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                <identifier>ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:12101</identifier>
                <datestamp>2026-07-11T13:51:45Z</datestamp>
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                    <dim:field mdschema="dc" element="title" lang="en">Bias in open biodiversity data: methodological implications for conservation decision-making</dim:field>
                    <dim:field mdschema="dc" element="date" qualifier="issued">2026</dim:field>
                    <dim:field mdschema="dc" element="identifier" qualifier="uri">http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/12101</dim:field>
                    <dim:field mdschema="dc" element="identifier" qualifier="uri">https://portal.sinteza.singidunum.ac.rs/Media/files/2026/570-576.pdf</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="orcid::0009-0000-2115-7283" confidence="-1">Ј. Ђукић</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="orcid::0000-0001-7921-2222" confidence="-1">D. Cvetković</dim:field>
                    <dim:field mdschema="dc" element="description" qualifier="abstract">Modern nature conservation increasingly relies on open biodiversity data,
especially on species occurrence records from digital platforms, collections,
monitoring programs, and citizen science. These datasets can cover large
areas and long time periods. However, their analytical value depends not
only on data volume, but also on representativeness, metadata quality, and
the way they were collected. One of the main problems is bias, because some
areas, taxonomic groups, time periods, and observer types generate far more
records than others. As a result, open data can give a distorted picture of
biodiversity and affect models, trend estimates, and conservation priorities.
This paper reviews the main forms of bias and links them with their effects
on analytical results and decision-making. It concludes that open biodiversity
data are highly valuable for modern nature conservation, but their analytical
and practical value depends on careful methodological use and a clear
understanding of their limits.</dim:field>
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                    <dim:field mdschema="dc" element="identifier" qualifier="doi">10.15308/Sinteza-2026-570-576</dim:field>
                    <dim:field mdschema="dc" element="source"> Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science</dim:field>
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