Seriousness perceptions of computer crime vs traditional crime

With the various advancements made in technology over the last few decades, computer crime has evolved and many are used to victimize more and more American internet users every year (NW3C, 2008). However, no researcher has examined neither how computer crime seriousness is perceived by internet users nor which (if any) social factors affect how the seriousness of computer crime is perceived. The current study attempted to determine internet users\u27 perceptions of computer crime seriousness versus traditional crime seriousness. In addition, the study tried to determine the effects of the following factors as they relate to computer crime seriousness: personal victimization, personal offending, friend offending, perception of crime prevalence in the U.S., and perceived likelihood of offender punishment. A survey was created and used to measure experience and perceptions of 313 college students from a Northern New England University. Results indicate computer crimes were rated significantly more serious in most cases, seriousness scores varied significantly by individual crime, crimes against children were rated significantly more serious than crimes against adults and perceived likelihood of offender punishment relates strongly with seriousness perception variation

sFDvent: A global trait database for deep‐sea hydrothermal‐vent fauna

Motivation: Traits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite grow‐ ing interest in a trait‐based approach to the biodiversity of the deep sea, where the impacts of human activities (including seabed mining) accelerate, there is no single re‐ pository for species traits for deep‐sea chemosynthesis‐based ecosystems, including hydrothermal vents. Using an international, collaborative approach, we have compiled the first global‐scale trait database for deep‐sea hydrothermal‐vent fauna – sFD‐ vent (sDiv‐funded trait database for the Functional Diversity of vents). We formed a funded working group to select traits appropriate to: (a) capture the performance of vent species and their influence on ecosystem processes, and (b) compare trait‐based diversity in different ecosystems. Forty contributors, representing expertise across most known hydrothermal‐vent systems and taxa, scored species traits using online collaborative tools and shared workspaces. Here, we characterise the sFDvent da‐ tabase, describe our approach, and evaluate its scope. Finally, we compare the sFD‐ vent database to similar databases from shallow‐marine and terrestrial ecosystems to highlight how the sFDvent database can inform cross‐ecosystem comparisons. We also make the sFDvent database publicly available online by assigning a persistent, unique DOI. Main types of variable contained: Six hundred and forty‐six vent species names, associated location information (33 regions), and scores for 13 traits (in categories: community structure, generalist/specialist, geographic distribution, habitat use, life history, mobility, species associations, symbiont, and trophic structure). Contributor IDs, certainty scores, and references are also provided. Spatial location and grain: Global coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid‐ocean ridges and 6 back‐arc spreading centres. Time period and grain: sFDvent includes information on deep‐sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years. Major taxa and level of measurement: Deep‐sea hydrothermal‐vent fauna with spe‐ cies‐level identification present or in progress. Software format: .csv and MS Excel (.xlsx).This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

Table_S4.5_Metadata

Here, we document the decision rules used to clean / process the data provided in Table S4.3 (raw data). We refer users to the Supporting Information accompanying the manuscript, as well as the manuscript itself, for further information

Table_S4.3_Raw

Table S4.3 is a copy of raw data contributions, compiled from the personalised files sent to each sFDvent contributor. This version of the dataset is provided for transparency and as metadata for users wanting to refer to raw contributions and/or data provided by specific contributors. This dataset includes traits that were removed from the error-checked, quality-controlled dataset due to lack of coverage and would require appropriate processing for each user’s research question before it could be used in an analysis. A glossary to support the traits and modalities given in Table S4.3 is provided in Table S4.1

sFDvent: A global trait database for deep‐sea hydrothermal‐vent fauna

Motivation: Traits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite growing interest in a trait‐based approach to the biodiversity of the deep sea, where the impacts of human activities (including seabed mining) accelerate, there is no single repository for species traits for deep‐sea chemosynthesis‐based ecosystems, including hydrothermal vents. Using an international, collaborative approach, we have compiled the first global‐scale trait database for deep‐sea hydrothermal‐vent fauna – sFDvent (s Div‐funded trait database for the F unctional D iversity of vent s). We formed a funded working group to select traits appropriate to: (a) capture the performance of vent species and their influence on ecosystem processes, and (b) compare trait‐based diversity in different ecosystems. Forty contributors, representing expertise across most known hydrothermal‐vent systems and taxa, scored species traits using online collaborative tools and shared workspaces. Here, we characterise the sFDvent database, describe our approach, and evaluate its scope. Finally, we compare the sFDvent database to similar databases from shallow‐marine and terrestrial ecosystems to highlight how the sFDvent database can inform cross‐ecosystem comparisons. We also make the sFDvent database publicly available online by assigning a persistent, unique DOI. Main types of variable contained: Six hundred and forty‐six vent species names, associated location information (33 regions), and scores for 13 traits (in categories: community structure, generalist/specialist, geographic distribution, habitat use, life history, mobility, species associations, symbiont, and trophic structure). Contributor IDs, certainty scores, and references are also provided. Spatial location and grain: Global coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid‐ocean ridges and 6 back‐arc spreading centres. Time period and grain: sFDvent includes information on deep‐sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years. Major taxa and level of measurement: Deep‐sea hydrothermal‐vent fauna with species‐level identification present or in progress. Software format: .csv and MS Excel (.xlsx)