efficiently retrieved. After incorporation of our fast KNN search into the SVM-KNN classification method, our HBoW can be used for efficient image classification and logo recognition. Experiments on serval well-known datasets show that our approach is effective for large scale image classification and retrieval.

Efficient NN Spatial Keyword Search Using Spatial Inverted (SI) Index

"Spatial information mining", or learning revelation in spatial database, alludes to the illustration out of concealed information, spatial relations, or different examples that are not unequivocally put away in spatial databases. To get to the spatial database alongside the catchphrase another kind of inquiry called spatial watchword question is utilized. A spatial watchword inquiry get client area and client given catchphrases as contentions and gives web protests that are spatially and literarily material to these information. The current answers for such inquiries depend on IR2-tree that has a couple of inadequacies as space utilization and event of false hit is extremely huge when the question of the last outcome is far from the inquiry point .To beat this issue a novel file structure called Spatial Inverted file is proposed. Presently a-days use of portable is expanding enormously .In the versatile system an intermediary is set between base station and Location Based Server (LBS).This intermediary utilizes the Spatial modified file procedure to answer the SK inquiry by utilizing spatial data from the base station and printed data from the client question. The outcome from the SI record is given to two file structure in the intermediary called EVR Tree and Grid list. The Estimated Valid Region (EVR) for the present area of the client and required spatial articles are produced and come back to the client. On the off chance that the EVR is absent in the two file structure of intermediary it offer question to LBS. In the event that the client given inquiry is miss written or miss spelled it can be oversee by SI record utilizing n gram/2L Approximation file.

Experimental Directory Structure (Exdir): An Alternative to HDF5 Without Introducing a New File Format.

Natural sciences generate an increasing amount of data in a wide range of formats developed by different research groups and commercial companies. At the same time there is a growing desire to share data along with publications in order to enable reproducible research. Open formats have publicly available specifications which facilitate data sharing and reproducible research. Hierarchical Data Format 5 (HDF5) is a popular open format widely used in neuroscience, often as a foundation for other, more specialized formats. However, drawbacks related to HDF5's complex specification have initiated a discussion for an improved replacement. We propose a novel alternative, the Experimental Directory Structure (Exdir), an open specification for data storage in experimental pipelines which amends drawbacks associated with HDF5 while retaining its advantages. HDF5 stores data and metadata in a hierarchy within a complex binary file which, among other things, is not human-readable, not optimal for version control systems, and lacks support for easy access to raw data from external applications. Exdir, on the other hand, uses file system directories to represent the hierarchy, with metadata stored in human-readable YAML files, datasets stored in binary NumPy files, and raw data stored directly in subdirectories. Furthermore, storing data in multiple files makes it easier to track for version control systems. Exdir is not a file format in itself, but a specification for organizing files in a directory structure. Exdir uses the same abstractions as HDF5 and is compatible with the HDF5 Data Model. Several research groups are already using data stored in a directory hierarchy as an alternative to HDF5, but no common standard exists. This complicates and limits the opportunity for data sharing and development of common tools for reading, writing, and analyzing data. Exdir facilitates improved data storage, data sharing, reproducible research, and novel insight from interdisciplinary collaboration. With the publication of Exdir, we invite the scientific community to join the development to create an open specification that will serve as many needs as possible and as a foundation for open access to and exchange of data.