A new long term gridded daily precipitation dataset at high-resolution for Cuba (CubaPrec1).

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37383804", "kind": "science-record", "revision": 2, "content_hash": "sha256:87abe96294105b122a95c84c4a7dd14880d7dd3a79c7024768e9304b5d3383a9", "created_at": "2026-11-01T19:02:08Z", "updated_at": "2026-11-01T21:31:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A new long term gridded daily precipitation dataset at high-resolution for Cuba (CubaPrec1).", "doi": "10.1016/j.dib.2023.109294", "pmid": "37383804", "authors": ["Abel Centella-Artola", "Arnoldo Bezanilla-Morlot", "Roberto Serrano-Notivoli", "Ranses Vázquez-Montenegro", "Maibys Sierra-Lorenzo", "Dayron Chang-Dominguez"], "journal": "Data in brief", "year": 2023, "abstract": "The paper presents a high-resolution (-3km) gridded dataset for daily precipitation across Cuba for 1961-2008, called CubaPrec1. The dataset was built using the information from the data series of 630 stations from the network operated by the National Institute of Water Resources. The original station data series were quality controlled using a spatial coherence process of the data, and the missing values were estimated on each day and location independently. Using the filled data series, a grid of 3 × 3 km spatial resolution was constructed by estimating daily precipitation and their corresponding uncertainties at each grid box. This new product represents a precise spatiotemporal distribution of precipitation in Cuba and provides a useful baseline for future studies in hydrology, climatology, and meteorology. The data collection described is available on zenodo: https://doi.org/10.5281/zenodo.7847844.", "claims": [{"id": "claim-method", "statement": "The gridded dataset presented here was built from these original 630 stations by creating reference values (RVs) using Generalized Linear Models (GLM) based on the rainfall values of the 10 nearest observations and its altitude, longitude, and latitude as covariates.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37383804", "locator": "sections[8] \"Overview\" paragraph 4", "quote": "The gridded dataset presented here was built from these original 630 stations by creating reference values (RVs) using Generalized Linear Models (GLM) based on the rainfall values of the 10 nearest observations and its altitude, longitude, and latitude as covariates."}], "state": "complete"}, {"id": "claim-data-access", "statement": "CubaPrec1: A 48 years long term gridded daily precipitation dataset at very high-resolution for Cuba. (Original data) (zenodo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37383804", "locator": "sections[16] \"Data Availability\" paragraph 2", "quote": "CubaPrec1: A 48 years long term gridded daily precipitation dataset at very high-resolution for Cuba. (Original data) (zenodo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper presents a high-resolution (-3km) gridded dataset for daily precipitation across Cuba for 1961-2008, called CubaPrec1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37383804", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The paper presents a high-resolution (-3km) gridded dataset for daily precipitation across Cuba for 1961-2008, called CubaPrec1."}, {"asset": "source:pmid:37383804", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The dataset was built using the information from the data series of 630 stations from the network operated by the National Institute of Water Resources."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The paper presents a high-resolution (-3km) gridded dataset for daily precipitation across Cuba for 1961-2008, called CubaPrec1.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37383804"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37383804", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.dib.2023.109294", "sha256": "sha256:0845ce2e754267d01906428de709470841fcdef11b293d1797d207b3b99deae9", "bytes": 18699, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.dib.2023.109294", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10294104", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Generalized Linear Models (GLM)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The gridded dataset presented here was built from these original 630 stations by creating reference values (RVs) using Generalized Linear Models (GLM) based on the rainfall values of the 10 nearest observations and its altitude, longitude, and latitude as covariates."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (Generalized Linear Models (GLM)), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["CubaPrec1: A 48 years long term gridded daily precipitation dataset at very high-resolution for Cuba. (Original data) (zenodo)."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:37383804"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:45:21Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 6117, "completion_tokens": 1569, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37383804", "type": "paper", "label": "pmid:37383804"}, {"id": "source:pmid:37383804", "type": "manuscript", "label": "PMID 37383804 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37383804", "type": "paper", "label": "pmid:37383804"}, {"id": "source:pmid:37383804", "type": "manuscript", "label": "PMID 37383804 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37383804", "type": "extraction", "used": ["source:pmid:37383804"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:37383804", "parent_hash": "sha256:b032f0d45a24849e3f058b9015f7bd3c6380243a5a293b26624e150b15bcad80", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"]}