cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.38n5ja
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mesostemma perfoliatum (Rech.fil.) Rech.fil."
 ]
}

 The dataset includes 7 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052656-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4qxebt
title: Occurrence Download

description: A dataset containing 15807 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Modiola caroliniana (L.) G.Don"
 ]
}

 The dataset includes 15807 records from 302 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052654-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eqq33g
title: Occurrence Download

description: A dataset containing 370 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Malva oblongifolia (Boiss.) Soldano, Banfi & Galasso"
 ]
}

 The dataset includes 370 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052657-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pcpqp8
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mesostemma platyphyllum (Rech.fil.) Rech.fil."
 ]
}

 The dataset includes 17 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052659-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.epxfkk
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Melica trebinjensis Strobl"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052660-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6x42e4
title: Occurrence Download

description: A dataset containing 4235 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Malva olbia (L.) Alef."
 ]
}

 The dataset includes 4235 records from 138 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052661-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5vkzmt
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mestoklema albanicum N.E.Br. ex Glen"
 ]
}

 The dataset includes 27 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052663-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rvd778
title: Occurrence Download

description: A dataset containing 581 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Melica turczaninowiana Ohwi"
 ]
}

 The dataset includes 581 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052662-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/v1rjpf/b8alyc
title: blc20171121_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jutceq
title: blc20170128_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sqf6uk
title: blc20170620_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/diqbvm
title: blc20170310_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8qqifo
title: blc20170911_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ab5phn
title: blc20170811_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4037400
title: Running biomechanics: resilience midsole and upper structure

description: Excel file containing all 1254 variables to the M matrix for Artificial neural network analysis and Information gain feature selection.
DOI: 10.7910/dvn/v1rjpf/hcof3u
title: blc20170105_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/khfuxd
title: blc20170704_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/4zwco2
title: blc20170210_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/2vd2dh
title: blc20171001_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xpncjx
title: blc20171013_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kwd83i
title: blc20170607_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/di8lrx
title: blc20170124_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ijs1cc
title: blc20170501_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/h52sys
title: blc20170822_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xlqfnh
title: blc20170214_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kvtrtc
title: blc20170502_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ud7mq8
title: blc20170927_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gpnd0t
title: blc20170913_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vlfspb
title: blc20171111_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xz9lx7
title: ykc20151215_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bfscqa
title: ykc20150828_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fokqht
title: ykc20151101_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/l9t3mz
title: ykc20151230_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/yrlkri
title: ykc20151209_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/if9i3h
title: ykc20150624_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/yjf0zc
title: ykc20151222_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fix3ig
title: ykc20151109_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/z4b2r9
title: ykc20151212_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vcd4qv
title: ykc20150623_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ubkifb
title: ykc20151205_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jgfury
title: ykc20151210_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ee5lym
title: ykc20150513_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zr2lib
title: ykc20151122_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/126x0v
title: ykc20151114_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rwjs42
title: ykc20151129_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sfo5tu
title: ykc20150302_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mzdqdt
title: ykc20151108_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ojhgli
title: ykc20151221_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ssh6q1
title: ykc20151206_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/oxprmv
title: ykc20150317_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bnfotd
title: ykc20151218_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/irtc19
title: ykc20151229_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ykp0m5
title: ykc20150822_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/esl9uy
title: ykc20151123_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/djc1ib
title: ykc20151219_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ncxgxm
title: ykc20151121_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wgzfhj
title: ykc20151111_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/pbukah
title: ykc20151207_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bmw0md
title: ykc20150307_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ez2i7y
title: ykc20151225_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tec7sv
title: ykc20151203_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4037235
title: 10m Wind Speed Climatology for the Gulf of California

description: Temporal averages of 10m wind speed for the Gulf of California
DOI: 10.7910/dvn/v1rjpf/vi5gzb
title: ykc20151223_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/dt4dqd
title: ykc20151104_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4037235.v1
title: CCMP_all.nc

description: Temporal averages of CCMP 10m wind speed for the Golf of California
DOI: 10.7910/dvn/v1rjpf/1lw3du
title: ykc20151201_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4037235.v2
title: 10m Wind Speed Climatology for the Golf of California

description: Temporal averages of CCMP 10m wind speed for the Golf of California
DOI: 10.7910/dvn/v1rjpf/3okdym
title: ykc20151103_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4037235.v4
title: 10m Wind Speed Climatology for the Gulf of California

description: Temporal averages of 10m wind speed for the Gulf of California
DOI: 10.7910/dvn/v1rjpf/ngwonb
title: ykc20151128_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4037235.v3
title: 10m Wind Speed Climatology for the Golf of California

description: Temporal averages of 10m wind speed for the Golf of California
DOI: 10.7910/dvn/v1rjpf/kqz0jq
title: ykc20150815_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4037022
title: Prairie plant species trait information .xlsx

description: These are species data used to calculate flowering potential (FP) metric for Biederman et al. (under review). These data include provenance (native or exotic), life-form (Forb, Legume, Graminoid, Cacti, Shrub, Tree), graminoid photosynthetic pathway (C3 vs C4), family, ecoregion(s) (short-, mixed- tall-grass prairie), flowering months, niche breadth (number of months the species flowers) and data source:<br>Baldwin et al. (Eds) (2012) The Jepson Manual: Vascular Plants of California (2nd edition).<br>Correll, Johnson (1996) Manual of the Vascular Plants of Texas. <br>Great Plains Flora Association (1986) Flora of the Great Plains. <br>Gleason, Cronquist (1991) Manual of Vascular Plants of Northeastern US and CA (2nd edition).<br>Flora of North America Editorial Committee, (1993+). Flora of North America North of Mexico<br>Britton, Brown (1970) An Illustrated Flora of the Northern US and CA.<br>www.minnesotawildflowers.info/flower/racemed-milkwort
DOI: 10.7910/dvn/v1rjpf/dangs6
title: ykc20151202_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4037022.v1
title: Plant species information .xlsx

description: These are species data used to calculate flowering potential (FP) metric for Biederman et al. (under review). These data include provenance (native or exotic), life-form (Forb, Legume, Graminoid, Cacti, Shrub, Tree), graminoid photosynthetic pathway (C3 vs C4), family, ecoregion(s) (short-, mixed- tall-grass prairie), flowering months, niche breadth (number of months the species flowers) and data source:<br>Baldwin et al. (Eds) (2012) The Jepson Manual: Vascular Plants of California (2nd edition).<br>Correll, Johnson (1996) Manual of the Vascular Plants of Texas. <br>Great Plains Flora Association (1986) Flora of the Great Plains. <br>Gleason, Cronquist (1991) Manual of Vascular Plants of Northeastern US and CA (2nd edition).<br>Flora of North America Editorial Committee, (1993+). Flora of North America North of Mexico<br>Britton, Brown (1970) An Illustrated Flora of the Northern US and CA.<br>www.minnesotawildflowers.info/flower/racemed-milkwort
DOI: 10.7910/dvn/v1rjpf/gxmzle
title: ykc20151208_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/eidbgv
title: ykc20150311_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/1g3txw
title: ykc20151214_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tocvif
title: ykc20151116_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zp9jaf
title: ykc20151112_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4036863
title: Pitfalls Data Collected in Grassland and Woodlot

description: Data for the collection of species using pitfalls placed in a grid formation in both woodlot and grassland areas. Quantification and identification of species to determine abundance. These samples were collected in both a woodlot area as well as a grassland area, both of which were found on Northern Essex Communiy College's campus. 10 cups were placed in holes that were dug into the soil for each area, 20 pitfalls in total for the entire lab. The areas are differentiated by labels in the raw data. Using a trowel, holes were dug into the soil to fit the plastic cups used for the pitfalls. The lip of the cups were made flush with the soil, this is essential to collect organisms as they could then fall into the pitfall. This was done with excess dirt if the cup could not be fit into the ground far enough. The pitfalls were left in both the woodlot and grassland for one hour so that there was time for collection to take place. They were then retrieved and all organisms inside were classified and counted. Once this was done the organisms were then released back into the environment. <br>
DOI: 10.7910/dvn/v1rjpf/ntri1n
title: ykc20151106_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4036863.v1
title: Pitfalls Data Collected in Grassland and Woodlot

description: Data for the collection of species using pitfalls placed in a grid formation in both woodlot and grassland areas. Quantification and identification of species to determine abundance. These samples were collected in both a woodlot area as well as a grassland area, both of which were found on Northern Essex Communiy College's campus. 10 cups were placed in holes that were dug into the soil for each area, 20 pitfalls in total for the entire lab. The areas are differentiated by labels in the raw data. Using a trowel, holes were dug into the soil to fit the plastic cups used for the pitfalls. The lip of the cups were made flush with the soil, this is essential to collect organisms as they could then fall into the pitfall. This was done with excess dirt if the cup could not be fit into the ground far enough. The pitfalls were left in both the woodlot and grassland for one hour so that there was time for collection to take place. They were then retrieved and all organisms inside were classified and counted. Once this was done the organisms were then released back into the environment. <br>
DOI: 10.6084/m9.figshare.4036851
title: Proportion and characteristics of men with unknown risk category in the National Prostate Cancer Register of Sweden

description: <b>Background:</b> Knowledge on missing data in a clinical cancer register is important to assess the validity of research results. For analysis of prostate cancer (Pca), risk category, a composite variable based on serum levels of prostate specific antigen (PSA), stage, and Gleason score, is crucial for treatment decisions and a strong determinant of outcome. The aim of this study was to assess the proportion and characteristics of men in the National Prostate Cancer Register (NPCR) of Sweden with unknown risk category. <b>Material and methods:</b> Men diagnosed with Pca between 1998 and 2012 registered in NPCR with known or unknown risk category were compared with respect to age, socioeconomic factors, comorbidity, cancer characteristics, cancer treatment, and mortality from Pca and other causes. <b>Results:</b> In total, 3315 of 129 391 (3%) men had unknown risk category. Compared to other men in NPCR, these men more often had a concomitant bladder cancer diagnosis, 19% versus 1%, diagnosis of benign prostatic hyperplasia 31% versus 5%, received unspecified Pca treatment 16% versus 3%, had higher comorbidity, Charlson Comorbidity Index 2 or higher, 34% versus 13%, and had lower Pca mortality 12% versus 30%, but similar mortality from other causes. <b>Conclusion:</b> Men with unknown risk category were rare in NPCR but distinctly different from other men in NPCR in many aspects including higher comorbidity and lower Pca mortality.
DOI: 10.6084/m9.figshare.4036857
title: Raw datasets for Large Scale SVM Experiment(s)

description: Raw data for the study titled "Large-scale ligand-based predictive modelling using support vector machines" and available at https://pharmb.io/publication/2016-large-scale-svm and http://dx.doi.org/10.1186/s13321-016-0151-5<br><br>Changelog:<br>V2: Add textfiles with checksums (md5 and sha1).<br>V3: Remove old tarball without checksums.
DOI: 10.7910/dvn/v1rjpf/mnfxsu
title: ykc20151216_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4036851.v1
title: Proportion and characteristics of men with unknown risk category in the National Prostate Cancer Register of Sweden

description: <b>Background:</b> Knowledge on missing data in a clinical cancer register is important to assess the validity of research results. For analysis of prostate cancer (Pca), risk category, a composite variable based on serum levels of prostate specific antigen (PSA), stage, and Gleason score, is crucial for treatment decisions and a strong determinant of outcome. The aim of this study was to assess the proportion and characteristics of men in the National Prostate Cancer Register (NPCR) of Sweden with unknown risk category. <b>Material and methods:</b> Men diagnosed with Pca between 1998 and 2012 registered in NPCR with known or unknown risk category were compared with respect to age, socioeconomic factors, comorbidity, cancer characteristics, cancer treatment, and mortality from Pca and other causes. <b>Results:</b> In total, 3315 of 129 391 (3%) men had unknown risk category. Compared to other men in NPCR, these men more often had a concomitant bladder cancer diagnosis, 19% versus 1%, diagnosis of benign prostatic hyperplasia 31% versus 5%, received unspecified Pca treatment 16% versus 3%, had higher comorbidity, Charlson Comorbidity Index 2 or higher, 34% versus 13%, and had lower Pca mortality 12% versus 30%, but similar mortality from other causes. <b>Conclusion:</b> Men with unknown risk category were rare in NPCR but distinctly different from other men in NPCR in many aspects including higher comorbidity and lower Pca mortality.
DOI: 10.7910/dvn/v1rjpf/bhixh1
title: ykc20150505_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4036857.v1
title: Raw datasets for Large Scale SVM Experiment(s)

description: Raw data for the study titled "Large-scale ligand-based predictive modelling using support vector machines" and available at https://pharmb.io/publication/2016-large-scale-svm and http://dx.doi.org/10.1186/s13321-016-0151-5
DOI: 10.6084/m9.figshare.4036812
title: Sweep Nets Data in Grassland

description: This data set was taken in a grassland field on the Northern Essex campus on 21-Oct 2016 at 9:30 in the morning. Sweep nets were used to as the sampling technique for capturing the insects in this field. A transect was randomly acquired, this transect was 15 paces along the beginning of the grassland and 30 meters into the grassland. Data was recorded a total of 10 times and each transect had a total time spend sampling for a minute each. The time spent sweeping was constant so that there was no variation in the time spent sweeping because the longer you sweep, the more insects you catch in many systems.Which would have led to a varying number of catch per unit efforts for the grassland. After each sweep, the insects were then identified and recorded and the number of each unique species of insect was also recorded. This data taken allowed us to compare the number of prey vs. the number of predators in this specific system.
DOI: 10.7910/dvn/v1rjpf/2jcro2
title: ykc20150621_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4036812.v1
title: Sweep Nets Data in Grassland

description: This data set was taken in a grassland field on the Northern Essex campus on 21-Oct 2016 at 9:30 in the morning. Sweep nets were used to as the sampling technique for capturing the insects in this field. A transect was randomly acquired, this transect was 15 paces along the beginning of the grassland and 30 meters into the grassland. Data was recorded a total of 10 times and each transect had a total time spend sampling for a minute each. The time spent sweeping was constant so that there was no variation in the time spent sweeping because the longer you sweep, the more insects you catch in many systems.Which would have led to a varying number of catch per unit efforts for the grassland. After each sweep, the insects were then identified and recorded and the number of each unique species of insect was also recorded. This data taken allowed us to compare the number of prey vs. the number of predators in this specific system.
DOI: 10.6084/m9.figshare.4036857.v2
title: Raw datasets for Large Scale SVM Experiment(s)

description: Raw data for the study titled "Large-scale ligand-based predictive modelling using support vector machines" and available at https://pharmb.io/publication/2016-large-scale-svm and http://dx.doi.org/10.1186/s13321-016-0151-5<br><br>Changelog:<br>V2: Add textfiles with checksums (md5 and sha1).
DOI: 10.6084/m9.figshare.4036797
title: Pan Trap Data Set

description: Used a grid to set up pantries in the woodlot and grassland on the Northern Essex Community College campus. Traps were made using yellow, white, and blue plastic bowls filled halfway with soapy water to collect insects. This data is used to estimate the density and abundance of different insect species in each habitat. Some traps did not collect any insects and were noted in the data as N/A.
DOI: 10.7910/dvn/v1rjpf/bzfez8
title: ykc20151127_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4036779
title: Identification of a European interserotypic reassortant strain of infectious bursal disease virus

description: Infectious bursal disease virus (IBDV, family <i>Birnaviridae</i>) is a bi-segmented double-stranded RNA virus for which two serotypes are described. Serotype 1 replicates in the bursa of Fabricius and causes an immunosuppressive and potentially fatal disease in young chickens. Serotype 2 is apathogenic in poultry species. Up to now, only one natural event of interserotypic reassortment has been described after the introduction of very virulent IBDV (vvIBDV) in the USA in 2009, resulting in an IBDV strain with its segment A related to vvIBDV and its segment B related to US serotype 2 strain OH. Here, we present the first European isolate illustrative of interserotypic reassortment. The reassorting isolate, named 100056, exhibits a genomic segment A typical of current European vvIBDV but a segment B close to European serotype 2 viruses, supporting an origin distinct from US strains. When inoculated into SPF chickens, isolate 100056 induced mild clinical signs in the absence of mortality but caused a severe bursal atrophy, which strongly suggests an immunosuppressive potential. These results illustrate that interserotypic reassortment is another mechanism that can create IBDV strains with a modified acute pathogenicity. As a consequence, and for a more precise inference of the possible phenotype, care should be taken that the molecular identification of IBDV strains is targeted to both genome segments.
DOI: 10.6084/m9.figshare.4036797.v1
title: Pan Trap Data Set

description: Used a grid to set up pantries in the woodlot and grassland on the Northern Essex Community College campus. Traps were made using yellow, white, and blue plastic bowls filled halfway with soapy water to collect insects. This data is used to estimate the density and abundance of different insect species in each habitat. Some traps did not collect any insects and were noted in the data as N/A.