Our Work

Version 4 adds a README file containing detailed guidance on: • downloading and reconstructing the File Geodatabase;• opening the data in ArcGIS Pro, QGIS, and Python;• interpreting the…

Version 4 adds a README file containing detailed guidance on: • downloading and reconstructing the File Geodatabase;• opening the data in ArcGIS Pro, QGIS, and Python;• interpreting the fields, units, and total_weight values;• appropriate data uses and important limitations;• licensing, attribution, and citation; and• troubleshooting common access and memory issues. No changes were made to the underlying geodatabase data relative to version 3; version 4 adds documentation only. Users should consult the README before analyzing or reusing the dataset. Dataset creator: Jinchao SongEmail: jinchaos@umich.eduORCID: https://orcid.org/0000-0002-3448-124X Concept DOI (all versions): https://doi.org/10.5281/zenodo.15809010Licence: Creative Commons Attribution 4.0 International (CC BY 4.0)

What's Changed Modernize the ontologies browse page (by @matthewhorridge in https://github.com/ncbo/bioportal_web_ui/pull/528) Redesigns /ontologies browsing: each ontology is a framed card with its …

What's Changed Modernize the ontologies browse page (by @matthewhorridge in https://github.com/ncbo/bioportal_web_ui/pull/528) Redesigns /ontologies browsing: each ontology is a framed card with its acronym as a pill next to the name, inline stat pills (classes, instances/concepts, properties, projects, notes) linking to the matching section, and status pills for retired, deprecated, and private ontologies; the search box moves into a toolbar at the top of the results column, and the toolbar and facet sidebar stay in place while only the results scroll. Fixes the long-standing typing lag in browse search: the page now renders only matching results in a window that grows on scroll, instead of rendering all ~1,500 ontologies and hiding non-matches — measured search cost per keystroke dropped from ~110–270 ms to ~30 ms. Improves search: the matched term is highlighted in names, acronyms, and descriptions; an exact acronym match (e.g. "GO") ranks first; submission keywords are included in the search index; descriptions show a match-centered snippet with an inline More/Less toggle; active facets and the search term appear as removable chips with a "Clear all" reset. Adds facets for license ("Has a license") and lifecycle status ("Show retired"), plus admin-only visibility filters (hide private / hide public); license pills link to the declared license with a short human-readable label (e.g. "CC BY 4.0"). Behavior changes to note: retired ontologies are now hidden by default (tick "Show retired" to include them — the visible count is lower than before), the chosen sort order is remembered per browser, upload dates are formatted per the browser locale, and the search box supports keyboard shortcuts (focused on load, "/" focuses it from anywhere, Esc clears it). Includes post-review fixes by @jvendetti: group filter chips show group names instead of ids, stat pill labels singularize when the count is 1, the results list scrolls back to top when filters or sort change, and the unused ngAnimate dependency was removed. Clear the unselected ontology location source on save (by @jvendetti in https://github.com/ncbo/bioportal_web_ui/pull/535) Addresses one direction of #435: switching an ontology's location from "Load from URL" to "Upload local file" now clears the stale pull URL, so the nightly pull no longer overwrites manual uploads; a pull URL can also now be removed without supplying a replacement file. The reverse direction (local file to "Load from URL") requires a server-side fix and is tracked separately. Full Changelog: https://github.com/ncbo/bioportal_web_ui/compare/v8.14.1...v8.15.0

This executive summary examines the proposed transition from generative artificial intelligence toward Artificial Machine Understanding (AMU): context-aware systems combining metacognitive state repre…

This executive summary examines the proposed transition from generative artificial intelligence toward Artificial Machine Understanding (AMU): context-aware systems combining metacognitive state representation, multimodal inference, agentic feedback, and embodied intelligence. The report presents a five-dimensional metacognitive framework organized around certainty, consistency, stability, relevance, and ethics. It connects emerging approaches—including liquid neural networks, multimodal fusion, world foundation models, and auto-judging agents—to enterprise transformation and human-machine collaboration. It also analyzes the 2026 socio-economic horizon, including workforce burnout, skill erosion, bot-mediated hiring and management, the gigification of credentialed work, and the risk that automation may decouple human effort from perceived value. The report advances human hyper-capability as the preferred strategic direction: using intelligent systems to extend expert judgment while preserving human agency, dignity, and adaptive expertise. This publication is a conceptual executive synthesis and forward-looking outlook. It has not undergone formal peer review. Quantitative projections and emerging-technology claims should be interpreted as scenario-level analysis rather than independently validated empirical findings.

ReferenceFrameRotations v3.5.0 Diff since v3.4.0 Add angleaxis_to_crp and angleaxis_to_mrp, completing the conversion matrix. compose_rotation now supports heterogeneous tuples and vectors of rotatio…

ReferenceFrameRotations v3.5.0 Diff since v3.4.0 Add angleaxis_to_crp and angleaxis_to_mrp, completing the conversion matrix. compose_rotation now supports heterogeneous tuples and vectors of rotations, which previously threw a MethodError. Such collections are folded with ∘, so the output has the type of the last rotation. Add hash and isequal for Quaternion, CRP, and MRP, so that they behave correctly as Set elements and Dict keys. The compact show of CRP and MRP now prints the sign of each component. Previously, it printed the absolute values, so two different rotations produced identical output. Quaternion(u::UniformScaling, q::Quaternion) now uses u.λ as the real part, as documented, instead of always returning the identity quaternion. dcm_to_angleaxis returned a reflected axis for 180° rotations whose axis had a vanishing first component, and lost up to eight significant digits for angles near π. It is now computed through the quaternion representation, which is well conditioned everywhere. quat_to_angleaxis and *(::EulerAngleAxis, ::EulerAngleAxis) now recover the rotation angle with atan instead of acos, removing a relative error of up to 1e-2 for small angles and the hard truncation to zero below ~1e-7 rad. q / λ is now computed componentwise instead of as q * (1 / λ), which rounded twice. inv(::EulerAngleAxis) now computes 2π at the output precision, preserving BigFloat accuracy. smallangle_to_dcm now has a return type that does not depend on the runtime value of the normalize keyword. The one- and two-rotation forms of angle_to_dcm now accept Irrational angles, matching the three-rotation form. Quaternion constructors, products, kinematics, ddcm, and the Euler angle-axis conversions now respect the indexing conventions of the input vector, supporting offset arrays. *(q, v), *(v, q), and v \ q now validate that the vector has three components, and v \ q treats v as the vectorial part, as documented. The Zygote rule for the DCM constructor no longer errors when the output cotangent is a zero tangent. Composing two MRPs that yield a 360° rotation now throws an ArgumentError, matching the CRP behavior, instead of silently returning NaN. Add a precompilation workload, reducing the measured first-call latency of the common operations from about 1070 ms to about 10 ms. rand(R, dims) now returns a concrete-eltype array for the unparameterized rotation types instead of a boxed, abstract-eltype array. Hoist the repeated divisions in crp_to_dcm, mrp_to_dcm, and dcm_to_quat. Convert EulerAngleAxis to CRP/MRP through the quaternion instead of the DCM. The Zygote extension is no longer triggered by ForwardDiff, which it never used. Fix incorrect docstrings, rotted examples, wrong comments, and typos throughout the package, and convert the remaining julia-repl blocks to jldoctest so that CI verifies them.

This record contains the R code, replication-level simulation results, scenario-level performance metrics, workflow-decomposition results, conceptual-domain mapping, figures, and software-environment …

This record contains the R code, replication-level simulation results, scenario-level performance metrics, workflow-decomposition results, conceptual-domain mapping, figures, and software-environment information supporting the manuscript “Beyond Point-and-Click: Interface-Mediated Analytic Constraints in Federated Electronic Health Record Research—A Monte Carlo Simulation Study.” The Monte Carlo study included six scenarios defined by three levels of additional hidden confounding and two propensity-score overlap conditions. Each scenario comprised 1,000 replications of 20,000 simulated patients under a homogeneous null treatment effect with a true hazard ratio of 1.00. Four analytic workflows were evaluated: propensity-score matching using a limited covariate set (Method A), overlap weighting using the same limited covariates (Method A-OW), overlap weighting using expanded EHR-accessible covariates (Method B), and oracle overlap weighting that additionally included an otherwise unavailable socioeconomic variable (Method C). No patient-level or third-party data were used. The archive includes replication-level results for all four analytic workflows, aggregate performance results, descriptive workflow contrasts, final figures, software-version information, SHA-256 checksums, and a verified post-processing script that reproduces the aggregate results from the replication-level output.

These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Partial Solar Eclipse.  It was decided to include only raw, unprocessed audio …

These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Partial Solar Eclipse.  It was decided to include only raw, unprocessed audio data files in each site-specific Zenodo record. This decision was so that any researcher can independently verify, reproduce, and extend the analysis performed. As a result, some sites have WAV files with 0 bytes of data or timestamps outside the range of probable recording times. Procedures used by the Eclipse Soundscapes team to process audio data for its purposes are outlined in the Data Management reports located in the Eclipse Soundscapes Zenodo community. Data with 0 bytes of data were included for completeness. When possible, all site-specific files, including the audio files, are included in a zip file for ease of download. If a zip file upload was not possible due to upload or bandwidth limitations, all available audio files are included individually. Data Site location information: Latitude: 40.61  Longitude:  -89.37 Local Eclipse Type: Partial Solar Eclipse Eclipse Percent (%): 94.78 WAV files Time & Date Settings: Set with Automated AudioMoth Time Chime (More information on TimeStamp Setting below) Data Collector Start Time Notes: N/A Included Data: Audio files in WAV format with the date and time in UTC within the file name: YYYYMMDD_HHMMSS meaning YearMonthDay_HourMinuteSecondFor example, 20240411_141600.WAV means that this audio file starts on April 11, 2024 at 14:16:00 Coordinated Universal Time (UTC) CONFIG Text file: Includes AudioMoth device setting information, such as sample rate in Hertz (Hz), gain, firmware, etc.  README.md:  Markdown formatted file with information about the recording and recording site. file_list.csv:  A machine and human file that gives the following information on each file in the record: File Name, File Type, Description, File Size in kilobytes, Name of Associated Data Dictionary with the file, calculated SHA-512 Hash of the file as a unique identifier to insure data integrity during transfer and compression. total_eclipse_data.csv:  A machine and human readable file that gives the following information about the site where the audio data recording was taken: ESID#, Latitude, Longitude, Eclipse_type, CoveragePercent, Eclipse Start UTC (1st contact), Totality Start UTC (2nd contact), Totality End UTC (3rd Contact), Eclipse End UTC (4th Contact), Max Eclipse Time UTC License.txt:  A human readable file that explains the terms and conditions under which the data can be used. AudioMoth_Operation_Manual.pdf:  A human readable document that explains the use of an AudioMoth device. The document is current up to the time of the AudioMoth's use in the Eclipse Soundscapes project. file_list_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the file_list.csv file. CONFIG_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the CONFIG.TXT file. eclipse_data_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the total_eclipse_data.csv file. WAV_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the *.WAV files. ES_Data_Management_Pre-Eclipse_Data_Infrastructure_Stage_0.pdf:  PDF document that describes Stage 0 (Pre-Eclipse Infrastructure and Data Stewardship Planning) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Receipt_Sorting_and_Metadata_Organization_Stage_1.pdf:  PDF document that describes Stage 1 (Receipt, Sorting, and Metadata Organization) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Data_Processing_Stage_2.pdf:  PDF document that describes Stage 2 (Data Processing) of the Eclipse Soundscapes (ES) data Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio datalifecycle. ES_Data_Management_Data_Sharing_Stage_3.pdf:  PDF document that describes Stage 3 (Public Data Sharing) of the Eclipse Soundscapes (ES) data lifecycle. Eclipse Information for this location: Eclipse Date: April 08, 2024 Eclipse Start Time (UTC) (1st Contact): 17:47:07 Totality Start Time (UTC) (2nd Contact): [N/A if partial eclipse] 0:00:00 Eclipse Maximum Time [when the most possible amount of the Sun in blocked] (UTC): 19:03:58 Totality End Time (UTC) (3rd Contact): [N/A if partial eclipse] 0:00:00 Eclipse End Time (UTC) (4th Contact): [N/A if partial eclipse] 20:19:17 Audio Data Collection During Eclipse Week ES Data Collectors used AudioMoth devices to record audio data, known as soundscapes, over a 5-day period during the eclipse week: 2 days before the eclipse, the day of the eclipse, and 2 days after. The complete raw audio data collected by the Data Collector at the location mentioned above is provided here. This data may or may not cover the entire requested timeframe due to factors such as availability, technical issues, or other unforeseen circumstances.  ES ID# Information: Each AudioMoth recording device was assigned a unique Eclipse Soundscapes Identification Number (ES ID#). This identifier connects the audio data, submitted via a MicroSD card, with the latitude and longitude information provided by the data collector through an online form. The ES team used the ES ID# to link the audio data with its corresponding location information and then uploaded this raw audio data and location details to Zenodo. This process ensures the anonymity of the ES Data Collectors while allowing them to easily search for and access their audio data on Zenodo.  TimeStamp Information: The ES team and the Data Collectors took care to set the date and time on the AudioMoth recording devices using an AudioMoth time chime before deployment, ensuring that the recordings would have an automatic timestamp. However, participants also manually noted the date and start time as a backup in case the time chime setup failed. The notes above indicate whether the WAV audio files for this site were timestamped manually or with the automated AudioMoth time chime.   Common Timestamp Error: Some AudioMoth devices experienced a malfunction where the timestamp on audio files reverted to a date in 1970 or before, even after initially recording correctly. Despite this issue, the affected data was still included in this ES site's collected raw audio dataset. Latitude & Longitude Information: The latitude and longitude for each site was taken manually by data collectors and submitted to the ES team, either via a web form or on paper. It is shared in Decimal Degrees format. General Project Information: The Eclipse Soundscapes Project is a NASA Volunteer Science project funded by NASA Science Activation that is studying how eclipses affect life on Earth during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. Eclipse Soundscapes revisits an eclipse study from almost 100 years ago that showed that animals and insects are affected by solar eclipses! Like this study from 100 years ago, ES asked for the public's help. ES uses modern technology to continue to study how solar eclipses affect life on Earth!  Eclipse Soundscapes is an enterprise of ARISA Lab, LLC and is supported by NASA award No. 80NSSC21M0008. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration.   Eclipse map/figure/table/predictions courtesy of Fred Espenak, NASA/Goddard Space Flight Center, from eclipse.gsfc.nasa.gov.   Eclipse Data Version Definitions {1st digit = year, 2nd digit = Eclipse type (1=Total Solar Eclipse, 9=Annular Solar Eclipse, 0=Partial Solar Eclipse), 3rd digit is unused and in place for future use} 2023.9.0 = Week of October 14, 2023 Annular Eclipse Audio Data, Path of Annularity (Annular Eclipse) 2023.0.0 = Week of October 14, 2023 Annular Eclipse Audio Data, OFF the Path of Annularity (Partial Eclipse) 2024.1.0 = Week of April 8, 2024 Total Solar Eclipse Audio Data, Path of Totality (Total Solar Eclipse) 2024.0.0 =  Week of April 8, 2024 Total Solar Eclipse Audio Data , OFF the Path of Totality (Partial Solar Eclipse) *Please note that this dataset's version number is listed below. Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024) (Archival Copy) This site-level record includes the Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024). The manual documents the participant training, device setup procedures, metadata submission requirements, ES ID system, timestamp protocols, data return workflow, and public archiving processes used during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. The manual is preserved for transparency and reproducibility and reflects the procedures under which this dataset was collected and processed. (DOI 10.5281/zenodo.18623442) Data Receipt, Processing, and Analysis Methods All programs supporting Stages 1–3 are openly available in the: Eclipse Soundscapes GitHub repository: https://github.com/ARISA-Lab-LLC/ESCSP Data Management Lifecycle The following section documents the relationship of this record to the full Eclipse Soundscapes (ES) data lifecycle, a multi-stage workflow designed to support large-scale participatory science, long-term data stewardship, open science, and scientific reuse. Each stage addressed a different operational need, beginning before eclipse deployment and continuing through validation, public archiving, and scientific analysis. Together, these stages transformed distributed volunteer-submitted audio recordings into structured, documented, publicly accessible NASA-funded research assets. Stage 0: Pre-Eclipse Infrastructure and Deployment Preparation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Pre-Eclipse Infrastructure and Deployment Preparation (Stage 0). Zenodo. https://doi.org/10.5281/zenodo.20413370 Stage 0 focused on building the operational foundation required to support geographically distributed eclipse data collection at national scale. This stage included AudioMoth device preparation, accessibility modifications, ES ID # assignment systems, metadata collection workflows, participant training materials, deployment logistics, and planning for downstream data stewardship and archival workflows. The 2023 annular eclipse served as both a scientific investigation and a large-scale operational beta test that informed improvements for the 2024 total solar eclipse campaign. Related Citations and Resources: Severino, M., & Kline, T. (2025, November 24). Eclipse Soundscapes Apprentice Role Curriculum: Solar Eclipses and Multi-Sensory Observing (Informal Education). Zenodo. https://doi.org/10.5281/zenodo.17703003 Severino, M., & Bauer, D. J. (2026). Eclipse Soundscapes Observer Role Training and Resources Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18633602</li> Severino, M., Winter, H., & Bauer, D. J. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Stage 1: Receipt, Sorting, and Metadata Organization Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Receipt, Sorting, and Metadata Organization (Stage 1). Zenodo. https://doi.org/10.5281/zenodo.19471425 Stage 1 transformed returned participant materials into organized, traceable site-level records. This included receiving mailed microSD cards, consolidating participant-submitted metadata, reconciling handwritten and online records, organizing physical audio media by ES ID #, and deriving eclipse timing and coverage information using NASA eclipse prediction datasets. The outputs of Stage 1 established the structured metadata relationships required for downstream validation, processing, archiving, and analysis workflows. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). EPTT (Eclipse Phase Timing Tool) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-Eclipse-Phase-Timing-Tool/li> Espenak, F. (n.d.). Eclipse predictions by Fred Espenak, NASA's GSFC Eclipse Web Site. NASA Goddard Space Flight Center. http://eclipse.gsfc.nasa.gov/eclipse.html Stage 2: Data Processing and Validation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Data Processing (Stage 2). Zenodo. https://doi.org/10.5281/zenodo.18683402 Stage 2 focused on centralized audio ingestion, validation, timestamp verification, metadata reconciliation, and preparation of datasets for analysis and public sharing. During this stage, returned audio recordings were processed using custom open-source tools developed by the ES team, including ES WAVES and ES AMES. The project implemented scalable infrastructure capable of processing large volumes of participant-submitted microSD cards while preserving all raw audio data without modification. Stage 2 established the validated dataset structure required for long-term preservation and scientific analysis. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). ES WAVES (Eclipse Soundscapes WAV Audio Validation & Extraction Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-WAV-Audio-Validation-Extraction-Suite Winter, H., & Goncalves, J. (2026). ES AMES (Eclipse Soundscapes AudioMoth Metadata Extractor Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-AMES-AudioMoth-Metadata-Extractor-Suite Stage 3: Public Data Sharing and Open Archiving Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Public Audio Data Sharing (Stage 3). Zenodo.https://doi.org/10.5281/zenodo.18683437 Stage 3 transformed validated site-level datasets into publicly archived, DOI-assigned research records published through the Eclipse Soundscapes Zenodo Community. This stage included dataset packaging, metadata standardization, README generation, integrity verification, DOI assignment, and automated repository upload workflows using the Automated Zenodo Upload Software (AZUS). These workflows established the project's long-term open-science infrastructure and ensured that datasets remained findable, accessible, interoperable, reusable, and citable for future scientific and educational use. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). AZUS (Automated Zenodo Upload Software) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/AZUS-Automated-Zenodo-Upload-Software Stage 4: Scientific Analysis and Research Use Stage 4 involves the scientific analysis and interpretation of validated eclipse soundscape datasets. Analysis workflows utilized datasets verified during earlier stages to investigate eclipse-related environmental and animal vocalization changes across hundreds of recording sites. This stage also includes broader scientific interpretation, publication development, and continued reuse of Eclipse Soundscapes datasets and infrastructure for future research, education, and open-science applications. Related Citations and Resources: Pease, B., Gilbert, N., & Severino, M. (2026). Eclipse Soundscapes Preliminary Findings – How Eclipses Affect Nature as determined by Sound (Recorded Webinar). Zenodo.https://doi.org/10.5281/zenodo.18613979 Gilbert, N. A., Pease, B. S., Severino, M., & Winter, H. III. (2026). Photic niche explains avian behavioral responses to solar eclipses. Ecology and Evolution, 16(2), e73090. https://doi.org/10.1002/ece3.73090 Analysis code repository: https://github.com/BrentPease1/eclipse-traits Companion Zenodo record archiving structured analysis scripts and derived outputs: https://doi.org/10.5281/zenodo.15790879[r] Public Archiving, Privacy, and Data Transparency The Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023-2024) includes a detailed explanation of how Eclipse Soundscapes audio data are publicly archived on Zenodo, how participant privacy is protected through the ES ID system, and how transparency and traceability are maintained. It also outlines the criteria for determining which recordings are included in the public archive, as well as the distinction between publicly shared archival data and datasets used for ES-led scientific analyses. Participants and data users can consult this section for full documentation of the project's open science and privacy practices. Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Citations Individual Site Citation: APA Citation (7th edition) Winter, H., Severino, M., & Volunteer Scientist. (2026). 2024 solar eclipse soundscapes audio data [Audio dataset, ES ID# 797]. Zenodo.{Insert DOI} Collected by volunteer scientists as part of the Eclipse Soundscapes Project. This project is supported by NASA award No. 80NSSC21M0008. Eclipse Community Citation Winter, H., Severino, M., & Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio data [Collection of audio datasets]. Eclipse Soundscapes Community, Zenodo. https://zenodo.org/communities/eclipsesoundscapes/ Collected by volunteer scientists as part of the Eclipse Soundscapes Project This project is supported by NASA award No. 80NSSC21M0008.  

This dataset supports the development and on-farm implementation of the Compostómetro, a web-based decision-support tool designed to formulate compost mixtures using locally available organic m…

This dataset supports the development and on-farm implementation of the Compostómetro, a web-based decision-support tool designed to formulate compost mixtures using locally available organic materials in tropical plantain and banana production systems. The study was conducted in 36 farms located in six municipalities of the department of Quindío, Colombia. The deposited files include: (i) an anonymized inventory of organic materials available on the participating farms; (ii) a technical and bibliographic database containing carbon, nitrogen, and carbon-to-nitrogen ratio values for 40 organic materials; (iii) the composition and calculated indicators of 36 compost formulations; (iv) independent computational verification of dry-mass, carbon, nitrogen, and C/N calculations; and (v) the R scripts used for data quality control, descriptive analyses, table generation, and visualization. Farm identifiers have been anonymized, and the dataset does not include farmers’ names, contact information, personal identifiers, or exact geographic coordinates. The deposited data describe the initial formulation and physical implementation of the compost piles; they do not constitute an assessment of compost stability, maturity, or final agronomic quality. The source code and technical documentation of the Compostómetro platform are available at: https://github.com/dbtrago/Compost-read.

Manual  for DIGITAL INTELLIGENCE SCALE