Benign effects of logging on aerial insectivorous bats in Southeast Asia revealed by remote sensing technologies
<b>Description: </b><p>Number of bat calls recorded by SongMeter bat 2 detectors set to record continuously on a trigger. Counts are classified into 21 acoustic call types, including 13 species.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/101"><b>Impacts of forest modification on bats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK Natural Environment Research Council (NERC) (Human Modified Tropical Forests programme & a PhD scholarship jointly funded by University of Kent & NERC & EnvEast DTP scholarship, NE/L002582/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Economic Planning Unit of the Malaysian Government and the Sabah Biodiversity Council (Research licence UPE: 40/200/19/2723)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7740421">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_data_archive_Yoh2.xlsx</p><p><b>SAFE_data_archive_Yoh2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>All data </b> (described in worksheet MasterData)</p><p>Description: All auto and manual identifications for bat passes across a disturbance gradient, identified to functional group or species when possible</p><p>Number of fields: 13</p><p>Number of data rows: 134920</p><p>Fields: </p><ul><li><b>LOCATION</b>: Where the data was collected (Field type: location)</li><li><b>DATE</b>: Date surveyed (Field type: date)</li><li><b>TIME</b>: Time of recording (Field type: time)</li><li><b>AUTO_ID</b>: Taxa as identified using the automatic classifier (Field type: taxa)</li><li><b>ACCURACY</b>: Confidence value for auto identification results (Field type: numeric)</li><li><b>THRESLEVEL</b>: Whether the data met the desired auto-identification confidence value (Field type: categorical)</li><li><b>MANUAL_ID_CLEAN</b>: Taxa as identified manually (Field type: taxa)</li><li><b>FINAL_ID</b>: Final taxa label considering both the auto and manual ID (Field type: taxa)</li><li><b>TREATMENT</b>: Habitat type (Field type: categorical)</li><li><b>fc_100m</b>: Forest extent within 100m buffer of the survey location (Field type: numeric)</li><li><b>chm_100m</b>: Average canopy height within 100m buffer from survey location (Field type: numeric)</li><li><b>shape_100m</b>: Forest shape within 100m buffer of survey location (Field type: numeric)</li><li><b>TRI_100m</b>: Topographic ruggedness within 100m buffer of survey location (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2011-04-01 to 2012-06-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Chiroptera <br> -  -  -  -  -  [CF_CROB] <br> -  -  -  -  -  [CF_H140] <br> -  -  -  -  -  [FMQCF1] <br> -  -  -  -  -  [FMQCF2] <br> -  -  -  -  -  [FMQCF3] <br> -  -  -  -  -  [FMQCF4] <br> -  -  -  -  -  [FMQCF5] <br> -  -  -  -  -  [FMQCF6] <br> -  -  -  -  -  [QCF] <br> -  -  -  -  -  [FM] <br> -  -  -  -  -  Rhinolophidae <br> -  -  -  -  -  -  <i>Rhinolophus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus acuminatus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus affinis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus borneensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus creaghi</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus luctus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus philippinensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ater</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros diadema</i> <br> -  -  -  -  -  -  -  <i>Hipposideros galeritus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br></div><p></p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0