{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp04_retrieval_first_paired20_20260802","cell_id":"negative_space_design__behavioral_economics","hypothesis_id":"H5","search_queries":["missing data zero dashboard regulatory monitoring decision study","dashboard distinguish no data from zero official guidance","patent dashboard missing data cause specific action","missingness visualization decision making no data indicator study","official monitoring dashboard no data error state action alert","Grafana no data error state configure behavior official","regulatory dashboard missing report follow up workflow official","data quality dashboard missing report alerts action official"],"sources":[{"source_id":"C1","title":"Handle missing data in Grafana Alerting","publisher":"Grafana Labs","url":"https://grafana.com/docs/grafana/latest/alerting/guides/missing-data/","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["Grafana operationally distinguishes No Data, MissingSeries, and other alert states instead of treating absent observations as normal measurements.","Administrators can assign state-specific behavior such as creating a dedicated alert, entering Alerting or Normal, retaining the last state, routing notifications, and displaying a state-reason annotation."]},{"source_id":"C2","title":"Quick Reference Guide to the NHSN PSC Annual Survey Data Quality Dashboard","publisher":"U.S. Centers for Disease Control and Prevention","url":"https://www.cdc.gov/nhsn/pdfs/pscmanual/PSC-Survey-Data-Quality-Dashboard-QRG.pdf","source_class":"GOVERNMENT_OR_REGULATOR","claims_supported":["An operational surveillance dashboard labels distinct conditions including Missing Survey, Incomplete Survey, Facility not operational, Survey Required, and Comparisons cannot be calculated.","The dashboard connects conditions to actions such as review, edit, submit, or confirm, retains prior-year context, and records a resolved state."]},{"source_id":"C3","title":"Data Quality Tools","publisher":"U.S. Centers for Disease Control and Prevention, National Syndromic Surveillance Program","url":"https://www.cdc.gov/nssp/php/onboarding-toolkits/data-quality.html","source_class":"GOVERNMENT_OR_REGULATOR","claims_supported":["CDC provides dashboards for monitoring data flow, timeliness, completeness, validity, exceptions, and processing backlogs.","The system supports custom anomaly rules, alerts, investigation of processing problems, and 30-, 60-, and 90-day alerts when active facilities stop sending data."]},{"source_id":"C4","title":"Data Quality Assurance: Module 2—Desk Review of Data Quality","publisher":"World Health Organization","url":"https://cdn.who.int/media/docs/default-source/data-quality-pages/2021_-dqa_module-2_desk-review-of-data-quality.pdf","source_class":"OFFICIAL_GUIDANCE","claims_supported":["WHO expressly requires missing values to be differentiated from true zero values in district and facility reports.","WHO explains that coding missing entries as zero makes no-event periods indistinguishable from failures to report and requires managerial interpretation of apparent zeros."]},{"source_id":"C5","title":"Understanding the Effects of Visualizing Missing Values on Visual Data Exploration","publisher":"arXiv; authors Hayeong Song, Yu Fu, Bahador Saket, and John Stasko","url":"https://arxiv.org/abs/2109.08723","source_class":"PRIMARY_RESEARCH","claims_supported":["A controlled study compared omission of records containing missing values with an explicit visualization using estimated points and error bars.","The presentation of missing values changed participants' decision-making workflow, supporting the premise that missingness displays can affect behavior, though not the proposed oversight outcome."]},{"source_id":"C6","title":"Toward Systematic Considerations of Missingness in Visual Analytics","publisher":"arXiv; authors Maoyuan Sun et al.","url":"https://arxiv.org/abs/2108.04931","source_class":"PRIMARY_RESEARCH","claims_supported":["The paper identifies system failure, network interruption, intentional hiding, and bias as distinct sources of missingness affecting visual-analytic decisions.","It distinguishes observed, inferred, and ignored missingness and argues that visualization should move missingness into users' awareness."]}],"proximity":"SUBSTANTIAL_COLLISION","closest_analogues":[{"name":"CDC NHSN Survey Data Quality Dashboard","similarity":"This deployed surveillance dashboard diagnoses several reasons that expected observations or comparisons are absent, preserves current and prior reporting context, and pairs the displayed condition with review, submission, correction, or confirmation actions.","remaining_difference":"It is primarily a facility-facing data-quality workflow; the source does not report a controlled comparison of matched actions versus a generic missing-data warning for inspectors deciding whether to close or escalate an entity-period.","source_ids":["C2"]},{"name":"Grafana No Data and MissingSeries handling","similarity":"This neighboring-domain monitoring system treats absence as an explicit state, distinguishes causes or state reasons, preserves prior state/history, and permits different alerts, notifications, and operational responses.","remaining_difference":"It monitors technical infrastructure rather than regulated entities and does not test false no-incident classifications or strategic under-reporting.","source_ids":["C1"]},{"name":"CDC NSSP data-flow and facility-silence monitoring","similarity":"This public-health surveillance practice monitors completeness, timeliness, processing failures, backlogs, and facilities that stop reporting, then alerts users and supports investigation.","remaining_difference":"The public documentation does not show the exact proposed entity-period empty-state interface or a behavioral comparison against an equally salient generic warning.","source_ids":["C3"]},{"name":"WHO missing-versus-zero data-quality rule","similarity":"The guidance directly identifies the hypothesis's core failure mode: missing reports encoded as zero make unreported events indistinguishable from genuine no-event periods.","remaining_difference":"It establishes the representational requirement but does not prescribe a cause-specific dashboard action for each absence state or report causal outcome evidence.","source_ids":["C4"]},{"name":"Missingness-aware visualization research","similarity":"The research establishes taxonomies of missingness causes and shows that rendering missingness can alter attention and decision workflow.","remaining_difference":"It does not evaluate cause labels plus matched oversight actions, case closure, requests for data, or behavior under incentives to under-report.","source_ids":["C5","C6"]}],"overlapping_components":["Explicit separation of missing observations from true zero or no-event observations","Classification of absent data by cause or operational state","Cause or state labels displayed in monitoring interfaces","State-specific alerts, notifications, or next actions","Preservation of prior values, reporting periods, state history, or audit context","Escalation or investigation when an expected source stops reporting","Making missingness behaviorally salient without populating the dashboard with invented observations"],"remaining_contrastive_claim":"In regulator-like entity-period decisions, pairing each diagnosed absence cause with one matched oversight action reduces false no-incident closure and increases appropriate follow-up more than an equally salient generic missing-data warning does.","claim_falsifier":"The contrastive claim would be falsified by a well-powered controlled study finding no material advantage for cause-matched actions over an equally salient generic missingness warning, or by pre-existing documentation of a regulator-facing system that already implements and validates that exact comparison on closure, data-request, or investigation outcomes.","problem_support":"STRONG","recommendation":"RESEARCH","world_novelty_boundary":"This bounded eight-query, six-source web search does not establish world novelty and is not a patentability conclusion; unindexed patents, procurement records, internal regulator systems, non-English literature, and unpublished evaluations could contain an equal or closer match."}