of metadata – need corporate knowledge • Limited data available for open access exchange • Lack of info about how data was collected • Iden.fying relevant data sets • Hard to iden.fy relevant variables in some data for par.cular ques.ons • GePng data at right spa.al and temporal scale • Implica.ons of necessary assump.ons • Data (including layers) constrains spa.al resolu.on • Opportunity for map improvement • But where does the improved map end up? (c.f. data synthesis, publica.on)
• Drawing on a number of diﬀerent exis.ng data sets • Using a range of dispersal models • Need for further data collec.on and modelling by researchers • Data acquisi.on challenges • O0en sourced through personal contacts • Popula.ng the database with the right traits Data iden8ﬁca8on and acquisi8on: Aqua8c
a loca.on to bring together tracking data across disciplines • Analysis tools are the carrot to a_ract the data • Obliga.on to make data available (because you may have degraded study animals QoL) • Sourced datasets through TERN DDP ("It's awesome!") • Challenges • Reuse hard because original studies determine tag set up • Raw data on its own not enough – need rich context from data custodians/ collectors • Who owns the data?
• Lack of adequate metadata (stuﬀ just missing – DNA, loca.on) • Inadequate response from authors • Need for format conversion • Challenges – phenology monitoring • Need be_er data => protocols and standards for data capture • Tools for managing and sharing 1000s of images • No global standards for phenocams • Challenges – drought induced mortality • Data is o0en biased, incomplete and patchy (but it's all we've got some.mes)
Diﬀerent data capture technologies inﬂuence data collected • Could only use 11 of the 17 possible data sets • GePng the data online delayed publica.on of ﬁrst paper • Reluctance to release primary data (priority, errors/quality, journal policies) • Ignorance of data value (commercial exploita.on, value adding by others) • Challenges – indigenous knowledge • Interac.on between cultural landscape scales and cultural infrastructure
have • When synthesising, may be constrained by lowest quality data set • E.g. spa.al resolu.on for seagrass, existence of presence/absence only • Need to capture context in metadata (seagrass, telemetry, endemics) • Mo.vators for data exchange/availability • Answer new ques.ons through more data • Use tools that are made available as carrot • Data gets collected but doesn't always get published • Some data owners are reluctant to share for understandable human issues
paywall journals) • Role here for DDP, Research Data Australia • Data quality (or purpose) mismatch • Non-‐interoperable data • Academic ethos • Hierarchical structure incompa.ble with data sharing • Academia selects for possessiveness • Underfunding => overcontribu.on => protec.veness
combinable data to agree on minimum elements they will collect that will make datasets more reusable/recombinable • More is More: concentrate on large long-‐term ﬁeld projects with standardised instruments and data products • Research Locally, Coordinate Globally: Research Data Alliance (rd-‐ alliance.org) provides loca.on for working groups to reduce barriers to data exchange • Bribe, don't Bully: Provide tools with a_rac.ve func.onality where data sharing is easier (than what they do now) • Change the Norms: Discussion within discipline around data-‐sharing norms