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The working hypothesis in this study was that sequence variation in the BMP15 gene may play a role in reproductive variability in the Indigenous, Mozaffarnagri, and Dorper sheep. To address this research question, blood was collected from the studied animals, DNA was isolated, and the targeted BMP15 regions were amplified using PCR. Next, representative PCR products were sequenced using Sanger sequencing, and their nucleotide sequences were compared with the reference BMP15 sequence and within the animals to detect nucleotide polymorphisms. High sequence similarity with the reference BMP15 sequence was shown by the sequencing data, and strong similarity was found in the analyzed region in the sequenced animals. In particular, the exon-1 region showed highly conserved sequence in the examined animals. Nevertheless, it is important to take into consideration the sample size when interpreting the sequencing results. Since only few animals were sequenced in the experiment (n = 15 per breed), the lack of polymorphism should not be regarded as evidence that all the animals within the breeds have monomorphic genes. On the contrary, this means that there were no strong variations in the sequences found within the analyzed BMP15 region among the selected animals. More extensive sequencing would have been required involving more animals and BMP15 regions in order to identify genetic variations within the breed. This information is important in showing the conservation of the region studied and will serve as the foundation of future research.

This dataset contains electroencephalography (EEG) recordings collected for five-class imagined alphabet decoding using an OpenBCI system. EEG data were recorded from 20 healthy adult participants during an eyes-closed alphabet-imagery task. The five target alphabet classes are A, K, N, P and Z. Each participant completed one recording session for each alphabet class and each session contained 25 repeated trials. The dataset includes raw OpenBCI recordings, externally generated event-log files, labelled training-segment files, metadata tables, preprocessed MATLAB epoch files, channel-quality audit files, generated figures and baseline deep-learning result files. The raw recordings were acquired with a 16-channel OpenBCI Cyton Serial Daisy configuration at 125 Hz. The raw channel montage includes Fp1, Fp2, C3, C4, P7, P8, O1, O2, F7, F8, F3, F4, T7, T8, P3 and P4. During preprocessing, channel-quality assessment identified T8 as problematic; therefore, the final exported preprocessed epoch set contains 15 retained EEG channels. The preprocessed release contains 100 alphabet sessions, 2,500 task trials, and 390,000 exported EEG windows. Each 10-second task segment was divided into 156 non-overlapping windows of 8 samples. At the 125 Hz sampling rate, each 8-sample window corresponds to 64 ms. The exported EEG tensor is stored as windows × time points × channels with a final shape of 390,000 × 8 × 15. The dataset is accompanied by metadata files describing participant-level anonymized information, channel configuration and task timing. It also includes code and outputs for baseline deep-learning experiments using leave-one-subject-out and calibration-based evaluation settings. The baseline workflow evaluates CNN-LSTM and LSTM-based architectures for imagined alphabet classification. Ethical clearance for the data collection reported in this dataset was obtained from the Committee for Ethical Clearance of Research Proposal, Faculty of Biological Sciences, University of Dhaka, Bangladesh, under the project titled "Imagined Alphabet Recognition from EEG Signals Using Neuro-Symbolic AI" (Approval Ref: 394/Biol.Scs./2026-2027 , dated 27 August,2026). This Mendeley Data release corresponds to the EEG acquisition, preprocessing and baseline deep-learning benchmarking component of that approved project; the participants, recording protocol and consent procedures are identical to those covered by the approval. This dataset is intended for research on EEG-based brain-computer interfaces, imagined speech or imagined character decoding, subject-independent EEG classification, calibration-based learning, low-cost OpenBCI acquisition, EEG preprocessing reproducibility and benchmark development for deep-learning methods.

This dataset contains experimental measurements and test records obtained from geophones for system identification, damping estimation, and parameter calibration. The data were collected during laboratory testing procedures designed to evaluate electromechanical damping characteristics and to adjust the external shunt resistance for achieving an optimal damping factor of 0.707 ($1/\sqrt{2}$).Dataset ContentsTime-Series Response Files: Raw voltage and signal output logs captured during impulse or step response tests under varying shunt resistance values.Parameter Logs: Tabular files containing recorded shunt resistance values, calculated damping coefficients, natural frequency metrics, and system identification variables.Test Metadata: Documentation detailing the experimental setup, data acquisition hardware specifications, sampling frequencies, and environmental conditions.Methodology & ApplicationThe data supports advanced signal processing and parameter estimation workflows such as second-order ARX model identification and least-squares optimization enabling researchers and engineers to accurately tune sensor damping, analyze transient responses, and validate mathematical models of electromagnetic geophones.

Anexos I e II do Artigo Gestão Colaborativa do patrimônio da União: construção e delimitação conceitual.

This work presents the development of a pedagogical resource aimed at zoological identification. The model was constructed from the digitization (three-dimensional scanning) of a real physical matrix and produced through additive manufacturing, ensuring morphological fidelity. The resource includes accessibility elements, such as Braille symbols and a raised-relief legend with visual contrast, aiming at its application in inclusive educational contexts. The material was developed with the potential for large-scale reproduction through digital files, favoring its dissemination and use in different educational contexts.

This work presents the development of a tactile teaching material for zoological identification, produced through three-dimensional scanning and additive manufacturing. The model, focused on the footprint morphology of the raccoon (Procyon cancrivorus), was conceived from the digitization of a physical plaster matrix, ensuring high geometric fidelity to the process. The resource includes accessibility elements, such as Braille symbology and a raised-relief legend with visual contrast, promoting its application in inclusive educational contexts. The material was developed with the potential for large-scale reproduction through digital files, favoring its dissemination and use in different educational contexts.

This dataset is the exact repository snapshot behind the technical report "TieOutBench: Measuring whether an LLM's finance work ties out - before deciding whether to trust it" (SSRN abstract 7243025, August 2026). It is the state of the public repository at git commit b965426 (2026-08-12), the version the report's numbers were produced from.

This work presents the development of a tactile teaching resource for zoological identification, produced through three-dimensional scanning and additive manufacturing. The model, based on the capybara footprint, was conceived from the digitization of a real physical matrix, allowing for geometric fidelity. The resource integrates accessibility elements, such as Braille symbology and visual contrast, aiming at its application in inclusive educational contexts. The material was developed with the potential for large-scale reproduction through digital files, favoring its dissemination and use in different educational contexts.

Origin Data

This work presents the development of a tactile teaching resource for zoological identification, produced through three-dimensional scanning and additive manufacturing. The resource, representing the footprint of the maned wolf, the largest canid in South America, was developed from the digitization of a real physical matrix and 3D printed, ensuring fidelity to the species' characteristics, such as the elongated arrangement of the toes and the presence of non-retractable claws. The resource includes accessibility elements, such as Braille symbols and a raised-relief legend with visual contrast, aiming for its application in inclusive educational contexts. The material was developed with the potential for large-scale reproduction through digital files, favoring its dissemination and use in different educational contexts.