File Metadata Research Articles

A flux tower site attribute dataset intended for land surface modeling

Abstract. Land surface models (LSMs) require reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are widely regarded as the benchmark for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets frequently lack site-observed attribute data, such as fractional vegetation cover and leaf area index, which limits their utility as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, energy balance closure (EBC), and external disturbances such as irrigation and deforestation, leading to 90 high-quality sites. For these sites, we collected vegetation, soil, and topography data as well as wind speed, temperature, and humidity measurement heights from literature; regional networks; and Biological, Ancillary, Disturbance, and Metadata (BADM) files. We then compiled the final flux tower attribute dataset by filling in missing attributes with global data and classifying plant functional types (PFTs). This dataset is provided in NetCDF (Network Common Data Form) format with necessary descriptions and reference sources. Model simulations revealed substantial disparities in the output between the attribute data observed at the site and those commonly used by LSMs, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of the site attribute to some extent, reduces uncertainty in LSM data source, and aids in diagnosing parameter and process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.12596218 (Shi et al., 2024).

Efficient File Security Scanning: Combining Hash Histories and Bloom Filters to Minimize Redundancy

As file repositories expand in size, traditional full-file security scanning becomes computationally expensive and redundant. This paper introduces a novel hybrid framework that leverages hash histories embedded in file metadata and Bloom filters for efficient security scanning. The approach ensures that only modified or newly added files are scanned, reducing overhead while maintaining robust security coverage. By augmenting file metadata with hash histories, the system provides decentralized tracking of file state changes. Bloom filters further optimize the process by efficiently determining whether a file requires scanning. The tradeoff between increased file sizes due to metadata augmentation and the improved scanning performance is thoroughly analyzed. Evaluations demonstrate significant reductions in scan time and resource usage, making the framework highly suitable for large-scale file repositories. Keywords File security, Hash histories, Bloom filters, Metadata augmentation, Incremental scanning, Security optimization

Big data and machine learning in digital forensics: Predictive technology for proactive crime prevention

The integration of big data analytics and machine learning [ML] has transformed digital forensics, enabling predictive technologies that proactively prevent cybercrimes and enhance crime investigation processes. As cyber threats grow in scale and complexity, traditional forensic methods face limitations in scalability, efficiency, and accuracy. Big data platforms, such as Hadoop and Spark, combined with advanced ML techniques, offer revolutionary approaches to address these challenges. By analysing vast datasets—including network traces, file metadata, and logs—big data enables the identification of hidden patterns and trends in criminal activities. Simultaneously, ML models, such as supervised and unsupervised learning algorithms, facilitate anomaly detection, predictive risk assessment, and real-time analysis. This article explores how the synergy of big data and ML advances digital forensics by automating large-scale forensic processes, identifying potential cyber threats, and predicting high-risk scenarios. It also discusses the integration of predictive technologies into forensic frameworks, examining the role of automation and real-time analytics in strengthening investigations. The challenges of implementing big data and ML, including issues of data quality, ethical concerns, and legal compliance, are critically analysed. Finally, the article highlights future trends, such as the role of quantum computing and explainable AI, in shaping the future of digital forensics. By providing a comprehensive overview of the field, this study emphasizes the transformative potential of big data and ML in proactive crime prevention, offering insights for researchers, regulators, and industry stakeholders.

Research Result Differences Between Manual Research and Using AI LLMs for Analyzing Popular 3D File Formats

A literature review has been conducted, helping outline some of the main peculiarities that exists within the 3D object file format domain. The difficulties that are present when working with different file formats in various fields have been noted – incompatibility issues, data loss, conversion difficulties, software wrappers, manufacturing industry standards and more. The problem of file format comparison has been defined – workflows are bogged down by the different output software tools use, expertise in different fields is a requirement, difficulties with conversion, complexity of file formats themselves and proprietary solutions, all lead to tremendous amount of manual work and technical knowledge required to keep track of differences between 3D file formats. As web sources for file format information can often become unreliable with time, new sources become available and analysis are constantly made, an argument has been made to try and use LLMs to alleviate some or most of the workload in file comparison. Such research necessitates a researcher to manually check all the output data of a model, in order to verify the correctness and accuracy of the used model. A methodology for the research process has been developed and implemented. Its main points include the definition of criteria for comparison, using manufacturing industry expert opinion to further verify the validity of the researcher and LLMs statements and the criteria itself, creation of comparison tables based on the criteria for all the chosen formats, short descriptions of the file formats themselves by the researcher and by the LLM and their comparison, as well as an analysis of file formats searchability on google trends. A comparison of binary data for some file formats is shown and described for further understanding of the issues with metadata and understanding proprietary 3D file formats. ISO standards for STEP ISO 10303-242:2022 and IGES, as well as their effects on the industry are briefly discussed. File formats that are discussed and compared: OBJ (Waveform obj, .obj); FBX (Filmbox, .fbx); glTF (GL Transmission Format, .glTF, .glb); USD/USDZ (Universal Scene Descriptor, .usd, .usdz); BLEND (Blender file format, .blend); AMF (Additive Manufacturing, .amf); STL (Stereolithography, .stl); 3DS (3D Studio Scene, .3ds); IGES (Initial Graphics Exchange Specification, .igs, .iges); STEP (Standard for the Exchange for Product Data, .stp, .step); DAE, Collada (Digital Asset Exchange File, .dae).

A comprehensive RGB-D dataset for 6D pose estimation for industrial robots pick and place: Creation and real-world validation

In the field of robotic grasping, 2D pose estimation algorithms are outdated and insufficient for modern requirements. Transitioning to 6D pose estimation of objects offers, particularly through deep learning methods, significantly improved performance. However, most high-performance 6D algorithms lack publicly disclosed methods for creating the necessary datasets, presenting challenges for researchers. This study introduces a methodology for creating a sample dataset for 6D pose estimation, addressing the challenges researchers face when applying these algorithms to specific projects. RGB and depth images are captured using Intel® RealSense™ Depth D435 camera, forming the foundation for accurately determining the pose of each object in the dataset. A CAD model along with accompanying metadata files, is generated to provide a complete dataset. The sample dataset was tested on two algorithms: EfficientPose and FFB6D, achieving accuracy rates of 97.05 % and 98.09 % respectively. These results indicate that the dataset is both effective and applicable to real-world robotic grasping tasks. Our research offers substantial practical value, enabling researchers and engineers to easily apply state-of-the-art 6D pose estimation algorithms to fields such as conveyor belt robotic picking, medical automation, and various other applications.

Traits.build: A data model, workflow and R package for building harmonised ecological trait databases

Trait databases have proliferated over the past decades, facilitating research on the ecology, evolution, and conservation of taxa across the Tree of Life. Typically, teams of independent researchers build these databases, and each must develop their own workflow and output structure. This divests research hours from downstream tasks such as trait-based analysis and interpretation and the resultant datasets are often difficult to integrate due to disparate database structures. Here we introduce the {traits.build} R-package, which offers a generalised workflow for building trait databases. {traits.build} contains bespoke functions for propagating metadata files, extensive tutorials, and sample configuration files, allowing researchers to efficiently build a new trait database using open source tools. In addition, the {traits.build} output structure is fully documented by a data model, ensuring the meaning of each variable and semantic relationship between variables is transparent and consistent. The data standard links to terms in previously published data standards, drawing strongly on DarwinCore and the Ecological Trait-data Standard, but also includes the ability to fully map location and context properties absent from these vocabularies. It is the first published database-building workflow that adheres to the Extensible Observation Ontology. Simultaneously developing a generalised workflow and publishing a data standard for the workflow provides {traits.build} users a straightforward pathway to build a new trait database that achieves the FAIR principles. The meaning of all variables in a {traits.build} database are already documented, allowing further integration with either other {traits.build} databases or indeed any other database with a documented data model. This follows the vision of the Open Traits Network to build trait databases whose data can be easily integrated for further analysis.

Coral skeletal proxy records database for the Great Barrier Reef, Australia

Abstract. The Great Barrier Reef (GBR), Australia, has a long history of palaeoenvironmental coral research. However, it can be logistically difficult to find the relevant research and records, which are often unpublished or exist as “grey literature”. This hinders researchers' abilities to efficiently assess the current state of coral core studies on the GBR and thus identify any key knowledge gaps. This study presents the Great Barrier Reef Coral Skeletal Records Database (GBRCD), which compiles 208 records from coral skeletal research conducted since the early 1990s. The database includes records from the Holocene, from ∼8000 years ago, to the present day; records are from the northern, central, and southern GBR from inshore and offshore locations. Massive Porites spp. coral records comprise the majority (92.5 %) of the database, and the remaining records are from Acropora, Isopora, or Cyphastrea spp. The database includes 78 variables, with Sr/Ca, U/Ca, and Ba/Ca being the most frequently measured. Most records measure data over 10 or more years and are at monthly or lower resolution. The GBRCD is machine readable and easily searchable so that users can find records relevant to their research, e.g. by filtering for site name, time period, or coral type. It is publicly available as comma-separated values (CSV) data and metadata files with entries linked by the unique record ID and as Linked Paleo Data (LiPD) files. The GBRCD is publicly available from the NOAA National Center for Environmental Information World Data Service for Paleoclimatology at https://doi.org/10.25921/hqxk-8h74 (Arzey et al., 2024). The intention is to update the GBRCD annually, depending on the availability of relevant new GBR records or submission of legacy records to the GBRCD for archiving. The collection and curation of existing GBR coral research data provide researchers with the ability to analyse common proxies such as Sr/Ca across multiple locations and/or examine regional to reef-scale trends. The database is also suitable for multi-proxy comparisons and combination or composite analyses to determine overarching changes recorded by the proxies. This database represents the first comprehensive compilation of coral records from the GBR. It enables the investigation of multiple environmental factors via various proxy systems for the GBR, northeastern Australia, and potentially the broader Indo-Pacific region.

Digital Signature Method for Images without Additional Files, Robust to JPEG Compression and Metadata Removal

The article discusses a method of digitally signing images that does not use metadata or additional files. The object for signature is an array of 8x8 pixel blocks with a discrete cosine transformation applied, which make up a JPEG file. Any known algorithm can be used as a signature algorithm, for example, RSA.The resulting digital signature is converted from a binary form to an image. The conversion method is based on encoding every few bits of the signature of one of the 64 basic functions of the discrete cosine transform. Next, the basic function is transformed into an image by an inverse discrete cosine transformation. The resulting signature, encoded as an image, is attached to the original image on the right to form a signed image.The digital signature is resistant to JPEG compression within adjustable limits. The following method is used to achieve compression resistance. Since the values in the blocks can change during compression, quantization is used – reducing the accuracy of the values. It is designed in such a way that when quantizing compressed and uncompressed images, the values in the blocks after quantization are becoming the same, which allows you to verify the validity of the digital signature. Quantization also includes a step of checking the parity of the received value to avoid misinterpretation of values when compression is strong.The quantization step does not apply to the part of the image containing the digital signature. This is due to the fact that this part consists of the basic functions of the discrete cosine transform, and even with strong compression, the corresponding basic function will still retain a large coefficient and will be unambiguously interpreted.

Video source identification using machine learning: A case study of 16 instant messaging applications

In recent years, there has been a notable increase in the prevalence of cybercrimes related to video content, including the distribution of illegal videos and the sharing of copyrighted material. This has led to the growing importance of identifying the source of video files to trace the owner of the files involved in the incident or identify the distributor. Previous research has concentrated on revealing the device (brand and/or model) that “originally” created a video file. This has been achieved by analysing the pattern noise generated by the image sensor in the camera, the storage structural features of the file, and the metadata patterns. However, due to the widespread use of mobile environments, instant messaging applications (IMAs) such as Telegram and Wire have been utilized to share illegal videos, which can result in the loss of information from the original file due to re-encoding at the application level, depending on the transmission settings. Consequently, it is necessary to extend the scope of existing research to identify the various applications that are capable of re-encoding video files in transit. Furthermore, it is essential to determine whether there are features that can be leveraged to distinguish them from the source identification perspective. In this paper, we propose a machine learning-based methodology for classifying the source application by extracting various features stored in the storage format and internal metadata of video files. To conduct this study, we analyzed 16 IMAs that are widely used in mobile environments and generated a total of 1974 sample videos, taking into account both the transmission options and encoding settings offered by each IMA. The training and testing results on this dataset indicate that the ExtraTrees model achieved an identification accuracy of approximately 99.96 %. Furthermore, we developed a proof-of-concept tool based on the proposed method, which extracts the suggested features from videos and queries a pre-trained model. This tool is released as open-source software for the community.

Peat profile database from peatlands in Canada.

Peatlands cover approximately 12% of the Canadian landscape and play an important role in the carbon cycle through their centennial- to millennial-scale storage of carbon under waterlogged and anoxic conditions. In recognizing the potential of these ecosystems as natural climate solutions and therefore the need to include them in national greenhouse gas inventories, the Canadian Model for Peatlands module (CaMP v. 2.0) was developed by the Canadian Forest Service. Model parameterization included compiling peat profiles across Canada to calibrate peat decomposition rates from different peatland types, to define typical bulk density profiles, and to describe the hydrological (i.e., water table) response of peatlands to climatic changes. A total of 1217 sites were included in the dataset from published and unpublished sources. The CORESITES table contains site location and summary data for each profile, as well as an estimate of total carbon mass per unit area (in megagrams of C per hectare). Total carbon mass per unit area at each location was calculated using bulk density and carbon content through each profile. The PROFILES table contains data for depth (in centimeters), bulk density (in grams per cubic meter), ash and carbon content (in percentage), and material descriptions for contiguous samples through each peat profile. Data gaps for bulk density and C content were filled using interpolation, regression trees, and assigned values based on material description and/or soil classification to allow for the estimation of total carbon mass per unit area. A subset of the sites (N = 374) also have pH and pore water trace-elemental geochemistry data and are found in the WATER table. The REFERENCES table contains the full citation of each source of the data and is linked to each core location through the SOURCEDATA table. The LOOKUP table defines codes in the database that required more space that what was sufficient in the metadata tables. The data can be accessed on Open Government Canada and will be useful for future work on carbon stock mapping and ecosystem modeling. All metadata and data are provided © Her Majesty the Queen in Right of Canada, 2023 and information contained in this publication may be reproduced for personal or public noncommercial purposes with attribution, whereas commercial reproduction and distribution are prohibited except with written permission from NRCan; complete details are noted in the Supporting Information file Metadata S1 (see Class III.B.3: Copyright restrictions).

PyDwCA: A Tool for Integrating Biodiversity Data

The Darwin Core Archive (DwC-A) format, based on the Darwin Core standard (Wieczorek et al. 2012), facilitates the exchange, management, and integration of biodiversity data from multiple sources. This ability to collate biodiversity data allows datasets to be aggregated at community-supported infrastructures, merged in different combinations, meta-analyzed and submitted to public repositories (Baker et al. 2014). Thus, the DwC-As serve as unifying archives in concatenated collective efforts, such as biodiversity inventories at different spatial and taxonomic scales. Here we describe PyDwCA*1, 2, a Python library implemented to handle the "star scheme" of DwC-A. This new library reads compressed zip files containing the expected meta.xml and uses it to assign the core component and its extensions. It also provides Python classes to define the core, the extensions, and the metadata file for creating an archive and writing it into a compressed zip file. PyDwCA also implements functionality to select, filter and merge DwC-A files. We present this new tool in the context of the construction of the Chilean National Biodiversity Inventory (Fig. 1), but PyDwCA serves as a versatile technical solution applicable to different contexts in the field of biodiversity informatics (e.g., integration of datasets from biological collection and sampling events). To exemplify how PyDwCA works, we present the step-by-step integration of the Chilean Catalogue of Vascular Plants (Rodriguez et al. 2018) on a matrix provided by the Catalogue of Life (Banki 2024), filtered with the species with occurrences recorded for Chile in the Global Biodiversity Information Facility (GBIF) (GBIF.Org 2023).

Implementation of the Frictionless data standard for linked data

Objective and ApproachData linkage centres receive, hold and provision data in a wide variety of formats. Using a consistent, flexible and open metadata standard for data holdings can enable a center to clearly and unambiguously describe their data holdings and facilitate processing pipelines. One such framework is the Frictionless data standard. We present our experience at our data centre implementing this standard for both internal data management and for communication with external data providers and third parties. ResultsOur team has replaced tool-specific metadata files with standard-compliant files to make our pipelines more modular and less fragile. Since the standard is based on JSON, most programming languages will support reading and writing metadata files and we are not restricted to any software. This work has also made our internal tooling interoperable with that of external partners. We find that the standard is well equipped to handle CSV and other delimited files. Our center uses a mix of file types including fixed-width text files which do not have an explicit specification within the standard, but its extensibility has allowed us to define our own file type while staying within the ecosystem. We have also developed tools to convert JSON metadata files to excel workbooks for a more human-friendly format. ConclusionsThe Frictionless data standard can be a powerful tool for data centers in organizing data and building processing pipelines. ImplicationsAdopting the Frictionless data standard for metadata files has streamlined our internal processes and improved internal and external communication.

GelBox: open-source software to improve rigor and reproducibility when analyzing gels and immunoblots.

GelBox is open-source software that was developed with the goal of enhancing rigor, reproducibility, and transparency when analyzing gels and immunoblots. It combines image adjustments (cropping, rotation, brightness, and contrast), background correction, and band-fitting in a single application. Users can also associate each lane in an image with metadata (for example, sample type). GelBox data files integrate the raw data, supplied metadata, image adjustments, and band-level analyses in a single file to improve traceability. GelBox has a user-friendly interface and was developed using MATLAB. The software, installation instructions, and tutorials, are available at https://campbell-muscle-lab.github.io/GelBox/.NEW & NOTEWORTHY GelBox is open-source software that was developed to enhance rigor, reproducibility, and transparency when analyzing gels and immunoblots. It combines image adjustments (cropping, rotation, brightness, and contrast), background correction, and band-fitting in a single application. Users can also associate each lane in an image with metadata (for example, sample type).

An archive‐based method for efficiently handling small file problems in HDFS

SummaryHadoop distributed file system (HDFS) performs well when storing and managing large files. However, its performance significantly decreases when dealing with massive small files. In response to this problem, a novel archive‐based solution is proposed. The archive refers to merging multiple small files into larger data files, which can effectively reduce the memory usage of the NameNode. The current archive‐based solutions have the disadvantages of long access time, long archive construction time, and no support for storage, updating and deleting small files in the archive system. Our method utilizes a dynamic hash function to distribute the metadata of small files across multiple metadata files. We construct a primary index that combines dynamic and static indexes for these metadata files. Regarding data files, include some read‐only files and one readable–writable file. A small file's contents are written into a readable and writable file. Upon reaching a predetermined threshold, the readable–writable file transitions into read‐only status, with a fresh readable–writable file replacing it. Experimental results show that the scheme improves the efficiency of archive access and archive creation and is more efficient than the original HDFS storage and update efficiency.

ВОЗМОЖНОСТИ ПОЛУЧЕНИЯ И ИСПОЛЬЗОВАНИЯ КРИМИНАЛИСТИЧЕСКИ ЗНАЧИМОЙ ИНФОРМАЦИИ ИЗ МЕТАДАННЫХ ФАЙЛОВ ПРИ РАСКРЫТИИ И РАССЛЕДОВАНИИ ПРЕСТУПЛЕНИЙ

Modern technologies and the digitalization of society have led to the fact that most of the information in the world is stored and transmitted electronically. This circumstance opens up new opportunities for law enforcement agencies in the detection and investigation of crimes. In today's world, where technology plays a key role in society, digital footprints are becoming an important element in crime investigation. Everyone has already got used to the fact that the main object of computer expertise in the field of computer information is the contents of files. However, such an important area as the study of file metadata remains underestimated in the investigation process. Metadata is an integral part of mod-ern crime investigation, providing investigators with valuable data for analysis and helping to restore the chronology of events and actions of persons associated with the crime. In the field of digital fo-rensics, special attention should be paid to the study and analysis of electronic traces that can be found in metadata. The issues of using file metadata have been touched upon in one way or another by many scien-tists, such as L.A. Buraeva [1], V.A. Gauzhaeva [2], R.R. Kardanov [3], Yu.M. Usoltsev [4], A.S. Arutyunov [5] and others. However, these authors made only an attempt to draw attention to the use of metadata in the investigation of crimes. Metadata may contain information about the time the file was created, its author, location, and other data that can be used in proving criminal cases. Metadata in the commission of crimes related to the use of information and telecommunication technologies can be compared with micro-objects in the commission of traditional crimes. Both are invisible traces, which are not so easy to destroy and to which few criminals pay attention. At the same time, they can provide significant assistance to the investigation in uncovering and investigat-ing crimes. This article attempts to draw the attention of the scientific world and employees involved in the preliminary investigation of crimes to the problems and possibilities of using metadata in the process of disclosure and investigation of crimes.

Towards a practical usage for the Sleuth Kit supporting file system add-ons

Most modern digital devices with storage utilize a file system to manage files and directories. Consequently, when digital forensic investigators derive evidence from such devices, they collect and analyze data stored on them through file system analysis. However, there are numerous types of file systems, with new ones continually being developed. Each file system possesses a distinct metadata structure and file management system. Therefore, investigators must possess prior knowledge of the specific file system being examined. Nevertheless, it is challenging for practitioners to be knowledgeable about all existing file systems. To address this issue, forensic software such as The Sleuth Kit (TSK), an open-source forensic tool, is employed for investigations. However, even these tools may not offer complete support for relatively recent file systems.Hence, we propose a structure for integrating a new file system into the open-source forensic tool TSK. Additionally, to validate this proposed structure, we demonstrate that support for five file systems (Ext4, XFS, Btrfs, F2FS, and Hikvision) can be added following this framework. To achieve this, we conducted an analysis of the metadata and file management scheme for these five file systems. Furthermore, we examined the operational procedures of the TSK framework. Based on these analyses, investigation capabilities for the five file systems have been incorporated into TSK. Moreover, reliability verification experiments were conducted on the developed tools; and performance evaluation was carried out in comparison with other commercial digital forensic tools. The findings of this study can serve as a foundation for future forensic studies based on file systems. Additionally, the TSK developed based on the proposed structure can assist investigators in conducting digital forensics effectively.

Optimizing Storage Efficiency Through Pipelining and Parallelization for Enhanced Data Deduplication

Nowadays, huge devices are connected to the internet to deal with many cloud environments for storing data efficiently. Also, it is used to store big data determined in business activities. However, the data storage in the cloud avoids duplicate copies of files and reduces complexity issues. However, this platform is utilized by many users for storing the file in cloud and this creates a data redundancy problem. Also, fingerprint indexing is a major issue in data storage in the cloud. So in order to overcome this issue a novel Enhanced Arithmetic Optimization Algorithm with Mixed-Kernel-based Extreme Learning Machine-based Random Forest (EAOA-MKELM-RF) method is proposed that overcome the deduplication issues. In spite, the scalability issues are solved, and the limitations determined in fingerprint indexing. Further, the pipelining and parallelizing hash function values are utilized to address the scalability issue in the deduplication process. The data storage container organizes the programs and files into data blocks, metadata files and nonduplicate blocks. However, the proposed methods determine the similarity issues and accelerate the fingerprint queries for better scalability. The datasets employed to evaluate the proposed approach are CorrAL-100 dataset, Madelon dataset, XOR-100 dataset, Infocom’05 dataset and Linux kernel dataset. The experimentation results demonstrated that the proposed method attained a superior accuracy by 96.8% in terms of restoration accuracy, robustness and delivery ratio. Additionally, the proposed approach reduces buffer time, improving overall system performance. This validation optimized the storage resources, diminished storage costs and improved overall data management.

Customized Virtual File System

Customized Virtual File System

OMD Curation Toolkit: a workflow for in-house curation of public omics datasets

BackgroundMajor advances in sequencing technologies and the sharing of data and metadata in science have resulted in a wealth of publicly available datasets. However, working with and especially curating public omics datasets remains challenging despite these efforts. While a growing number of initiatives aim to re-use previous results, these present limitations that often lead to the need for further in-house curation and processing.ResultsHere, we present the Omics Dataset Curation Toolkit (OMD Curation Toolkit), a python3 package designed to accompany and guide the researcher during the curation process of metadata and fastq files of public omics datasets. This workflow provides a standardized framework with multiple capabilities (collection, control check, treatment and integration) to facilitate the arduous task of curating public sequencing data projects. While centered on the European Nucleotide Archive (ENA), the majority of the provided tools are generic and can be used to curate datasets from different sources.ConclusionsThus, it offers valuable tools for the in-house curation previously needed to re-use public omics data. Due to its workflow structure and capabilities, it can be easily used and benefit investigators in developing novel omics meta-analyses based on sequencing data.

CFIEE: An Open-Source Critical Metadata Extraction Tool for RISC-V Hardware-Based CFI Schemes

Control flow critical metadata play a key role in hardware-based control flow integrity (CFI) mechanisms that effectively monitor and secure program control flow based on pre-extracted metadata. The existing control flow analysis tools exhibit some deficiencies, including inadequate compatibility with the RISC-V architecture, a steep learning curve, limited automation capabilities, and restricted data output formats. CFIEE is an open-source tool with a graphical interface for the automated extraction of control flow critical metadata. The tool possesses the capability to analyze RISC-V binary executables, transforming the binary into an intermediate representation (IR) in the form of the disassembled code, and extracting the critical metadata required for studying hardware-based CFI mechanism through a designed control flow transfer relationship analysis algorithm. The extracted metadata include program basic blocks and their corresponding hash values, control flow graphs, function call relationships, distribution of forward transfer instructions, etc. We selected 15 embedded system programs with processor adaptation for functional verification. The results demonstrate the CFIEE’s capability to automatically analyze programs within the supported RISC-V instruction set and generate comprehensive and precise metadata files. This tool can significantly enhance the efficiency of control flow metadata extraction and furnish configurable metadata for the hardware-based security mechanisms.