cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/v1rjpf/ruemtm
title: iqa20160511_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830028.v1
title: samples.zip

description: Part of the species distribution dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/uq7zhh
title: iqa20150215_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bd35vz
title: iqa20150512_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830022
title: scikit-learn rcv1 dataset lyrl2004_vectors_test_pt0.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/ybaoxw
title: iqa20150220_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mwyzg8
title: iqa20161204_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/pjrmv5
title: iqa20160702_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830022.v1
title: lyrl2004_vectors_test_pt0.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/wjpzu6
title: iqa20151206_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830019
title: scikit-learn rcv1 dataset lyrl2004_vectors_test_pt1.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/mjvl3i
title: iqa20150428_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xz6cik
title: iqa20150320_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830019.v1
title: lyrl2004_vectors_test_pt1.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.6084/m9.figshare.3830019.v2
title: scikit-learn rcv1 dataset lyrl2004_vectors_test_pt1.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/92gv2r
title: iqa20161107_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830016
title: scikit-learn rcv1 dataset lyrl2004_vectors_test_pt2.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/6zuyjm
title: iqa20160518_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ilmlk3
title: iqa20161018_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830016.v1
title: lyrl2004_vectors_test_pt2.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/g9acog
title: iqa20161207_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830013
title: scikit-learn rcv1 dataset lyrl2004_vectors_test_pt3.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/xykvtw
title: iqa20160725_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830013.v2
title: scikit-learn rcv1 dataset lyrl2004_vectors_test_pt3.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/zzd1m8
title: iqa20151229_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830013.v1
title: lyrl2004_vectors_test_pt3.dat.gz

description: Part of the rcv1 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/4qj3kb
title: iqa20161219_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830010
title: scikit-learn rcv1 dataset lyrl2004_vectors_train.dat.gz

description: Part of the rcv1 dataset used in scikit-learn
DOI: 10.7910/dvn/v1rjpf/idjarf
title: iqa20151222_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xsq8ps
title: iqa20160827_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830010.v1
title: lyrl2004_vectors_train.dat.gz

description: Part of the rcv1 dataset used in scikit-learn
DOI: 10.6084/m9.figshare.3830007
title: scikit-learn rcv1 dataset rcv1-v2.topics.qrels.gz

description: Part of the rcv1 dataset used in scikit-learn
DOI: 10.7910/dvn/v1rjpf/fvlivu
title: iqa20161206_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hmtqgj
title: iqa20160716_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wlwzez
title: iqa20150912_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lrhek5
title: iqa20150628_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830007.v1
title: rcv1-v2.topics.qrels.gz

description: Part of the rcv1 dataset used in scikit-learn
DOI: 10.6084/m9.figshare.3830004
title: EFEITOS DA INIBIÇÃO DA ÓXIDO NÍTRICO SINTASE II SENSÍVEL À AMINOGUANIDINA NAS RESPOSTAS COMPORTAMENTAIS TIPO ANSIEDADE EM ZEBRAFISHES ADULTOS (Danio rerio)

description: Painel apresentado na VII Reunião Anual da IBNeC
DOI: 10.7910/dvn/v1rjpf/dsz75o
title: iqa20150514_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830004.v2
title: EFEITOS DA INIBIÇÃO DA ÓXIDO NÍTRICO SINTASE II SENSÍVEL À AMINOGUANIDINA NAS RESPOSTAS COMPORTAMENTAIS TIPO ANSIEDADE EM ZEBRAFISHES ADULTOS (Danio rerio)

description: Painel apresentado na VII Reunião Anual da IBNeC
DOI: 10.6084/m9.figshare.3830004.v1
title: EFEITOS DA INIBIÇÃO DA ÓXIDO NÍTRICO SINTASE II SENSÍVEL À AMINOGUANIDINA NAS RESPOSTAS COMPORTAMENTAIS TIPO ANSIEDADE EM ZEBRAFISHES ADULTOS (Danio rerio)

description: Painel apresentado na VII Reunião Anual da IBNeC
DOI: 10.7910/dvn/v1rjpf/mjatqc
title: iqa20150617_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3830001
title: scikit-learn kddcup99 dataset kddcup.data.gz

description: Part of the kddcup99 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/cmcq7b
title: iqa20160105_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/1adwbd
title: iqa20160107_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829998
title: scikit-learn covtype dataset covtype.data.gz

description: Part of the covtype dataset used in scikit-learn
DOI: 10.7910/dvn/v1rjpf/bipjmt
title: iqa20151212_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/y9jnh2
title: iqa20160203_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qwtngx
title: iqa20150918_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ldgpbn
title: iqa20151228_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829995
title: scikit-learn kddcup99 dataset kddcup.data_10_percent.gz

description: Part of the kddcup99 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/hijwre
title: iqa20150630_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/klaayc
title: iqa20150504_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829995.v1
title: kddcup.data_10_percent.gz

description: Part of the kddcup99 dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/w466vo
title: iqa20150826_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829992
title: scikit-learn california housing dataset cal_housing.tgz

description: Part of the California housing dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/j37i5v
title: iqa20160525_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fj7lpc
title: iqa20150216_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/nricxt
title: iqa20160909_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829992.v1
title: cal_housing.tgz

description: Part of the California housing dataset used in scikit-learn.
DOI: 10.6084/m9.figshare.3829989
title: scikit-learn olivetti faces dataset olivettifaces.mat

description: Part of the olivetti faces dataset used in scikit-learn
DOI: 10.7910/dvn/v1rjpf/zebpyz
title: iqa20160608_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sl4fzn
title: iqa20150303_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829989.v1
title: olivettifaces.mat

description: Part of the olivetti faces dataset used in scikit-learn
DOI: 10.7910/dvn/v1rjpf/eti4ot
title: iqa20160615_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829986
title: scikit-learn lfw dataset lfw.tgz

description: Part of the LFW dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/tpvl4p
title: iqa20150820_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/svtxoc
title: iqa20160514_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/x8b9jb
title: iqa20150811_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829983
title: ANÁLISE COMPORTAMENTAL DOS EFEITOS PATOGÊNICOS DE PSEUDOLOMA NEUROPHILIA EM ZEBRAFISH (Danio rerio, Hamilton 1822)

description: Painel e resumo de trabalho apresentado na VII Reunião Anual da IBNeC, em Belém
DOI: 10.7910/dvn/v1rjpf/cigdbk
title: iqa20160810_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829983.v2
title: ANÁLISE COMPORTAMENTAL DOS EFEITOS PATOGÊNICOS DE PSEUDOLOMA NEUROPHILIA EM ZEBRAFISH (Danio rerio, Hamilton 1822)

description: Painel e resumo de trabalho apresentado na VII Reunião Anual da IBNeC, em Belém
DOI: 10.7910/dvn/v1rjpf/w8zksx
title: iqa20161224_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829980
title: scikit-learn lfw dataset lfw-funneled.tgz

description: Part of the LFW dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/qhwh6r
title: iqa20150102_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qzovio
title: iqa20150901_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829977
title: scikit-learn lfw dataset pairsDevTrain.txt

description: Part of the LFW dataset used in scikit-learn
DOI: 10.7910/dvn/v1rjpf/wyb9zt
title: iqa20150723_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jpcose
title: iqa20151224_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/enronf
title: iqa20150924_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829977.v2
title: scikit-learn LFW dataset pairsDevTrain.txt

description: Part of the LFW dataset used in scikit-learn
DOI: 10.7910/dvn/v1rjpf/cpafib
title: iqa20160820_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829974
title: scikit-learn lfw dataset pairsDevTest.txt

description: Part of the LFW dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/gn0gwv
title: iqa20160522_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8rsere
title: iqa20150930_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829974.v1
title: pairsDevTest.txt

description: Part of the LFW dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/4qy1wt
title: iqa20160626_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829971
title: scikit-learn lfw dataset pairs.txt

description: Part of the LFW dataset used in scikit-learn.
DOI: 10.7910/dvn/v1rjpf/fz0saf
title: iqa20161105_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/r4zhx6
title: iqa20160621_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ymwdgq
title: iqa20161031_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/nn3wn5
title: iqa20150926_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zkplf7
title: iqa20150103_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hw8cmf
title: iqa20151207_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829968
title: Tools for structured team communication in pre-registration health professions education: a Best Evidence Medical Education (BEME) review: BEME Guide No. 41

description: <b>Introduction:</b> Calls for the inclusion of standardized protocols for information exchange into pre-registration health professions curricula have accompanied their introduction into clinical practice. In order to help clinical educators respond to these calls, we have reviewed educational interventions for pre-registration students that incorporate one or more of these ?tools for structured communication?. <b>Methods:</b> Searches of 10 databases (1990?2014) were supplemented by hand searches and by citation searches (to January 2015). Studies evaluating an intervention for pre-registration students of any clinical profession and incorporating at least one tool were included. Quality of included studies was assessed using a checklist of 11 indicators and a narrative synthesis of findings undertaken. <b>Results:</b> Fifty studies met our inclusion criteria. Of these, 21 evaluated the specific effect of a tool on educational outcomes, and 27 met seven or more quality indicators. <b>Conclusions:</b> Pre-registration students, particularly those in the US, are learning to use tools for structured communication either in specific sessions or integrated into more extensive courses or programmes; mostly ‘Situation Background Assessment Recommendation’ and its variants. There is some evidence that learning to use a tool can improve the clarity and comprehensiveness of student communication, their perceived self-confidence and their sense of preparedness for clinical practice. There is, as yet, little evidence for the transfer of these skills to the clinical setting or for any influence of teaching approach on learning outcomes. Educators will need to consider the positioning of such learning with other skills such as clinical reasoning and decision-making.
DOI: 10.7910/dvn/v1rjpf/3dorri
title: iqa20150223_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829968.v1
title: Tools for structured team communication in pre-registration health professions education: a Best Evidence Medical Education (BEME) review: BEME Guide No. 41

description: <b>Introduction:</b> Calls for the inclusion of standardized protocols for information exchange into pre-registration health professions curricula have accompanied their introduction into clinical practice. In order to help clinical educators respond to these calls, we have reviewed educational interventions for pre-registration students that incorporate one or more of these ?tools for structured communication?. <b>Methods:</b> Searches of 10 databases (1990?2014) were supplemented by hand searches and by citation searches (to January 2015). Studies evaluating an intervention for pre-registration students of any clinical profession and incorporating at least one tool were included. Quality of included studies was assessed using a checklist of 11 indicators and a narrative synthesis of findings undertaken. <b>Results:</b> Fifty studies met our inclusion criteria. Of these, 21 evaluated the specific effect of a tool on educational outcomes, and 27 met seven or more quality indicators. <b>Conclusions:</b> Pre-registration students, particularly those in the US, are learning to use tools for structured communication either in specific sessions or integrated into more extensive courses or programmes; mostly ‘Situation Background Assessment Recommendation’ and its variants. There is some evidence that learning to use a tool can improve the clarity and comprehensiveness of student communication, their perceived self-confidence and their sense of preparedness for clinical practice. There is, as yet, little evidence for the transfer of these skills to the clinical setting or for any influence of teaching approach on learning outcomes. Educators will need to consider the positioning of such learning with other skills such as clinical reasoning and decision-making.
DOI: 10.6084/m9.figshare.3829962
title: scikit-learn 20newsgroups dataset 20news-bydate.tar.gz

description: Part of the 20newsgroup dataset used in the scikit-learn Python machine learning library (http://scikit-learn.org/).
DOI: 10.7910/dvn/v1rjpf/h4hlqb
title: iqa20151214_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/7csxjr
title: iqa20151107_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/svw0ef
title: iqa20161111_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kiknnu
title: iqa20161006_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/j5s0nz
title: iqa20150205_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829947
title: Mamajek 18: An Over-Density in Sco-Cen at D = 175 pc

description: Between the famous Upper Sco OB subgroup of Sco-Cen, and the Lupus and CrA star-forming complexes, lies a region of young (~10 Myr-old) stars at d ~ 100-200 parsecs dubbed "Lower Sco". Within the Lower Sco subregion of Sco-Cen is a very strong over-density of pre-main sequence stars which may be a candidate open cluster (indexed as "Mamajek 18"). High resolution optical spectroscopy of dozens of X-ray-emitting stars in this group were taken with the MIKE spectrograph on the Magellan Clay 6.5-m telescope in July 2007, and low-resolution spectra taken with the SMARTS 1.5-m in 2010, revealing most of the stars to be Li-rich FGK pre-MS stars. The median radial velocity of the 18 Li-rich stars in this clump is +1.7+-1.0 km/s. <br><br>Cross-referencing the candidate pre-MS stars in this cluster with the Gaia DR1 data (http://www.cosmos.esa.int/web/gaia/dr1; Gaia collaboration and Brown et al. 2016, A&A, in press, http://www.aanda.org/articles/aa/pdf/forth/aa29512-16.pdf) shows that the cluster stars have mean proper motion pmRA, pmDec = -12.2, -21.7 (+-0.3, 0.4) mas/yr. The clipped Gaia DR1 mean parallax of these stars is 5.73+-0.3 mas (approximate distance 175+-9 pc; dominated by systematic parallax error 0.3 mas). The resultant space motion for the group is U,V,W = -3.9, -19.9, -4.2 (+-1.0, 0.9, 0.6) km/s, with total heliocentric space motion 20.7+-0.6 km/s - similar to the other subgroups of Sco-Cen.<br> <br><br><br>
DOI: 10.7910/dvn/v1rjpf/nhnewn
title: iqa20160605_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kcqvsi
title: iqa20151128_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829947.v1
title: Mamajek 18: An Over-Density in Sco-Cen at D = 175 pc

description: Between the famous Upper Sco OB subgroup of Sco-Cen, and the Lupus and CrA star-forming complexes, lies a region of young (~10 Myr-old) stars at d ~ 100-200 parsecs dubbed "Lower Sco". Within the Lower Sco subregion of Sco-Cen is a very strong over-density of pre-main sequence stars which may be a candidate open cluster (indexed as "Mamajek 18"). High resolution optical spectroscopy of dozens of X-ray-emitting stars in this group were taken with the MIKE spectrograph on the Magellan Clay 6.5-m telescope in July 2007, and low-resolution spectra taken with the SMARTS 1.5-m in 2010, revealing most of the stars to be Li-rich FGK pre-MS stars. The median radial velocity of the 18 Li-rich stars in this clump is +1.7+-1.0 km/s. <br><br>Cross-referencing the candidate pre-MS stars in this cluster with the Gaia DR1 data (http://www.cosmos.esa.int/web/gaia/dr1; Gaia collaboration and Brown et al. 2016, A&A, in press, http://www.aanda.org/articles/aa/pdf/forth/aa29512-16.pdf) shows that the cluster stars have mean proper motion pmRA, pmDec = -12.2, -21.7 (+-0.3, 0.4) mas/yr. The clipped Gaia DR1 mean parallax of these stars is 5.73+-0.02 mas (approximate distance 175+-1 pc). The resultant space motion for the group is U,V,W = -3.9, -19.9, -4.2 (+-0.7, 0.6, 0.5) km/s, with total heliocentric space motion 20.7+-0.6 km/s - similar to the other subgroups of Sco-Cen.<br> <br><br><br>
DOI: 10.7910/dvn/v1rjpf/lxjaqz
title: iqa20161213_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829944
title: BIO2050 LAB 1

description: Gender: Male/female binary classification based on the what the individual chooses to identify as. It does not refer to biological status.Instagram: An online photo and video sharing application where individuals can post a picture and share it privately or publicly. <br>METHOD: <br>a. Survey 20 individuals, asking whether or not those individuals have Instagram. <br>
DOI: 10.7910/dvn/v1rjpf/2xx7ug
title: iqa20160628_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829944.v1
title: BIO2050 LAB 1

description: Gender: Male/female binary classification based on the what the individual chooses to identify as. It does not refer to biological status.Instagram: An online photo and video sharing application where individuals can post a picture and share it privately or publicly. <br>METHOD: <br>a. Survey 20 individuals, asking whether or not those individuals have Instagram. <br>
DOI: 10.7910/dvn/v1rjpf/hkoczb
title: iqa20150416_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829935
title: ecology lab 1.csv

description: the data is about student in Ecology lab 8 room 117gender: the different gender in Ecology lab 8 room.117glasses: the population wearing eye glasses in Ecology lab 8 room.117<br>
DOI: 10.6084/m9.figshare.3829935.v1
title: ecology lab 1.csv

description: the data is about student in Ecology lab 8 room 117gender: the different gender in Ecology lab 8 room.117glasses: the population wearing eye glasses in Ecology lab 8 room.117<br>
DOI: 10.7910/dvn/v1rjpf/t9toho
title: iqa20150624_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wo3saw
title: iqa20160817_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tiisv7
title: iqa20151013_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/dvmgz2
title: iqa20160512_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829923.v1
title: Embodied mood regulation: the impact of body posture on mood recovery, negative thoughts, and mood-congruent recall

description: Previous work has shown that a stooped posture may activate negative mood. Extending this work, the present experiments examine how stooped body posture influences recovery from pre-existing negative mood. In Experiment 1 (<i>n </i>= 229), participants were randomly assigned to receive either a negative or neutral mood induction, after which participants were instructed to take either a stooped, straight, or control posture while writing down their thoughts. Stooped posture (compared to straight or control postures) led to less mood recovery in the negative mood condition, and more negative mood in the neutral mood condition. Furthermore, stooped posture led to more negative thoughts overall compared to straight or control postures. In Experiment 2 (<i>n = </i>122), all participants underwent a negative mood induction, after which half received cognitive reappraisal instructions and half received no instructions. Mood-congruent cognitions were assessed through autobiographical memory recall. Again, stooped (compared to straight) position led to less mood recovery. Notably, this was independent of regulation instruction. These findings demonstrate for the first time that posture plays an important role in recovering from negative mood.
DOI: 10.6084/m9.figshare.3829914
title: A simple model for ion-channel related cardiac toxicity.pptx

description: Invited talk at NC3Rs/HESI 2016 workshop: Accelerating the acceptance of mathematical models as evidence in safety and efficacy decision making.
DOI: 10.7910/dvn/v1rjpf/8ub3c2
title: iqa20151121_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829914.v1
title: A simple model for ion-channel related cardiac toxicity.pptx

description: Invited talk at NC3Rs/HESI 2016 workshop: Accelerating the acceptance of mathematical models as evidence in safety and efficacy decision making.
DOI: 10.7910/dvn/v1rjpf/igpoiw
title: iqa20160809_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/q1gjzj
title: iqa20151111_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8xylts
title: iqa20161217_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/glsjko
title: iqa20151028_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3829899
title: A novel approach for extracting viscoelastic parameters of living cells through combination of inverse finite element simulation and Atomic Force Microscopy

description: Dynamic mechanical behaviour of living cells has been described by viscoelasticity. However, quantitation of the viscoelastic parameters for living cells is far from sophisticated. In this paper, combining inverse finite element (FE) simulation with Atomic Force Microscope characterization, we attempt to develop a new method to evaluate and acquire trustworthy viscoelastic index of living cells. First, influence of the experiment parameters on stress relaxation process is assessed using FE simulation. As suggested by the simulations, cell height has negligible impact on shape of the force–time curve, i.e. the characteristic relaxation time; and the effect originates from substrate can be totally eliminated when stiff substrate (Young’s modulus larger than 3 GPa) is used. Then, so as to develop an effective optimization strategy for the inverse FE simulation, the parameters sensitivity evaluation is performed for Young’s modulus, Poisson’s ratio, and characteristic relaxation time. With the experiment data obtained through typical stress relaxation measurement, viscoelastic parameters are extracted through the inverse FE simulation by comparing the simulation results and experimental measurements. Finally, reliability of the acquired mechanical parameters is verified with different load experiments performed on the same cell.