Cameron Neylon - Lightning talk at NISO Altmetrics Initiative
1. 09.10.13 ALM Principles discussion document - Google Drive
https://docs.google.com/a/martinfenner.org/document/d/1TWPzw0El3KGw-CJrA_VJaOmv9nTxfaggkhRRvUzbb_o/edit#heading=h.6q44zvf4m1bs 1/3
Usage and article measures: Community
principles
The community of stakeholders engaged in measuring the usage, impact, and conversations
around scholarly articles adopts these community principles.
Preamble
We critique and measure research outputs for many reasons, to understand their importance,
their relevance to a particular problem, and to support discovery of new resources. New forms
of data are becoming available that can enrich our understanding of how individual research
outputs are being used: who is using them, what are they being used for, and when. The
responsible application of these new data to the challenges of research assessment places
requirements on those who generate, curate, and analyse these data.
As core members of the stakeholder community who provide, integrate, analyse, and use this
data we affirm our commitment to providing data to support measuring and tracking the use and
interest in research outputs as as a community resource. We call for the creation of a shared
community infrastructure for aggregating and validating article-level measures and for the
adoption of the following community principles.
Principles
1. Publishers should collect data on the usage of all their individual articles.
2. Usage data should be comprehensive and should include usage statistics;; citations;; and
other social media usage and references.
3. Data should be freely and openly available with reuse permitted to the fullest extent
possible. Data should be made available at the individual article level and in bulk.
4. The collection, aggregation, and reporting of data should be supported by best practices
agreed by the community with development towards standards. Data providers and data
aggregators will promote good practice in the use and the application of this data.
5. Data from different sources and service providers should be aggregated via an
infrastructure supported by the community to facilitate data validation and comparisons of
data. An organization taking on this task would be a trusted community organization
outside the control of any one major stakeholder.
2. 09.10.13 ALM Principles discussion document - Google Drive
https://docs.google.com/a/martinfenner.org/document/d/1TWPzw0El3KGw-CJrA_VJaOmv9nTxfaggkhRRvUzbb_o/edit#heading=h.6q44zvf4m1bs 2/3
Issues
Insufficiently
ambitious?
Greater
scope
for
data
sources
Insufficiently
ambitious?
Greater
scope
for
object
types
How
will
this
be
addressed
for
smaller
journals?
What
is
the
commitment
of
the
community
to
help?
What
is
the
value
of
a
centralised
“service”
or
clearing
house?
Would
a
standards
body
be
better?
What
might
the
role
of
Crossref
be?
Who
gets
to
decide?
Who
is
the
community?
3. 09.10.13 ALM Principles discussion document - Google Drive
https://docs.google.com/a/martinfenner.org/document/d/1TWPzw0El3KGw-CJrA_VJaOmv9nTxfaggkhRRvUzbb_o/edit#heading=h.6q44zvf4m1bs 3/3
I
create
derivative
data
or
value
added
services?
Do
you
mean
I
should
give
them
away?
How
to
distinguish
the
“underlying”
data
and
what
is
my
derived
service
offering?
How
does
this
apply
to
social
media
data?
Should
we
collect
it
together?
What
are
the
risks
in
using
one
collection
mechanism?
The
benefits?
Who
should
be
driving
this,
and
what
is
the
intention?
Is
now
the
right
time
for
this
-‐
shouldn’t
we
be
focusing
on
pushing
and
delivering
on
DORA
and
the
NISO
initiative
first