Citations for: RAzeZKbUCEMXZXDc-WzgHZ4K5mOMwotYhS2uCKDDmdcHI
Hurlbert & Jetz 2007 hotspot scale-dependence — Iberian-birds × HEALPix-NESTED replication outcome
The replication reproduces Hurlbert & Jetz 2007's qualitative finding that biodiversity-hotspot identity is strongly scale-dependent: at every HEALPix-NESTED resolution tested (Nside 16 to 512, cell side ≈ 407 km down to ≈ 13 km), hotspot non-overlap rises monotonically with grid refinement and dissolves only at the coarsest resolutions, matching the pattern reported for Australia and southern Africa. At the 0.25° reference scale (≈ 25 km, HEALPix Nside 256) this replication finds 89.9 % misidentification on a museum-only basis-of-record subset and 97.8 % on an all-observations subset, versus Hurlbert & Jetz's 47.8 % (Australia) and 68.6 % (southern Africa). A gold-standard test using the EU Birds Directive Article 12 expert rangemap polygons (2013–2018, 260 Iberian species) does not close the gap (85–95 % misidentification on the matched-species subset), attributing the magnitude offset predominantly to observer-effort bias in the modern citizen-science GBIF atlas itself (log-log Pearson r ≈ 0.96 between per-cell record count and per-cell species count for the all-observations strategy at Nside 256), rather than to a refutation of Hurlbert & Jetz's underlying claim — hence Partially Supported.
Hotspot misidentification (symmetric set non-overlap of top-5 % richest cells) was computed at six HEALPix-NESTED resolutions for two GBIF basis-of-record strategies on Iberian birds, year-split 2000 (atlas = post-2000 occurrences; rangemap-equivalent = pre-2000 species convex hulls). Headline numbers at Nside 256 (≈ 25 km, the 0.25° equivalent of Hurlbert & Jetz Table 2): - museum strategy (PRESERVED_SPECIMEN + MACHINE_OBSERVATION): 89.9 % misidentified (Wilcoxon signed-rank p < 10⁻¹⁰⁰). - allbor strategy (+ HUMAN_OBSERVATION, citizen science included): 97.8 % misidentified (Wilcoxon signed-rank p < 10⁻¹⁰⁰). - Hurlbert & Jetz 2007 Australia: 47.8 %. - Hurlbert & Jetz 2007 Southern Africa: 68.6 %. Direction-of-effect agreement is monotone across all six Nsides for both strategies, replicating the central scale-dependence finding. Gold-standard rangemap test at Nside 256 (matched species subset, 260 Iberian breeding species also present in our post-2000 GBIF set): EU Birds Directive Article 12 expert polygons (2013–2018, 10 × 10 km grid cells, EEA CC-BY 4.0) yield misidentification of 94.9 % for museum (n = 213 matched species) and 85.2 % for allbor (n = 226). The expert rangemap does NOT close the gap with Hurlbert & Jetz's 47.8–68.6 % range — refuting the hypothesis that hull-as-rangemap substitution is the dominant cause of the magnitude offset. Mechanism decomposition at Nside 256: - Atlas-vs-observer-effort per-cell correlation (log-log Pearson r at Nside 256): r = 0.48 (museum), r = 0.96 (allbor); the all-observations atlas is essentially a log-linear function of per-cell record count, so for allbor the "atlas hotspots" predominantly index citizen-science observer effort (cities, accessible protected areas) rather than biological richness. This is the dominant residual: under any rangemap substitute including the gold-standard Article 12 expert polygons, the atlas top-5 % set consistently disagrees with the rangemap top-5 % set, because the atlas top-5 % cells are observer hotspots while the rangemap top-5 % cells are biological hotspots. - Top-K sweep: at top-25 % (versus Hurlbert & Jetz's top-5 %), misidentification drops to 74.2 % (museum) and 71.6 % (allbor), inside the Hurlbert & Jetz Australia–southern-Africa range. - Concave-hull substitute (shapely 2.0, ratio = 0.3) closes 6.3 percentage points for museum (89.9 → 83.0) and 13.5 percentage points for allbor (97.8 → 83.8) on the full-species set, confirming that convex hulls over-predict presence — a secondary contributor relative to the observer-effort dominant. Land-mask sensitivity (peninsula-only cells via NaturalEarth 10m) shifts the Nside 256 number by ≤ 4.3 percentage points and is therefore not a material confounder. Per-species temporal hull drift (Jaccard distance between pre-2000 and post-2000 convex hulls) is large for museum (median 0.81) and moderate for allbor (median 0.21); this does not materially shift the aggregate top-5 % misidentification at Nside 256 (Δ < 1 percentage point), because the pre-2000 and post-2000 hulls, though individually differently shaped, are similarly inflated in total area. GitHub repository: https://github.com/annefou/sdm-scale-replication
The magnitude offset relative to Hurlbert & Jetz 2007 is dominated by a single methodological factor that is documented and characterised but cannot be removed inside this replication. The direction of the scale-dependence finding is unaffected. 1. Atlas-axis observer-effort confounding (DOMINANT). Per-cell GBIF record count and per-cell species count correlate at log-log Pearson r ≈ 0.96 for the all-observations (allbor) strategy at the headline scale, and r ≈ 0.48 for the museum-only strategy. The "atlas hotspots" derived from modern Iberian GBIF therefore predominantly index where citizen-science observers go (cities, accessible protected areas, well-birded corridors) rather than where birds are biologically densest. The gold-standard rangemap test using EU Birds Directive Article 12 expert polygons (Test 6, matched subset, 213–226 species) yields 85–95 % misidentification at the headline scale — confirming that the residual is not a rangemap-substitute artefact but is genuinely the observer-effort distortion of the modern atlas. Hurlbert & Jetz's 1990s atlases had observer bias too, but to a much smaller degree than the current citizen-science-dominated GBIF record. 2. Top-K hotspot-threshold choice. Hurlbert & Jetz's top-5 % is a convention, not a biologically grounded threshold. At top-25 %, the misidentification numbers in this replication (74.2 % museum, 71.6 % allbor) sit inside the Hurlbert & Jetz Australia–southern-Africa reference range (47.8–68.6 %). The specific magnitude of the gap is K-sensitive in a way the original paper did not surface. 3. Hull-as-rangemap substitute (SECONDARY). The canonical pipeline uses convex hulls of pre-2000 GBIF occurrences as a rangemap surrogate where Hurlbert & Jetz used expert BirdLife polygons. Convex hulls over-predict presence; concave hulls close 6.3–13.5 percentage points of the magnitude gap at the headline scale on the full-species set. The gold-standard Article 12 test (above) shows this is a real but secondary contributor — the expert rangemap does not close the residual once observer-effort confounding is in play. Two further caveats. At the coarsest resolutions tested (Nside 16 with 8 Iberian cells, Nside 32 with 29) the top-5 % hotspot set rounds to one or two cells, so per-Nside numbers at those resolutions are noise-dominated; the conclusion does not rely on them. Cell geometry differs from Hurlbert & Jetz (HEALPix equal-area NESTED here, lat-lon graticule there); at Iberia's mid-latitude this is unlikely to shift the comparison materially but is not directly tested. Despite these limitations, the direction of Hurlbert & Jetz's finding — that hotspot identification is unstable across resolutions and stabilises only at very coarse cells — is robustly replicated in modern Iberian bird data with HEALPix-NESTED indexing. The observer-effort confound is itself an empirical extension of Hurlbert & Jetz's underlying message that the choice of data source materially changes which cells qualify as hotspots: with modern citizen-science atlases, the observer-effort axis becomes the dominant data-source distortion rather than the range-map polygon choice that Hurlbert & Jetz centred on.
Replication of Hurlbert & Jetz 2007 hotspot scale-dependence in Iberian birds on a HEALPix-NESTED substrate
Hurlbert & Jetz 2007 overlaid expert-drawn bird range maps (Handbook of the Birds of the World; regional atlases) on two structured regional bird-atlas surveys (Australia, southern Africa) on lat-lon grids at 0.25-8 degrees, circa 2007. This replication deviates on four axes: - Range-map layer: convex hulls of GBIF occurrences (a surrogate), cross-checked against EU Article 12 expert distribution polygons — not WWF / Handbook expert range maps. - Atlas layer: modern GBIF occurrence richness (post-2000, citizen-science-dominated for the all-observations strategy) — not a dedicated structured atlas survey with controlled effort. - Substrate: HEALPix-NESTED equal-area cells — not a lat-lon graticule, removing the cell-area distortion intrinsic to lat-lon grids. - Region and era: the Iberian peninsula, 2013-2024 GBIF / 2013-2018 Article 12 — not Australia or southern Africa, circa 2007. The two-basis-of-record design (museum vs all-observations) is an addition to the original: it probes observer-effort sensitivity, a dimension unavailable to Hurlbert & Jetz before citizen-science data dominated occurrence records. The consequences of these deviations for the headline number are quantified in the linked Replication Outcome's Evidence and Limitations fields.
Iberian bird occurrence records (Spain, Portugal, Andorra, Gibraltar) were obtained from GBIF as two basis-of-record strategies, each with a citable download DOI: a "museum" strategy (PRESERVED_SPECIMEN + MACHINE_OBSERVATION; GBIF download DOI 10.15468/dl.r8pcat) and an "all-observations" strategy (additionally HUMAN_OBSERVATION, i.e. citizen-science records; GBIF download DOI 10.15468/dl.e9xv7p). Records were year-split at 2000. Post-2000 occurrences form the atlas-equivalent layer (per-cell richness = number of distinct species observed in the cell). Pre-2000 occurrences form the range-map- equivalent layer (per species, the convex hull of its occurrences, whose cell coverage contributes to per-cell range-map richness). Both layers are binned onto a HEALPix-NESTED ladder at Nside 16, 32, 64, 128, 256 and 512 (cell side approximately 407 km down to 13 km, bracketing Hurlbert & Jetz's 0.25-8 degree range) using the geographic, WGS84-aware healpix-geo library, NESTED ordering throughout. At each resolution, hotspots are the top-5% richest cells (Hurlbert & Jetz's definition); the headline metric is the symmetric non-overlap of the atlas-derived and range-map-derived hotspot sets. A Wilcoxon signed-rank test on the paired per-cell richness provides the dissolution criterion (statistical indistinguishability at P > 0.10, matching the original's >= 4 degree threshold). A verification battery tests robustness: a peninsula-only land mask (NaturalEarth 10m); a top-K hotspot-threshold sweep (1-25%); a per-species pre/post convex-hull drift measure (Jaccard distance); an atlas-richness-versus-observer-effort (per-cell record count) correlation; and two tighter range-map surrogates — a concave hull, and EU Birds Directive Article 12 expert distribution polygons (EEA, 2013-2018, 10 km grid, CC-BY 4.0; 260 Iberian breeding species) used as a gold-standard cross-check on the convex-hull surrogate. The full pipeline is a Snakemake workflow (download -> clean -> analysis -> figures) reproducible from a fresh checkout; the verification battery lives in companion diagnostic notebooks.
Scope: the scale-dependence of bird species-richness hotspot identification. The replication tests whether Hurlbert & Jetz's central finding — that range-map-derived richness hotspots disagree with finer-resolution survey-derived hotspots, with the disagreement growing as the grid is refined and dissolving only at coarse grain — re-emerges for a different region (the Iberian peninsula) under modern occurrence data and an equal-area sphere-aware substrate. In scope: the top-5%-richest-cells hotspot definition; the symmetric non-overlap between range-map-derived and atlas-derived hotspot sets; how that non-overlap varies across a spatial-resolution ladder; and the coarse-grain resolution at which the two richness surfaces become statistically indistinguishable. Out of scope: Hurlbert & Jetz's secondary findings (per-species range occupancy / commission rates, and protected-area coverage of hotspots); and all non-bird taxa — the original claim and this replication are bird-only.
Scale-dependent misidentification of bird richness hotspots from range maps (Hurlbert & Jetz 2007)
AIDA sentence: Aggregating expert-drawn bird range maps onto spatial grids finer than approximately two degrees misidentifies up to two-thirds of species-richness hotspots relative to atlas survey data.
At resolutions less than 2° (≈ 200 km), range maps overestimate the area of occupancy of individual species and mischaracterize spatial patterns of species richness, resulting in up to two-thirds of biodiversity hotspots being misidentiﬁed.
Hurlbert & Jetz showed that the spatial grain used to aggregate expert-drawn bird range maps affects whether richness hotspots are real: at resolutions finer than ~2°, up to two-thirds are misidentified relative to atlas-survey ground truth. We propose testing whether the same scale dependence re-emerges for Iberian birds using modern GBIF occurrence data on an equal-area HEALPix-NESTED grid, and what drives hotspot disagreement.
Paper annotation: At resolutions less than 2° (≈ 200 km), range maps overestimate the area of occupancy of individual species and mischaracterize spatial patterns of species richness, resulting in up to two-thirds of biodiversity hotspots being misidentiﬁed.
Citations for: RAG8PjhjvPQFZo54BTaV_b7TryMonH--aDGzEXLhzvQ4w
Coastal-ROM trade-off reproduction outcome (Loveland et al. 2024)
Validated for hindcasting under the WSE-only-interest conditioning Loveland's §6 Conclusions states and the Quote carries verbatim. The conditional trade-off — reduced-order SWAN source terms (Gen1, Gen2) save computation versus the third-generation ST6 Gen3 package without compromising water-surface-elevation accuracy at NOAA gauges when WSE is of primary interest, with the wave-statistics caveat near hurricane tracks Loveland themselves quantify — reproduces from the public data with one per-storm divergence and one framing distinction worth surfacing. Wall-clock cost: Loveland's §5.1 prose summary ("around 1.5 times longer") and §6 framing ("about a 40 percent increase") cover Hurricane Ike cleanly (Gen3/Gen1 = 1.44×, Gen3/Gen2 = 1.52×) but understate Hurricane Ida by approximately 20 percentage points relative to Loveland's own Table 4 (Gen3/Gen1 = 1.70×, Gen3/Gen2 = 1.76×). The Ida cost overshoot is in the paper's data but not in either prose summary; the replication therefore reproduces the cost direction unequivocally and the magnitude only at the per-storm granularity of Table 4. WSE-RMSE invariance: reproduces in full from Tables 5–6 (maximum with-wave Δ = 0.007 m for Ike, 0.001 m for Ida — within observational noise). Wave-statistics sensitivity caveat: faithfully reflected by Loveland's own Tables 5–7 and Fig. 9 (Tp RMSE Ida Gen3 = 9.96 s versus Gen1 = 4.87 s, a factor-of-2 divergence; spatial Hs differences of several metres near hurricane tracks). Scope of the Validated label, made explicit so that a CiTO-only citer cannot miss the conditioning: the verdict applies (a) to the hindcasting regime Loveland tested, not to operational forecasting where wind-field uncertainty dominates the error budget — a second conditioning Loveland states in §6 that the chosen Quote does not carry; (b) to the consistency of Loveland's published Δ values with the public observational baseline, not to model-level reproducibility of the ADCIRC+SWAN runs themselves, which were not re-executed.
Source files: results/headline_comparison.csv, results/tradeoffs.csv, results/obs_wse_peaks.csv, results/obs_wave_peaks.csv. Paper baselines transcribed verbatim from Loveland Tables 4-7. Figures supporting this verdict: - figures/main_result.png — storm-averaged trade-off summary (compute cost left panel; WSE-RMSE + Hs-RMSE right panel). - figures/fig10_11_obs_equivalent.png — observational side of Loveland Figs 10/11. Per-gauge observed peak WSE for both storms (14 Ike, 13 Ida) overlaid with Loveland's storm-averaged Gen1/Gen2/Gen3 RMSE bands. The three bands are visually indistinguishable on the same axis as the bars; the inter-config Δ (≤ 0.007 m Ike, ≤ 0.001 m Ida) is invisible at the scale of the inter-gauge peak variability (0.47-3.81 m). This is the WSE-invariance finding made visual. - figures/fig5_6_obs_equivalent.png — observational side of Loveland Figs 5/6. Per-buoy observed peak Hs and Tp for both storms with Loveland's storm-averaged Gen1/Gen2/Gen3 RMSE bands. The Ida Tp Gen3 = 9.96 s band is visibly separated from the Gen1/Gen2 bands at ~5 s — the factor-of-2 wave-statistics divergence that Loveland's text and the chain's Quote already carve out as a sensitivity caveat. - figures/fig7_8_buoy_winds_equivalent.png — observational side of Loveland Figs 7/8. NDBC buoy wind-speed time series for the 4 Ike-track buoys (42001, 42002, 42019, 42035 — all retrievable) and the 4 Ida-track buoys (42040, 42039 retrievable; 42001 retired 2021-07-25 before Ida; 42007 not in the 2021 NDBC stdmet archive). ADCIRC wind forcing not reproduced because the model output is not retrievable. Loveland's own Fig 8 panels visibly show ADCIRC's Ida winds diverging from the buoy series near landfall — direct visual evidence for the wind-field-accuracy conditioning Loveland states in §6 and that the Outcome's Conclusion carves out. Run-time (1064 Frontera cores), per storm, with Loveland's prose-summary framings as reference points: - Loveland §5.1: "around 1.5 times longer" (50 percent increase). Loveland §6: "about a 40 percent increase". - Ike: No SWAN 1.094 h; Gen1 2.837 h; Gen2 2.688 h; Gen3 4.086 h. Gen3/Gen1 = 1.44× (+44 percent); Gen3/Gen2 = 1.52× (+52 percent). Sits inside §5.1's "1.5×" framing; close to §6's "40 percent". - Ida: No SWAN 0.647 h; Gen1 1.881 h; Gen2 1.818 h; Gen3 3.206 h. Gen3/Gen1 = 1.70× (+70 percent); Gen3/Gen2 = 1.76× (+76 percent). Exceeds §5.1's "1.5×" framing by approximately 20 percentage points; exceeds §6's "40 percent" framing by approximately 30 percentage points. The Ida overshoot is in Loveland's own Table 4 but is not surfaced in either §5.1 or §6 prose. WSE RMSE at NOAA gauges (m): - Ike: No SWAN 0.210; Gen1 0.190; Gen2 0.196; Gen3 0.197. Max with-wave Δ = 0.007 m. - Ida: No SWAN 0.285; Gen1 0.282; Gen2 0.281; Gen3 0.282. Max with-wave Δ = 0.001 m. Hs RMSE at NDBC buoys (m): - Ike: Gen1 0.900; Gen2 1.033; Gen3 0.802 — Gen3 best. - Ida: Gen1 0.813; Gen2 0.883; Gen3 0.834 — Gen1 best. Tp RMSE at NDBC buoys (s): - Ike: Gen1 2.822; Gen2 2.541; Gen3 2.413. - Ida: Gen1 4.865; Gen2 5.068; Gen3 9.960 — Gen3 a factor of 2 worse than Gen1. Mean wave direction RMSE at NDBC buoys (degrees): - Ike: Gen1 43.238; Gen2 40.392; Gen3 37.076. - Ida: Gen1 62.380; Gen2 60.229; Gen3 67.730. Observational baseline (independently downloaded, NOT a re-derivation of the model-vs-obs RMSE): 14/14 NOAA CO-OPS gauges for Ike (5–14 September 2008); 13/13 for Ida (26 August – 4 September 2021); 9/10 NDBC buoys (42007 absent from NDBC historical archive for 2021). Storm peaks: Galveston Pier 21 = 3.20 m (Ike, 13 September 2008); Grand Isle = 1.65 m (Ida, 29 August 2021). These confirm that the observational dataset Loveland modelled against is publicly retrievable and consistent with NHC historical records. The model-vs-obs RMSE Δ values themselves were transcribed from Loveland's Tables 5–7, not independently re-computed (the model outputs are not retrievable — see Limitations item 1).
Three caveats limit how this Outcome should be cited: 1. Stat-level reproduction, not model re-run. This replication does not independently re-run ADCIRC+SWAN at the paper's compute scale (1064 Frontera cores, 3-6 million-element meshes for Ike and Ida respectively). The model output side of the comparison is transcribed verbatim from Loveland et al.'s Tables 4-7; the observational side is independently downloaded from NOAA CO-OPS and NDBC. The Validated verdict therefore confirms consistency of Loveland's published numbers with the public observational baseline, not the reproducibility of the model run itself. Loveland's DesignSafe deposit (DOI 10.17603/DS2-7HBT-EF65) is access-restricted (HTTP 401 on the public listing API) and was not retrievable during this study. 2. Wind-field-accuracy conditioning, dropped by the Quote. Loveland's §6 Conclusions adds a second conditioning that the chosen Quote does not carry: 'if the meteorological forcing is not sufficiently accurate, which is common in forecasting scenarios, then the additional computational cost associated with the detailed Gen3 source terms may not improve accuracy of the model.' The Validated verdict therefore covers the hindcasting regime (Loveland used Oceanweather Inc. validated hindcasts for Ike and HURDAT2-derived GAHM winds for Ida) and should not be cited as supporting reduced-order source terms in operational forecasting where wind-field uncertainty dominates the error budget. This condition lives in the Replication Study's Methodology / Scope field as well. 3. Wave-statistics sensitivity is context, not independent corroboration. The second sentence of the Quote (large Hs sensitivities near hurricane tracks) is Loveland's own quantified finding — Tables 5-7 and Fig. 9, including the Tp RMSE Ida factor-of-2 divergence noted above. The replication confirms these published numbers but does not surface new wave-statistics evidence. Outcome-level citers should not use this chain as independent corroboration of the wave-statistics caveat; that caveat traces to Loveland's own data. 4. The independently downloaded NOAA observations are a sanity check on Loveland's source data, not an independent re-derivation of the model-vs-obs RMSE. What this replication independently verified is that (a) the NOAA gauge/buoy time series Loveland used are publicly retrievable end-to-end (14/14 Ike + 13/13 Ida WSE gauges, 9/10 NDBC wave buoys) and (b) the storm peaks at those gauges are consistent with NHC historical records. The model-fidelity Δ values (Tables 5–7) were not independently re-computed — they were transcribed from Loveland's published tables. A downstream citer should not infer model-level verification of the model output from this Outcome's evidence trail. 5. Wall-clock-cost framing granularity. Loveland's §5.1 prose summary ("around 1.5 times longer") and §6 framing ("about a 40 percent increase") cover Ike but understate Ida by approximately 20 percentage points relative to Loveland's own Table 4. The verdict on the run-time direction is unequivocally supported; the magnitude is supported at per-storm granularity but not at the prose-summary level. This is visible in Loveland's published data; the replication surfaces but does not author it. The replication's main figure (figures/main_result.png) summarises the trade-off as 'save 30-40 percent wall-clock at less than 5 percent added WSE-RMSE cost', which is faithful to Loveland's framing for Ike but is the lower bound of the actual per-storm range. A revised summary that includes Ida — 'save 30-70 percent wall-clock at less than 5 percent added WSE-RMSE cost' — is more accurate but less catchy; both are honest given the figure shows the per-storm bars.
Stat-level reproduction of the Loveland et al. (2024) coupled ADCIRC+SWAN reduced-order source-term trade-off
1. No ADCIRC+SWAN model re-runs. Loveland's runs were on 1064 Intel Xeon Platinum 8280 cores (19 nodes of TACC Frontera, "Cascade Lake"), with unstructured meshes of 6,675,517 elements / 3,352,598 nodes for Ike and 3,102,441 elements / 1,593,485 nodes for Ida. This compute scale is out of scope for a laptop / GitHub Actions / Docker reproduction. The replication therefore transcribes Loveland's published model-side outputs rather than regenerating them. The trade-off ratios reported in the Outcome's Evidence field are derived from Loveland's Table 4; the WSE-RMSE values are from Tables 5-6; the wave-statistics RMSE values are from Tables 5-7 and the §5.2 prose. 2. DesignSafe deposit not retrievable. Loveland deposited their model inputs (meshes, fort.26 source-term configs, OWI Ike winds, HURDAT2-derived Ida GAHM winds, NOAA gauge / buoy time series, model output files) at DOI 10.17603/DS2-7HBT-EF65 (DesignSafe-CI project PRJ-4678). Both /api/projects/v2/PRJ-4678/ and /api/datafiles/listing/public/designsafe.storage.published/PRJ-4678/ return HTTP 401 to anonymous requests, so the deposit cannot be re-fetched by an unauthenticated reproducer. The fort.26 files for the Ida run are reprinted in the paper's Appendix on pp. 12-13. 3. NDBC wave-buoy 42007 absent from the 2021 historical archive. Loveland's Table 3 lists 10 buoys; only 9 are retrievable for Ida from NDBC's URL pattern. The missing buoy is recorded in data/raw/sources.json. 4. Per-storm wall-clock-cost framing granularity. Loveland's §5.1 prose ("around 1.5 times longer") and §6 prose ("about a 40 percent increase") cover Hurricane Ike (Gen3/Gen1 = 1.44×, Gen3/Gen2 = 1.52×) but understate Hurricane Ida (Gen3/Gen1 = 1.70×, Gen3/Gen2 = 1.76×) by approximately 20 percentage points relative to Loveland's own Table 4. This is not a deviation from method — the replication uses the same per-storm cells from Table 4 — but is surfaced by the Outcome because Loveland's prose summary lacks this per-storm distinction. See Outcome Limitations item 5.
This study is a stat-level (table-and-figure-level) reproduction, deliberately distinguishing what was independently re-derived from what was transcribed from the source paper. The downstream reader should not infer model-level verification. INDEPENDENTLY RE-DERIVED (notebooks/01_data_download.py + 02_data_clean.py): - Observational baseline. NOAA CO-OPS water-level gauge time series for the 14 Ike stations (Table 1 of the paper) over 5-14 September 2008 and the 13 Ida stations (Table 2) over 26 August - 4 September 2021, downloaded fresh from the public CO-OPS API. NDBC wave-buoy data (significant wave height, peak period, mean wave direction) for 10 buoys (Table 3), 9 of which were retrievable; buoy 42007 is absent from NDBC's 2021 historical archive at the URL pattern. Cleaned into tidy per-storm xarray Datasets per-variable, with per-variable resampling onto a common time grid (a naive nearest-reindex collapses NDBC's 10-min wind interleave into NaN-filled wave records; per-variable resampling avoids this). - Storm-peak consistency check. Per-gauge peak water levels were extracted (Galveston Pier 21 = 3.20 m on 13 September 2008; Grand Isle = 1.65 m on 29 August 2021) and verified against NHC historical reports as a sanity check on the observational baseline Loveland modelled against. TRANSCRIBED FROM THE SOURCE PAPER (notebooks/03_analysis.py, data/published_baselines/): - All model-side outputs. Run times (Table 4) for each (storm, config) cell, WSE-RMSE per gauge (Tables 5-6 and the prose summaries in §5.3 of the paper), Hs / Tp / mean-wave-direction RMSE per buoy (Tables 5-7 transcribed from §5.2). These values come from Loveland's Frontera-scale ADCIRC+SWAN runs and were not re-computed. - Source-term configuration files. The fort.26 files printed in the paper's Appendix (pp. 12-13) were inspected for documentation but not used to drive any new SWAN runs. - Spatial figures. The paper's Fig. 9 (spatial Hs differences near hurricane tracks) and Fig. 12-13 (spatial WSE differences) were inspected for qualitative context but not regenerated. What the comparison therefore tests: the internal consistency of Loveland's published model-vs-observation Δ values with the publicly retrievable observational baseline, and the per-storm trade-off ratios visible in Loveland's own Table 4. Not the reproducibility of the model runs themselves. The headline statistic (Gen3 / (Gen1 or Gen2) wall-clock ratio per storm, and maximum WSE-RMSE Δ across source-term configurations per storm) is consolidated in results/headline_comparison.csv and visualised in figures/main_result.png. Orchestrated via Snakemake (pipeline rules in Snakefile); reproducible environment via pixi (pixi.toml + pixi.lock); container build via Dockerfile to ghcr.io/annefou/coastal-rom-replication.
In scope: the conditional trade-off claim from Loveland's §6 Conclusions as carried verbatim by the Quote — that reduced-order SWAN source terms (Gen1 first-generation, Gen2 second-generation) save computation relative to the third-generation ST6 Gen3 package without compromising water-surface-elevation (WSE) accuracy at NOAA gauges when WSE is of primary interest, with Loveland's own quantified caveat that large source-term sensitivities are observed in significant-wave-height fields near hurricane tracks. The two storm scenarios (Hurricane Ike 2008, Hurricane Ida 2021) and the four model configurations (No SWAN, Gen1, Gen2, Gen3) are tested in full. Out of scope: (1) operational forecasting where wind-field uncertainty dominates the error budget — a second conditioning Loveland states in §6 ("if the meteorological forcing is not sufficiently accurate ... the additional computational cost associated with the detailed Gen3 source terms may not improve accuracy of the model") that the chosen Quote does not carry; the Outcome's Validated label therefore applies to the hindcasting regime only. (2) Model-level reproducibility of the ADCIRC+SWAN runs themselves — see Methodology field for what was and was not independently re-derived. (3) Generalisation beyond the Gulf of Mexico or beyond Loveland's two test storms.
Coastal-ROM trade-off in coupled ADCIRC+SWAN hindcasts (Loveland et al. 2024)
AIDA sentence: In coupled ADCIRC+SWAN (storm-surge plus spectral-wave) hindcasts of Gulf of Mexico hurricanes, the third-generation ST6 source-term package in SWAN yields no improvement in water-surface-elevation root-mean-square error at NOAA gauges over the first- or second-generation source-term packages, while increasing wall-clock run time.
For instance, it may be worth the savings in computation if only water surface elevations are of primary interest as opposed to wave statistics. However, large sensitivities to the source term choice in global output of significant wave height near hurricane tracks were observed.
Loveland’s §6 conclusion presents a conditional trade-off: reduced-order Gen1/Gen2 SWAN source terms can substitute for ST6 (Gen3) when modelling water-surface elevation at NOAA gauges, but not where significant wave-height accuracy near hurricane tracks matters. The abstract omits these conditions, risking over-citation as a general endorsement. This chain tests the conditional claim against NOAA observations and published error statistics.
Paper annotation: For instance, it may be worth the savings in computation if only water surface elevations are of primary interest as opposed to wave statistics. However, large sensitivities to the source term choice in global output of significant wave height near hurricane tracks were observed.
How many nanopubs to a journal article? That would be an interesting statistical question over time. A bit of meta-science. Perhaps something to stratify over research areas as nanopubs are for everyone. Anyone interested?
How many nanopubs to a journal article? That would be an interesting statistical question over ti...
Nano Session #32 has role: Planned attendance role
Agenda: Yuanxi Fu: “Nanopublication for Documenting Scholarly Growth” - Many contemporary scholarly big-data infrastructures focus on the public-facing dimensions of scholarship, such as publications, funding records, citations, and institutional affiliations. In contrast, this talk explores how nanopublications might provide an infrastructure for documenting a scholar’s intellectual growth over time. Christian Meesters: “Software Provenance with Snakemake” - Why workflow managers are essential on HPC and how Snakemake automates reproducibility. Live demo: running a workflow, generating an HTML report with publication-ready figures, and exporting software metadata as a nanopublication, plus a mini visual knowledge graph via a single plugin. Jitsi meeting link: https://meet.jit.si/nanosession
A specification defining a graphical language for visualizing, specifying, constructing, and documenting the artifacts of distributed object systems.
OneWater - Eau Bien Commun is a national research programme launched in March 2022 and is designed to build knowledge in the field of fresh waters, to change the current management paradigm and to have water recognized as a common good. The ten-year programme is co-directed by the CNRS, the BRGM and INRAE. It will help speed up the transitions and measure the impacts of global changes on socio-ecosystems in mainland France and in the French overseas territories.
The Water4All Partnership -Water Security for the planet- is a funding programme for scientific research in freshwater. It aims to tackle water challenges to face climate change, help to achieve the United Nations’ Sustainable Development Goals and boost the EU’s competitiveness and growth. It is co-funded by the European Union within the frame of the Horizon Europe programme (a key funding programme for research and innovation). The Partnership duration is for seven years from 2022.
Water4All Partnership - Water Security for the planet
Erik Schultes is participant in Knowledge Pixels Incubator Office Hour 20 May 2026
Knowledge Pixels Incubator Office Hour 20 May 2026
GitHub Inc. is a Git-based software repository that provides version control. It offers all of the distributed version control and source code management (SCM) functionality of Git as well as adding its own features. It provides access control and several collaboration features such as bug tracking, feature requests, task management, and wikis for each project. It provides a number of different access levels, from free through to paid services.
Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance.
Moudrý et al.'s review states explicitly that coarse-resolution predictors are NOT sufficient for species tied to rare environments. Pyrenean Bombus specialists are exactly such a population. The review notes the still-open gap that few studies manipulate predictor AND response resolution simultaneously, which the DestinE Climate DT era now makes operationally tractable.
Paper annotation: Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance.
Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use.
The ODMAP protocol formalises what to report in any SDM study: spatial resolution is an obligatory Overview field, but sensitivity TO that resolution is not. ODMAP requires stating the grain; it does not require testing how results change at other grains. For the Climate-DT era — where multi-resolution analysis is now operationally affordable — that is the natural extension of the reporting standard.
Paper annotation: Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use.
Coarser-grain models, including models fit at 3 km, consistently under-predicted range area relative to 1 km models, suggesting that coarse-grain estimates of distributions could be missing important habitat.
When aggregating distributions to produce continental biodiversity predictions, coarse-grain models overpredicted diversity in the west and underpredicted it in the great plains, prairie pothole region and boreal/taiga zones
Continental confirmation (572 NA bird species, 5 grains 1-50 km, 4,955 SDMs): median 40% range loss when coarsening from 1 km to 50 km; grain-sensitivity highest in heterogeneous landscapes (deserts/scrublands) — same biome category as Iberia. The Iberian Bombus rare-alpine-species rank reshuffle is the single-mechanism instance of this continental-scale pattern. Peer-reviewed companion: doi:10.1111/geb.13934.
Paper annotation: Coarser-grain models, including models fit at 3 km, consistently under-predicted range area relative to 1 km models, suggesting that coarse-grain estimates of distributions could be missing important habitat.
The incorporation of local variables into SDMs at fine grains is key to predict local abundance. SDMs worked out at really fine grains, approaching the average size of territory or home range of target species, are needed to predict local abundance effectively.
This may result from the fact that each single cell may represent a potential territory/home range, and hence a higher suitability over a given area means that more potential territories occur there.
Empirical evidence on 14 bird species in Lombardy that fine-grain SDMs predict local abundance much better than coarse-grain ones: at 1 ha grain (home-range scale), 62% of species had a significant suitability-abundance effect; at 1 km only one did. Grain was the only factor significantly affecting explanatory power. Supports the case that DestinE Climate DT's higher resolution opens new operational possibilities for biodiversity work.
Paper annotation: The incorporation of local variables into SDMs at fine grains is key to predict local abundance. SDMs worked out at really fine grains, approaching the average size of territory or home range of target species, are needed to predict local abundance effectively.
We scored a sample of 400 species distribution modeling studies (see text S3 and table S3.1) using the proposed criteria. For every year between 1995 and 2015, we randomly selected peer-reviewed studies representing a variety of modeling studies with applied biodiversity focus (see text S3). We then examined studies for each of the four aspects of data and models (response variable, predictor variables, model building, and model evaluation).
Unfortunately, 46% of the studies were classified as deficient for one or more of the 15 specific issues.
Audit of 400 species distribution modeling studies (1995-2015) scored against 15 best-practice standards spanning data, predictor variables, model building, and evaluation: 46% are deficient on at least one. "Spatial & temporal resolution" is one of the 15, with a slightly-negative temporal trend within 95% CIs — the standard has not improved over the 20-year window. Defines the baseline DestinE-era operational outputs should meet or exceed.
Paper annotation: We scored a sample of 400 species distribution modeling studies (see text S3 and table S3.1) using the proposed criteria. For every year between 1995 and 2015, we randomly selected peer-reviewed studies representing a variety of modeling studies with applied biodiversity focus (see text S3). We then examined studies for each of the four aspects of data and models (response variable, predictor variables, model building, and model evaluation).
This workflow takes as input a set of binding land-use plan metadata and applies text mining / pattern recognition tools (within Galaxy) to scan for a predefined list of sustainability‐related keywords (e.g. GFZ, GRZ, BauNVO, etc). When a keyword is found, the workflow attempts to capture an associated value (numeric, percentage, descriptor) if present in proximity to the keyword. The resulting structured table allows downstream analysis of how many building plans contain each metric keyword, what the reported values are, and comparisons across plans/projects. This enables automation of otherwise manual review of building plan sustainability metrics, facilitating large‐scale benchmarking, reporting, or auditing.
Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.
An important counterweight to the “finer is always better” assumption: in a Rhododendron ponticum transferability test, the finest model (50 m) performed worse (CBI 0.77) than a medium resolution (300 m, CBI 0.90), while the coarsest (1 km) performed worst (CBI 0.65). For the DestinE Climate-DT era, higher resolution does not automatically mean better projections — spatial grain still requires empirical justification.
Paper annotation: Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.
Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected.
The earliest large-scale empirical test of grain-size sensitivity in Species Distribution Models, across five continents and ten modelling techniques. The headline result (grain matters, but its effect is region- and species-dependent) is the baseline that the Climate-DT era now revisits at much finer resolutions than were operationally available in 2007. Cited here as the historical anchor for the resolution-sensitivity argument.
Paper annotation: Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected.
At resolutions less than 2° (≈200 km), range maps overestimate the area of occupancy of individual species and mischaracterize spatial patterns of species richness, resulting in up to two-thirds of biodiversity hotspots being misidentified.
This foundational study showed that species-richness patterns derived from range maps are strongly scale-dependent: at coarse resolutions (~200 km), up to two-thirds of biodiversity hotspots can be misidentified. It provides the baseline for evaluating high-resolution biodiversity projections, including the DestinE Climate DT–based Iberian Bombus chain. The key question is whether finer Climate-DT resolutions eliminate or merely reduce this scale dependence.
Paper annotation: At resolutions less than 2° (≈200 km), range maps overestimate the area of occupancy of individual species and mischaracterize spatial patterns of species richness, resulting in up to two-thirds of biodiversity hotspots being misidentified.
Files with .csv (Comma Separated Values) extension represent plain text files that contain records of data with comma separated values. Each line in a CSV file is a new record from the set of records contained in the file. Such files are generated when data transfer is intended from one storage system to another. Since all applications can recognize records separated by comma, import of such data files to database is done very conveniently. Almost all spreadsheet applications such as Microsoft Excel or OpenOffice Calc can import CSV without much effort. Data imported from such files is arranged in cells of a spreadsheet for representation to user.
Catalogue of data and metadata artefacts relevant to Personal Exposure and Health (PEH) research
Personal Exposure and Health Data Catalogue (PEH Data Catalogue)
Where are the servers in the nanopublication ecosystem located?
This text will hopefully be replaced by a useful answer soon.
FAQ: Where are the servers in the nanopublication ecosystem located?
0000-0001-6528-9720 is participant in Aus dem SeDOA Innovation Lab (SIL): Nanopublikationen als Werkzeug für kollaborative und transpar...
Aus dem SeDOA Innovation Lab (SIL): Nanopublikationen als Werkzeug für kollaborative und transparente Datenpflege
Daniel Mietchen is participant in Aus dem SeDOA Innovation Lab (SIL): Nanopublikationen als Werkzeug für kollaborative und transpar...
0000-0002-1484-0516 is participant in Aus dem SeDOA Innovation Lab (SIL): Nanopublikationen als Werkzeug für kollaborative und transpar...
0000-0002-2343-7905 is participant in Aus dem SeDOA Innovation Lab (SIL): Nanopublikationen als Werkzeug für kollaborative und transpar...
0000-0001-8188-9697 is participant in BiblioCon2026
0000-0001-7027-8065 is participant in BiblioCon2026
0000-0001-8958-6463 is participant in Aus dem SeDOA Innovation Lab (SIL): Nanopublikationen als Werkzeug für kollaborative und transpar...
Sabine de Günther is participant in BiblioCon2026
Sabine de Günther is participant in Aus dem SeDOA Innovation Lab (SIL): Nanopublikationen als Werkzeug für kollaborative und transpar...
0000-0001-6528-9720 is participant in BiblioCon2026
0000-0002-2343-7905 is participant in BiblioCon2026
Daniel Mietchen is participant in BiblioCon2026
0000-0002-1484-0516 is participant in BiblioCon2026
How can I have my name displayed instead of (or in addition to) my ORCID?
By default, tools that present nanopublication authorship (such as Nanodash) identify authors by their ORCID. To have a human-readable name shown alongside or in place of the ORCID, you need to publish a small nanopublication that declares a foaf:name for your ORCID. The easiest way to do this is through the Nanodash profile page. After logging in with your ORCID, this page lets you enter your display name and publish an introduction nanopublication in a single step. The introduction nanopublication contains a triple of the form orcid:0000-0000-0000-0000 foaf:name "Your Name" in its assertion, and at the same time registers the public key you use to sign nanopublications. Once published, any tool that resolves authorship through the nanopublication network — Nanodash, resource pages, query results, and so on — will display your name in place of (or together with) the bare ORCID. You can update the displayed name at any time by publishing a new introduction nanopublication that supersedes the previous one.
FAQ: How can I have my name displayed instead of (or in addition to) my ORCID?
In which programming languages is nanopublication-related tooling available?
Reference libraries for working with nanopublications are currently available in four programming languages: Java — the original and most complete implementation, including the nanopub command-line tool used for validating, signing, publishing, and retracting nanopublications. Python — a client for fetching, creating, signing, and publishing nanopublications. JavaScript / TypeScript — for use in browser-side and Node.js applications. Rust — a more recent implementation focused on performance and portability. Beyond these libraries, larger components of the ecosystem are written predominantly in Java, including Nanodash (the main web interface), the Nanopub Registry, and Nanopub Query. Since the Registry and Query services expose HTTP REST APIs, nanopublications can also be fetched, queried, and inspected from any programming language with a basic HTTP client — no dedicated library required.
FAQ: In which programming languages is nanopublication-related tooling available?
Southan 2020 - Opening up connectivity between documents, structures and bioactivity
has a logical error: "of the 62000 papers curated by ChEMBL in EPMC only 85000 are full-text and only 600 OA"
Read: Southan 2020 - Opening up connectivity between documents, structures and bioactivity
Hoàng et al. 2026 - The Power of Agency: How Interactivity in Data Storytelling Influences Emotional Engagement and Perceived Trustworthiness in News Context
There are currently two issues with this: (1) the DOI has not been activated, so the article is only available via the DOI-containing publisher URL https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2026.1843009 (2) only the abstract is available, accompanied by a note that states "The final, formatted version of the article will be published soon."
Read: Hoàng et al. 2026 - The Power of Agency: How Interactivity in Data Storytelling Influences Emotio...
0000-0001-9488-1870 displays view: Introduction paragraph view
Colonial Collections provenance events view
Colonial Collections Consortium displays view: Colonial Collections provenance events view
📜 Colonial Collections provenance events
Get Colonial Collections provenance events - Returns all provenance-event claims (CIDOC-CRM E10_Transfer_of_Custody) from Colonial Collections nanopubs typed #ProvenanceEventVersion1, with event type and label, affected object, narrative text and language, attributed community group, individual creator, and date. Sorted from most recent first.
Returns all provenance-event claims (Colonial Collections nanopubs typed #ProvenanceEventVersion1) describing a CIDOC-CRM E10_Transfer_of_Custody. Columns: event type (Getty AAT) and its label, affected object (with short label), narrative text and its language, attributed community group, individual creator, and date. Sorted from most recent first.
Get Colonial Collections provenance events
prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> prefix np: <http://www.nanopub.org/nschema#> prefix npa: <http://purl.org/nanopub/admin/> prefix npx: <http://purl.org/nanopub/x/> prefix xsd: <http://www.w3.org/2001/XMLSchema#> prefix dct: <http://purl.org/dc/terms/> prefix prov: <http://www.w3.org/ns/prov#> prefix crm: <http://www.cidoc-crm.org/cidoc-crm/> select ?event_type ?event_type_label ?source ?source_label ?narrative ?lang ?group ?group_label ?creator ?creator_label ?date ?np ("^" as ?np_label) where { graph npa:graph { ?np npa:hasValidSignatureForPublicKey ?pubkey ; dct:created ?datetime ; np:hasAssertion ?ag ; np:hasProvenance ?pg ; np:hasPublicationInfo ?pig . filter not exists { ?np npx:hasNanopubType npx:ExampleNanopub . } filter not exists { ?inv npx:invalidates ?np ; npa:hasValidSignatureForPublicKey ?pubkey . } } bind(substr(str(?datetime), 1, 10) as ?date) graph ?pig { ?np a <https://n2t.net/ark:/27023/9819f32405815dc7f2e0ecd9d8a9e604#ProvenanceEventVersion1> . } graph ?ag { ?ev a crm:E10_Transfer_of_Custody . optional { ?ev crm:P2_has_type ?event_type . optional { ?event_type rdfs:label ?event_type_label } } optional { ?ev crm:P30_transferred_custody_of ?source . bind(substr(replace(str(?source), "^.*/", ""), 1, 8) as ?source_label) } optional { ?ev crm:P67i_is_referred_to_by ?ling . ?ling crm:P190_has_symbolic_content ?narrative_lit . bind(str(?narrative_lit) as ?narrative) bind(lang(?narrative_lit) as ?lang) } } optional { graph ?pg { ?ag prov:wasAttributedTo ?creator . optional { ?creator rdfs:label ?creator_label } optional { ?creator prov:actedOnBehalfOf ?group . optional { ?group rdfs:label ?group_label } } } } } order by desc(?datetime)
Get Colonial Collections annotations - Returns all Web Annotations contained in Colonial Collections nanopubs, with content subtype, scope, source object, body text and language, attributed community group, individual creator, and date. Sorted from most recent first.
Returns all Web Annotations (oa:Annotation) contained in Colonial Collections nanopubs, together with their specific content subtype, the scope (which field of the target object the annotation refines), the source object (with a short label derived from its identifier), the body text and its language, the community group on whose behalf the annotation was made, the individual creator, and the creation date. Sorted from most recent first.
prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> prefix np: <http://www.nanopub.org/nschema#> prefix npa: <http://purl.org/nanopub/admin/> prefix npx: <http://purl.org/nanopub/x/> prefix xsd: <http://www.w3.org/2001/XMLSchema#> prefix dct: <http://purl.org/dc/terms/> prefix oa: <http://www.w3.org/ns/oa#> prefix prov: <http://www.w3.org/ns/prov#> select ?subtype ?subtype_label ?scope ?scope_label ?source ?source_label ?body ?lang ?group ?group_label ?creator ?creator_label ?date ?np ("^" as ?np_label) where { graph npa:graph { ?np npa:hasValidSignatureForPublicKey ?pubkey ; dct:created ?datetime ; np:hasAssertion ?ag ; np:hasProvenance ?pg ; np:hasPublicationInfo ?pig . filter not exists { ?np npx:hasNanopubType npx:ExampleNanopub . } filter not exists { ?inv npx:invalidates ?np ; npa:hasValidSignatureForPublicKey ?pubkey . } } bind(substr(str(?datetime), 1, 10) as ?date) graph ?pig { ?np a ?subtype . filter(strstarts(str(?subtype), "https://n2t.net/ark:/27023/9819f32405815dc7f2e0ecd9d8a9e604#") && str(?subtype) != "https://n2t.net/ark:/27023/9819f32405815dc7f2e0ecd9d8a9e604#Nanopub") bind(strafter(str(?subtype), "#") as ?subtype_label) } graph ?ag { ?anno a oa:Annotation ; oa:hasBody ?b ; oa:hasTarget ?t . ?b rdf:value ?body_lit . bind(str(?body_lit) as ?body) bind(lang(?body_lit) as ?lang) optional { ?t oa:hasScope ?scope . bind(if(contains(str(?scope), "#"), strafter(str(?scope), "#"), str(?scope)) as ?scope_label) } optional { ?t oa:hasSource ?source . bind(substr(replace(str(?source), "^.*/", ""), 1, 8) as ?source_label) } } optional { graph ?pg { ?ag prov:wasAttributedTo ?creator . optional { ?creator rdfs:label ?creator_label } optional { ?creator prov:actedOnBehalfOf ?group . optional { ?group rdfs:label ?group_label } } } } } order by desc(?datetime)
Colonial Collections Consortium displays view: Colonial Collections annotations view
The Colonial Collections Consortium is a partnership between five organisations in the Netherlands: Museum Bronbeek, NIOD, Cultural Heritage Agency, Rijksmuseum and Wereldmuseum. The consortium supports institutions administering collections in provenance research by sharing knowledge, answering questions and providing stakeholders with a network.
Ashley Caselli is participant in Knowledge Pixels Incubator Office Hour 20 May 2026
Virginia Balseiro is participant in Knowledge Pixels Incubator Office Hour 20 May 2026
Get agenda items for event - Returns all agenda items belonging to the given event, with label, description, related spaces, links, creator, and nanopub link.
Returns all agenda items belonging to the given event, with label, description, related spaces, links, creator, and nanopub link.
prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> prefix dct: <http://purl.org/dc/terms/> prefix np: <http://www.nanopub.org/nschema#> prefix npa: <http://purl.org/nanopub/admin/> prefix npx: <http://purl.org/nanopub/x/> prefix schema: <http://schema.org/> prefix gen: <https://w3id.org/kpxl/gen/terms/> select ?item ?item_label ?description (group_concat(distinct str(?space); separator=" ") as ?spaces_multi_iri) (group_concat(distinct concat('link'); separator=", ") as ?link) ?creator ?np ("^" as ?np_label) where { graph npa:graph { ?np npa:hasValidSignatureForPublicKeyHash ?pubkey . filter not exists { ?npx npx:invalidates ?np ; npa:hasValidSignatureForPublicKeyHash ?pubkey . } filter not exists { ?np npx:hasNanopubType npx:ExampleNanopub . } ?np dct:created ?npdate . ?np dct:creator ?creator . ?np np:hasAssertion ?a . ?np npx:introduces ?item . } values ?_event_multi_iri {} graph ?a { ?item a gen:AgendaItem . ?item dct:isPartOf ?_event_multi_iri . optional { ?item rdfs:label ?item_label . } optional { ?item dct:description ?description . } optional { ?item schema:about ?space . } optional { ?item rdfs:seeAlso ?link . } } } group by ?item ?item_label ?description ?creator ?np ?npdate order by asc(?npdate)
Returns all status updates belonging to the given event, with label, description, concerned spaces, links, creator, and nanopub link.
prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> prefix dct: <http://purl.org/dc/terms/> prefix np: <http://www.nanopub.org/nschema#> prefix npa: <http://purl.org/nanopub/admin/> prefix npx: <http://purl.org/nanopub/x/> prefix schema: <http://schema.org/> prefix gen: <https://w3id.org/kpxl/gen/terms/> select ?item ?item_label ?description (group_concat(distinct str(?space); separator=" ") as ?spaces_multi_iri) (group_concat(distinct concat('link'); separator=", ") as ?link) ?creator ?np ("^" as ?np_label) where { graph npa:graph { ?np npa:hasValidSignatureForPublicKeyHash ?pubkey . filter not exists { ?npx npx:invalidates ?np ; npa:hasValidSignatureForPublicKeyHash ?pubkey . } filter not exists { ?np npx:hasNanopubType npx:ExampleNanopub . } ?np dct:created ?npdate . ?np dct:creator ?creator . ?np np:hasAssertion ?a . ?np npx:introduces ?item . } values ?_event_multi_iri {} graph ?a { ?item a gen:StatusUpdate . ?item dct:isPartOf ?_event_multi_iri . ?item schema:about ?space . optional { ?item rdfs:label ?item_label . } optional { ?item dct:description ?description . } optional { ?item rdfs:seeAlso ?link . } } } group by ?item ?item_label ?description ?creator ?np ?npdate order by asc(?npdate)
Publish a status update belonging to an event. Must be linked to one or more spaces (e.g. projects) that the update concerns, and optionally to one or more URLs with further info.
description or notes for the status update
link with further info on the status update
space (e.g. project) the update concerns
Publish an agenda item belonging to an event. Optionally link it to one or more spaces (e.g. projects) that the item concerns, and to one or more URLs with further info.
description or notes for the agenda item
link with further info on the agenda item
Get status updates for event - Returns all status updates belonging to the given event, with label, description, concerned spaces, links, creator, and nanopub link.
MAC vocab update: https://docs.google.com/document/d/1jlFObE9xYfcHfzAOcdJ27Mvk2QBJuxIEIUjcZKmVBaE/edit?usp=sharing
Agenda item: Claude Code assisted FAIR Digital Twins
MAC | Metabolomics and Analytics Center - The Metabolomics and Analytics Centre (MAC) at the Leiden Academic Centre for...
current working draft at https://docs.google.com/document/d/19_GiEFMx4Mk0RBxt9MB7hyDc7ixAnlYQlFek5sEuWn4/edit?tab=t.0#heading=h.afcqcumppzzc
Agenda item: Drafted "Makin MAC FAIR" paper
Review unaddressed Nanosuggestions and mark the addressed ones as such.
Knowledge Pixels Incubator Office Hour 20 May 2026 - This is the office hour of the Knowledge Pixels incubators on 20 May 2026.
Knowledge Pixels Incubator Office Hour 20 May 2026 has observer: Daniel Mietchen
Plan to attend: Knowledge Pixels Incubator Office Hour 20 May 2026