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EUROBIS data served via ERDDAP
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Dataset Title: | Nematodes from the Exe Estuary (microcosm experiments)
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Institution: | MBA, PML (Dataset ID: manuela_pe) |
Information: | Summary ![]() ![]() ![]() |
To view the map, check View : Map of All Related Data above.
WARNING: This may involve lots of data.
For some datasets, this may be slow.
Consider using this only when you need it and
have selected a small subset of the data.
To view the counts of distinct combinations of the variables listed above,
check View : Distinct Data Counts above and select a value for one of the variables above.
Distinct Data
(Metadata)
(Refine the data subset and/or download the data)
ScientificName | BasisOfRecord | YearCollected | aphia_id |
---|---|---|---|
Not applicable | None | None | Not applicable |
Adoncholaimus fuscus | O | 1992.0 | |
Aegialoalaimus | O | 1992.0 | 2380.0 |
Alaimella | O | 1992.0 | 2400.0 |
Anticoma | O | 1992.0 | 2500.0 |
Ascolaimus | O | 1992.0 | 2416.0 |
Axonolaimus | O | 1992.0 | 2416.0 |
Bathylaimus | O | 1992.0 | 2586.0 |
Camacolaimus | O | 1992.0 | 2392.0 |
Campylaimus | O | 1992.0 | 2436.0 |
Chromadora | O | 1992.0 | 2276.0 |
Chromadoridae | O | 1992.0 | 2162.0 |
Chromadorita | O | 1992.0 | 2294.0 |
Chromaspirina | O | 1992.0 | 2344.0 |
Daptonema | O | 1992.0 | 2456.0 |
Daptonema normandicum | O | 1992.0 | |
Daptonema psammoides | O | 1992.0 | |
Daptonema setosum | O | 1992.0 | |
Desmodora schulzi | O | 1992.0 | |
Desmoscolex | O | 1992.0 | 2368.0 |
Eleutherolaimus | O | 1992.0 | 2480.0 |
Enoploides longispiculosus | O | 1992.0 | |
Enoplolaimus propinquus | O | 1992.0 | |
Epsilonema | O | 1992.0 | 2360.0 |
Eumorpholaimus | O | 1992.0 | 2480.0 |
Gammanema | O | 1992.0 | 2332.0 |
Geomonhystera | O | 1992.0 | |
Halalaimus longicaudatus | O | 1992.0 | |
Hypodontolaimus | O | 1992.0 | 2298.0 |
Innocuonema | O | 1992.0 | 2300.0 |
Leptolaimus | O | 1992.0 | 2408.0 |
Leptonemella | O | 1992.0 | 2352.0 |
Mesacanthion | O | 1992.0 | 2516.0 |
Metachromadora | O | 1992.0 | 2346.0 |
Metadesmolaimus | O | 1992.0 | 2460.0 |
Metoncholaimus | O | 1992.0 | 2572.0 |
Microlaimus | O | 1992.0 | 2366.0 |
Molgolaimus | O | 1992.0 | 2344.0 |
Monoposthia | O | 1992.0 | 2368.0 |
Morlaixia | O | 1992.0 | 2440.0 |
Neochromadora tecta | O | 1992.0 | |
Neochromadora trichophora | O | 1992.0 | |
Odontophora | O | 1992.0 | 2418.0 |
Oncholaimellus | O | 1992.0 | 2568.0 |
Oncholaimus | O | 1992.0 | 2572.0 |
Oxystomina | O | 1992.0 | 2552.0 |
Paracanthonchus | O | 1992.0 | 2320.0 |
Paramonhystera | O | 1992.0 | |
Pomponema | O | 1992.0 | 2318.0 |
Praeacanthonchus | O | 1992.0 | 2312.0 |
Ptycholaimellus | O | 1992.0 | 2300.0 |
Rhabditis | O | 1992.0 | 2596.0 |
Rhynchonema | O | 1992.0 | 2464.0 |
Richtersia | O | 1992.0 | 2334.0 |
Sabatieria | O | 1992.0 | 2432.0 |
Sigmophoranema | O | 1992.0 | 2348.0 |
Siphonolaimus | O | 1992.0 | 2496.0 |
Spirinia | O | 1992.0 | 2350.0 |
Stephanolaimus | O | 1992.0 | 2408.0 |
Syringolaimus | O | 1992.0 | 2526.0 |
Terschellingia communis | O | 1992.0 | |
Thalassoalaimus tardus | O | 1992.0 | |
Thalassomonhystera | O | 1992.0 | 2448.0 |
Theristus | O | 1992.0 | 2468.0 |
Trefusia | O | 1992.0 | 2592.0 |
Trichotheristus | O | 1992.0 | 2470.0 |
Trissonchulus benepapillosus | O | 1992.0 | |
Viscosia cobbi | O | 1992.0 | |
Viscosia glabra | O | 1992.0 | |
Viscosia viscosa | O | 1992.0 | |
Xyala | O | 1992.0 | 2472.0 |
Xyalidae | O | 1992.0 | 2190.0 |
In total, there are 71 rows of distinct combinations of the variables listed above.
All of the rows are shown above.
To change the maximum number of rows displayed, change View : Distinct Data above.
To view the related data counts,
check View : Related Data Counts above and select a value for one of the variables above.
WARNING: This may involve lots of data.
For some datasets, this may be slow.
Consider using this only when you need it and
have selected a small subset of the data.
Related Data
(Metadata)
(Refine the data subset and/or download the data)
To view the related data, change View : Related Data above.
WARNING: This may involve lots of data. For some datasets, this may be slow. Consider using this only when you need it and have selected a small subset of the data.