Datos de registros biológicos

Introducción

Resources which present evidence of the occurrence of a species at a particular place and normally on a specified date. These datasets expand on most Checklist Data because they contribute to mapping the historical or current distribution of a species. At the most basic, such datasets may provide only general locality information (even limited to a country identifier). Ideally they also include coordinates and a coordinate precision to support fine scale mapping. In many cases, these datasets may separately record multiple individuals of the same species. Examples of such datasets include databases of specimens in natural history collections, citizen science observations, data from species atlas projects, etc. If sufficient information exists in the source dataset (or applies consistently to all occurrences in the dataset), it is recommended that these datasets are presented as Sampling Event Data. These datasets include the same basic descriptive information included under Resource metadata.

How to transform your data into occurrence data

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Finalmente, sus datos deben ser transformados a una estructura de tabla utilizando nombres de términos Darwin Core (DwC) como nombres de las columnas.

Intente poner sus datos en el modelo Excel, que incluye todos los campos DwC requridos y los campos DwC recomendados.

De manera alternativa, si sus datos están en una base de datos compatible, puede elaborar una tabla SQL (ver) utilizando nombres de columnas DwC. Sea cuidadoso para incluir todos los required DwC fields y añada tantos recommended DwC fields como sea posible.

For extra guidance, you can look at the exemplar datasets.

You can augment your table with extra DwC columns, but only DwC terms from this list.

Modelos

Excel Template Excel Template (with example data)

Populate it and upload it to the IPT. Try to augment it with as many DwC terms as you can.

Campos DwC recomendados

Conjuntos de datos ejemplares

Preguntas frecuentes

Q. How do I indicate a species was absent?

A. Set occurrenceStatus="absent". In addition, individualCount and organismQuantity should be equal to 0.

Q. How can I generalize sensitive species occurrence data?

A. How you generalize sensitive species data (e.g. restrict the resolution of the data) depends on the species' category of sensitivity. Where there is low risk of perverse outcomes, unrestricted publication of sensitive species data remains appropriate. Note it is the responsibility of the publisher to protect sensitive species occurrence data. For guidance, please refer to this best-practice guide. You could refer to this recent essay in Science, which presents a simplified assessment scheme that can be used to help assess the risks from publishing sensitive species data.

When generalizing data you should try not to reduce the value of the data for analysis, and make users aware how and why the original record was modified using the Darwin Core term informationWithheld.

As indicated in the best-practice guide, you should also publish a checklist of the sensitive species being generalized. For each species you should explain:

  • the rationale for inclusion in the list

  • the geographic coverage of sensitivity

  • its sensitivity category

  • the date to review its sensitivity

This will help alert other data custodians that these species are regarded as potentially sensitive in a certain area and that they should take the sensitivity into account when publishing the results of their analyses, etc.

Helpful formulas for generalizing point location

The following formula obscures a latitude/longitude point by a factor of 5000m. Note pointX and pointY must be provided in 'length in meters' and TRUNC truncates the number to an integer by removing the decimal part:

pointX = TRUNC(pointX / 5000) * 5000
pointY = TRUNC(pointY / 5000) * 5000