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This dataset includes raw z-stack files from the Nikon microscope as well as deleted-slices versions of some of them, and Imaris files for which the original microscope z-stacks are not available. These correspond to the images and movies in the manuscript "Longitudinal single-axon-resolution imaging of peripheral nerve injury response in mice using an optical window implant". The experimental conditions and other information about the images is in the manuscript and the file metadata.

This dataset contains quantitative summary data supporting the publication "Suppressing phagocyte activation by overexpressing the phosphatidylserine lipase ABHD12 preserves sarmopathic nerves". The data include LC-MS/MS metabolomics measurements, in vitro axon degeneration assays, and in vivo histological and behavioral analyses used to generate the quantitative results presented in Figures 1–4 and Supplemental Figure S1. These data support the study's investigation of chronic SARM1 activation, phosphatidylserine dysregulation, macrophage activation, and the therapeutic effects of neuronal ABHD12 overexpression in a mouse model of sarmopathy.

This dataset supports a landowner-typology study of the buffer zone of Cerro Castillo National Park, a mountain protected-area gateway in the Aysén region of Chilean Patagonia undergoing tourism-driven amenity transition. It contains de-identified responses from a survey of 641 landowners conducted Feb–July 2024 within 10 km of the park (637 retained for segmentation; 636 georeferenced to watershed sub-catchments), with the derived variables, segment assignments and validation results needed to reproduce the analysis. Four owner segments — Committed newcomers, Rooted landholders, Active citizens, and Park-disengaged owners — are obtained by k-means on eleven standardised variables: two territorial-bond indices (bond with own land, bond with the park), a disposition-to-change index, four conservation-citizenship sub-indices (civic identity, civic behaviour, norm compliance, participatory action), age, education, tenure length and acquisition mode. Because the bond items were administered under two survey versions (A/B), the two bond indices are harmonised across versions by mean-sigma random-groups test-equating before clustering, removing the instrument artifact; the equated inputs (idx_tierra_h, idx_parque_h) are stored alongside the raw indices. The workbook has seven sheets: (1) de-identified case-level data (641 × 191) — segment labels, all clustering inputs, the equated bond indices, an assignment-method flag, and profiling, spatial and institutional variables; (2) a codebook with variable descriptions, types and value labels (Spanish glossed in English); (3) Derived_variables — the construction of every derived index, verified to reproduce the stored values; (4) Validation — internal indices (silhouette, Davies–Bouldin) for k = 2–8 and nonparametric bootstrap stability; (5) a segment-by-sticker crosstab from a qualitative sub-sample; (6) a sub-catchment table of person- versus land-area dominance; and (7) a README documentation sheet. No direct or indirect identifiers are included; geography is retained only as coarse watershed sub-catchment (1–19). Data were collected under FONDECYT Regular project 1230020 (ANID, Chile); ethics approval IRI11_23, Universidad Austral de Chile; participants gave informed consent. This is the segmentation/typology dataset, distinct from the companion deposit (Gale & Báez-Montenegro, 2026; Mendeley Data, doi:10.17632/cf69gj7ndg.1), which holds the relational-values and place-attachment survey underlying a separate structural-equation analysis. The two share the 2024 field survey but differ in scope and structure: the present file provides the segment assignments, the equated (harmonised) clustering inputs, an education variable absent from the companion deposit, spatial and institutional variables, validation material, and the item batteries needed to reconstruct and verify every derived index; the companion deposit organises the relational-values and place-attachment items for structural-equation modelling.

Hearing the Unheard" is a survey dataset capturing the general public's awareness, attitudes, willingness to adopt, and concerns toward computer-vision-based Sign Language Recognition (SLR) technology used to communicate with deaf and hard-of-hearing (DHH) people. It contains 107 anonymous responses collected through an online Google Forms questionnaire between February and April 2024 from individual members of the public. The dataset supports the peer-reviewed paper "Hearing the Unheard" (ACM Digital Library, DOI: 10.1145/3723178.3723277) and is shared to enable replication and new research on technology acceptance, accessibility, and inclusive design. Each of the 17 questions covers one of several themes: demographics (gender, age group, occupation), prior exposure to deaf individuals or sign language, familiarity with SLR technology, perceived benefits, likelihood of using a translation app, willingness to learn sign language, support for integrating SLR into public services (e.g., hospitals, police stations), useful application contexts, worries (cost, accuracy, privacy, over-reliance), the importance of privacy and consent during development, adoption factors, perceived drawbacks, and open-ended feedback. Items include single-choice Likert scales, categorical questions, multiple-choice ("select all that apply") questions, and optional free text. The dataset is provided in three progressively processed versions so it serves both social-science analysis and machine learning / deep learning model training: 1. Raw - a faithful, value-for-value copy of the original export, with a stable respondent_id and machine-friendly column codes; nothing recoded or dropped. 2. Cleaned - a tidy, human-readable table with trimmed whitespace, parsed ISO 8601 timestamps (collected in GMT+6), verified for duplicates, with no responses removed or imputed. 3. ML/DL-ready - a fully numeric feature matrix (107 rows x 41 columns, zero missing values). Ordinal/Likert answers are encoded as rank-preserving integers, nominal variables are one-hot encoded, multi-select questions are expanded into multi-hot indicator columns, and binary flags summarize open text. This version loads directly into scikit-learn, XGBoost, PyTorch, or TensorFlow/Keras. "data_dictionary.csv" file have all the explanation. A complete data dictionary documents every variable and its encoding, and a single reproducible Python script regenerates all files deterministically from the original export. No personally identifiable information was collected; participation was voluntary and anonymous. Suggested uses include modelling adoption likelihood or public-service integration support, segmenting attitude profiles, and analyzing how concerns relate to willingness to adopt. Keywords: sign language recognition, public perception, technology acceptance, deaf and hard-of-hearing, accessibility, computer vision, survey dataset, human-computer interaction.

Raw data from experiments described in Zhou, Saffarian-Deemyad, Shi, & Weiss et al.

This dataset contains imageability and familiarity ratings for Ukrainian and English work-related proverbs collected from Ukrainian university students. The data were gathered as part of a cross-linguistic study examining how bodily grounding influences the mental imagery associated with proverbial expressions in a first language (L1) and a second language (L2). The participants (N = 49) were students at Vasyl’ Stus Donetsk National University. Ukrainian was their first language (L1), and English was their second language (L2). Participants evaluated Ukrainian and English work-related proverbs using 7-point Likert scales measuring imageability and familiarity. The stimulus set consisted of two proverb categories: body-based (BOD) proverbs containing explicit references to bodily actions, body parts, or sensorimotor experiences, and abstract (ABS) proverbs expressing work-related meanings without direct bodily imagery. Ratings were collected separately for Ukrainian and English proverb sets. The dataset includes raw participant responses, worksheet-level calculations, category means, language-specific means, and derived variables used for hypothesis testing. Statistical calculations included comparisons between BOD and ABS proverb categories as well as between L1 and L2 proverb processing. All participant data are fully anonymized. No personally identifiable information is included. The dataset may be useful for research on embodied cognition, conceptual metaphor theory, psycholinguistics, figurative language processing, proverb comprehension, imageability, familiarity, and cross-linguistic studies of language representation. File contents • Raw imageability ratings for Ukrainian proverbs • Raw imageability ratings for English proverbs • Raw familiarity ratings for Ukrainian proverbs • Raw familiarity ratings for English proverbs • Calculated category means (BOD and ABS) • Derived variables for hypothesis testing (H1–H3) • Statistical summary tables Variables Participant_ID – anonymous participant identifier Proverb_Rating – participant rating assigned to a proverb Imageability – perceived ease of forming a mental image (1–7) Familiarity – perceived familiarity with the proverb (1–7) Language – Ukrainian (L1) or English (L2) Category – Body-Based (BOD) or Abstract (ABS) Mean_Score – average score calculated for a participant, proverb category, or language condition License CC BY 4.0

Source code, input data, and results for the hybrid algorithm designed for electric power distribution system reconfiguration and distributed generation allocation. To run the program, save all .py files in a single folder. Also, save the test system data files in this same folder. The following Python packages must be installed on your machine: Path numpy pandas math time pandapower networkx heapq numba random The algorithm begins with the "Programa Principal" "Main Program"; to start testing, simply specify which test system you wish to run (33, 69, 136, or 415). Note: You may need to modify the file path the algorithm uses to save data on your machine.

The complete hemagglutinin (HA) gene sequence obtained in this study was compared with representative H5N1 avian influenza virus (AIV) sequences from Brazil and Colombia available in the GenBank database, including the earliest reported H5N1 sequence from South America. Multiple sequence alignment, phylogenetic reconstruction, and molecular clock analyses consistently placed the virus within clade 2.3.4.4b. To facilitate data sharing and support future evolutionary, epidemiological, and comparative investigations, the assembled HA sequence, together with its associated metadata, including host species, sampling locality, and collection date, was submitted to a publicly accessible nucleotide sequence database.

Individuals are expected to balance the costs and benefits of their escape decisions based on aspects of their biological condition and life history, for example, size, body mass or satiation level. Flight initiation distance (FID) is the distance at which animals escape when approached by humans, under standardized conditions. It is used as a proxy of animals´ perception of predation risk. Escape behavior is a proxy to measure anti-predator strategies and to monitor tolerance and human impacts on birds. This study analyzes the perception of predation risk in bird species within a tropical dry forest with continuous human presence. Our experiment evaluates the influence of body mass, activity, and residence status on the perceived predation risk based on FID. We predicted longer FID in larger than in smaller bird species (based on body mass), in perching birds relative to foraging individuals, and in migratory relative to resident species. The values of FID in 19 species (82% residents) during 76 trials ranged from 2.46 to 32.29 m. There was a quadratic relationship with size, in which birds with intermediate size had longer FID than lighter or heavier species. Perching individuals had longer FID than foraging individuals. FIDs were similar between resident and migratory birds. Behavioral mechanisms and life history of particular species may explain differences in escape strategies.

Source data including uncropped gel images and microcopy data