cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48420/17112050.v1
title: BnCN_QueF-Y5Y_GFET.csv

description: Isd of BnCN + QueF-Y5Y reaction. Mean and standard deviation of channels shown.
DOI: 10.48420/17112047
title: Static light scattering of WT QueF at 25degC and 37degC.csv

description: Light scattering for QueF at two temperatures for 68hours
DOI: 10.48420/17112047.v1
title: Static light scattering of WT QueF at 25degC and 37degC.csv

description: Light scattering for QueF at two temperatures for 68hours
DOI: 10.48420/17112044.v1
title: MD FEL IN WATER.csv

description: Radius of gyration and RMSD for QueF-WT/Y5Y over 200ns MD simulation
DOI: 10.48420/17112044
title: MD FEL IN WATER.csv

description: Radius of gyration and RMSD for QueF-WT/Y5Y over 200ns MD simulation
DOI: 10.48420/17112041
title: Protein mass spec.csv

description: Mass change for QueF with preQ0/BnCN
DOI: 10.15454/fr68sb/dn7nr0
title: Data_Nutrilip_MilkLipids.tab

description: :unav
DOI: 10.15454/fr68sb/zs1l9k
title: Metadata_Nutrilip_MilkLipids.xlsx

description: :unav
DOI: 10.48420/17112032
title: BnCN transfer characteristic raw current.csv

description: Transfer characteristics of graphene channel pre and post experiment for analysis of shift of charge neutral point. Mean and standard deviation shown.
DOI: 10.48420/17112032.v1
title: BnCN transfer characteristic raw current.csv

description: Transfer characteristics of graphene channel pre and post experiment for analysis of shift of charge neutral point. Mean and standard deviation shown.
DOI: 10.48420/17112026
title: PreQ0 transfer characteristic - Nomalised - raw.csv

description: Transfer characteristics of graphene channel pre and post experiment for analysis of shift of charge neutral point. Mean and standard deviation shown.
DOI: 10.48420/17112026.v1
title: PreQ0 transfer characteristic - Nomalised - raw.csv

description: Transfer characteristics of graphene channel pre and post experiment for analysis of shift of charge neutral point. Mean and standard deviation shown.
DOI: 10.6084/m9.figshare.20765305
title: Frontiers_table2.pdf

description: serum data
DOI: 10.6084/m9.figshare.20765305.v1
title: Frontiers_table2.pdf

description: serum data
DOI: 10.48420/17112065
title: BnCN transfer characteristic - Normalised.csv

description: Transfer characteristics of graphene channel pre and post experiment for analysis of shift of charge neutral point. Mean and standard deviation shown.
DOI: 10.48420/17112065.v1
title: BnCN transfer characteristic - Normalised.csv

description: Transfer characteristics of graphene channel pre and post experiment for analysis of shift of charge neutral point. Mean and standard deviation shown.
DOI: 10.48504/vzhm-3196
title: Hands-on
title: Dokumentation künstlerisch-technischer Prozesse im Druck
DOI: 10.48420/17112068
title: Nitrile only controls on GFET.csv

description: Isd of nitrile only control(BnCN/preQ0) on bare graphene. Mean and standard deviation of channels show.
DOI: 10.48420/17112068.v1
title: Nitrile only controls on GFET.csv

description: Isd of nitrile only control(BnCN/preQ0) on bare graphene. Mean and standard deviation of channels show.
DOI: 10.6084/m9.figshare.20765350
title: Synthetic firn seismic velocity profiles

description: Seismic Velocity profiles for firn generated using the Herron and Langway firn densification model. <br> Velocity models for the 'true' subsurface, the starting velocity model used for FWI, and the final updated velcoity model from FWI are included. <br>
DOI: 10.17637/rh.20750713
title: Research Notes

description: Anonymised notes from the interview-based research for the CDT Summer Project 2022: <strong>How does the UK innovation ecosystem foster best cybersecurity practice for suppliers of connected places solutions?</strong>. For the purpose of the summer project, this data does not include transcripts as they may contain information that is sensitive or could make it possible to identify participants and the organisations they work for.
DOI: 10.17637/rh.20750713.v1
title: Research Notes

description: Anonymised notes from the interview-based research for the CDT Summer Project 2022: <strong>How does the UK innovation ecosystem foster best cybersecurity practice for suppliers of connected places solutions?</strong>. For the purpose of the summer project, this data does not include transcripts as they may contain information that is sensitive or could make it possible to identify participants and the organisations they work for.
DOI: 10.5285/2ba39ec4-c89e-4091-bdf9-e40491ec92a5
title: Electromagnetic ion cyclotron electron diffusion coefficients calculated using Van Allen Probe EMFISIS data for a range of ion compositions

description: The banded structure of Electromagnetic Ion Cyclotron (EMIC) wave spectra and their resonant interactions with radiation belt electrons depend on the cold ion composition. However, there is a great deal of uncertainty in the composition in the inner magnetosphere due to difficulties in direct flux measurements. Here we determine the sensitivity of electron diffusion by EMIC waves to the cold ion composition. The diffusion coefficients are calculated using collocated EMIC waves spectra and plasma densities observed by Van Allen Probe EMFISIS data, parameterised by Dst, using quasi-linear theory implemented in the PADIE code. Funding was provided by NERC Highlight Topic grant: NE/P01738X/1 (Rad-Sat), NERC grant: NE/V00249X/1 (Sat-Risk) and NERC grant: NE/R016038/1
description: Data sources: * OMNIWeb (https://omniweb.gsfc.nasa.gov) for the geomagnetic and solar wind indices. * Van Allen Probes data the Level 3 and Level 4 EMFISIS magnetometer data obtained from http://emfisis.physics.uiowa.edu/data/index. The wave data was processed in Ross et al. (2021) and the technique for calculating the diffusion coefficients is given in that paper.
description: Code: PADIE (Glauert, S. A., and Horne, R. B. (2005), Calculation of pitch angle and energy diffusion coefficients with the PADIE code, J. Geophys. Res., 110, A04206, doi:10.1029/2004JA010851.)
description: The wave data used in this paper was from Ross et al. (2021).
DOI: 10.5281/zenodo.4555458
title: cwbtools

description: The 'Corpus Workbench' ('CWB', &lt;http://cwb.sourceforge.net/&gt;) offers a classic and mature approach for working with large, linguistically and structurally annotated corpora. The 'CWB' is memory efficient and its design makes running queries fast (Evert and Hardie 2011, &lt;http://www.stefan-evert.de/PUB/EvertHardie2011.pdf&gt;). The 'cwbtools' package offers pure R tools to create indexed corpus files as well as high-level wrappers for the original C implementation of CWB as exposed by the 'RcppCWB' package &lt;https://CRAN.R-project.org/package=RcppCWB&gt;. Additional functionality to add and modify annotations of corpora from within R makes working with CWB indexed corpora much more flexible and convenient. The 'cwbtools' package in combination with the R packages 'RcppCWB' (&lt;https://CRAN.R-project.org/package=RcppCWB&gt;) and 'polmineR' (&lt;https://CRAN.R-project.org/package=polmineR&gt;) offers a lightweight infrastructure to support the combination of quantitative and qualitative approaches for working with textual data.
DOI: 10.5519/qd.yg63lcpv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (463 records)
DOI: 10.6084/m9.figshare.20765488.v1
title: Downscaling Gridded Rainfall using SRGAN

description: This repository contains codes and supporting data for the article 'Generating Perceptually Realistic High-resolution Rainfall Maps using Deep Learning-based Generative Adversarial Network'.
DOI: 10.6084/m9.figshare.20748151
title: Biodiversity hotspots and their future analogs

description: Climatic analogs for six biodiversity hotspots, as presented by Ramirez et al., 2017 (1) These hotspots were extracted by a species richness map (2), presented in Ramirez et al. 2017 <br> These are raster files (.asc format), with values indicating: <br> 1: pixel within the biodiversity hotspot 2: pixel that is future climatic analog for a given pixel within the biodiversity hotspot 3: pixel within the biodiversity hotspot, that is also future climatic analog for a given pixel within the biodiversity hotspot <br> all other cells have a value of 0 <br> <br> References (1) Ramírez, F., Afán, I., Davis, L. S., & Chiaradia, A. (2017). Climate impacts on global hot spots of marine biodiversity. Science Advances, 3(2), e1601198. <br> (2) Ramírez, Francisco; Afán, Isabel; Davis, Lloyd S.; Chiaradia, Andre; 2017; "Climate impacts on global hotspots of marine biodiversity [Dataset]"; DIGITALCSIC; http://dx.doi.org/10.20350/digitalCSIC/7394 <br>
DOI: 10.6084/m9.figshare.20748151.v1
title: Biodiversity hotspots and their future analogs

description: Climatic analogs for six biodiversity hotspots, as presented by Ramirez et al., 2017 (1) These hotspots were extracted by a species richness map (2), presented in Ramirez et al. 2017 <br> These are raster files (.asc format), with values indicating: <br> 1: pixel within the biodiversity hotspot 2: pixel that is future climatic analog for a given pixel within the biodiversity hotspot 3: pixel within the biodiversity hotspot, that is also future climatic analog for a given pixel within the biodiversity hotspot <br> all other cells have a value of 0 <br> <br> References (1) Ramírez, F., Afán, I., Davis, L. S., & Chiaradia, A. (2017). Climate impacts on global hot spots of marine biodiversity. Science Advances, 3(2), e1601198. <br> (2) Ramírez, Francisco; Afán, Isabel; Davis, Lloyd S.; Chiaradia, Andre; 2017; "Climate impacts on global hotspots of marine biodiversity [Dataset]"; DIGITALCSIC; http://dx.doi.org/10.20350/digitalCSIC/7394 <br>
DOI: 10.6084/m9.figshare.20748151.v2
title: Biodiversity hotspots and their future analogs

description: Climatic analogs for six biodiversity hotspots, as presented by Ramirez et al., 2017 (1) These hotspots were extracted by a species richness map (2), presented in Ramirez et al. 2017 <br> These are raster files (.asc format), with values indicating: <br> 1: pixel within the biodiversity hotspot 2: pixel that is future climatic analog for a given pixel within the biodiversity hotspot 3: pixel within the biodiversity hotspot, that is also future climatic analog for a given pixel within the biodiversity hotspot <br> all other cells have a value of 0 <br> <br> References (1) Ramírez, F., Afán, I., Davis, L. S., & Chiaradia, A. (2017). Climate impacts on global hot spots of marine biodiversity. Science Advances, 3(2), e1601198. <br> (2) Ramírez, Francisco; Afán, Isabel; Davis, Lloyd S.; Chiaradia, Andre; 2017; "Climate impacts on global hotspots of marine biodiversity [Dataset]"; DIGITALCSIC; http://dx.doi.org/10.20350/digitalCSIC/7394 <br>
DOI: 10.5519/qd.zolsqb1t
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (464 records)
DOI: 10.6084/m9.figshare.20765320
title: Supplementary_table_11

description: Supplementary table reporting the raw counts of miRNAs residing in the 8q24 region.
DOI: 10.6084/m9.figshare.20765320.v1
title: Supplementary_table

description: Supplementary table reporting the raw counts of miRNAs residing in the 8q24 region.
DOI: 10.6084/m9.figshare.20765686
title: supplementary_table_12

description: Supplementary table reporting the adjustment variables used in the differential expression analysis.
DOI: 10.6084/m9.figshare.20765686.v2
title: supplementary_table_12

description: Supplementary table reporting the adjustment variables used in the differential expression analysis.
DOI: 10.6084/m9.figshare.20765686.v1
title: supplementary_table_XX

description: Supplementary table reporting the adjustment variables used in the differential expression analysis.
DOI: 10.34740/kaggle/dsv/4147282
title: YouTube Daily
DOI: 10.6084/m9.figshare.20747653
title: MFA connectivity

description: Climate connectivity status for Major Fishing Areas (MFA). <br> Connectivity metrics represent: mean_from: the mean percentage of pixels that will have an analog in the other MFAs in the future mean_to: mean percentage of pixels that will host analogs from other MFAs in the future
DOI: 10.6084/m9.figshare.20747653.v1
title: MFA connectivity

description: Climate connectivity status for Major Fishing Areas (MFA). <br> Connectivity metrics represent: mean_from: the mean percentage of pixels that will have an analog in the other MFAs in the future mean_to: mean percentage of pixels that will host analogs from other MFAs in the future
DOI: 10.6084/m9.figshare.20747605
title: metrics_df.xlsx

description: Surface ocean climate connectivity metrics. <br> Sheet 1 - Metrics data frame: The pixel-wise connectivity metrics <br> Sheet 2 - Variable description: Description of variables in Sheet 1
DOI: 10.6084/m9.figshare.20747605.v1
title: metrics_df.xlsx

description: Surface ocean climate connectivity metrics. <br> Sheet 1 - Metrics data frame: The pixel-wise connectivity metrics <br> Sheet 2 - Variable description: Description of variables in Sheet 1
DOI: 10.25574/57002
title: Chandra X-ray Observatory ObsId 57002
DOI: 10.25574/57006
title: Chandra X-ray Observatory ObsId 57006
DOI: 10.25574/57009
title: Chandra X-ray Observatory ObsId 57009
DOI: 10.25574/57013
title: Chandra X-ray Observatory ObsId 57013
DOI: 10.25574/57015
title: Chandra X-ray Observatory ObsId 57015
DOI: 10.25574/57019
title: Chandra X-ray Observatory ObsId 57019
DOI: 10.25574/57022
title: Chandra X-ray Observatory ObsId 57022
DOI: 10.25574/57027
title: Chandra X-ray Observatory ObsId 57027
DOI: 10.25574/57030
title: Chandra X-ray Observatory ObsId 57030
DOI: 10.25574/57033
title: Chandra X-ray Observatory ObsId 57033
DOI: 10.25574/57036
title: Chandra X-ray Observatory ObsId 57036
DOI: 10.25574/57042
title: Chandra X-ray Observatory ObsId 57042
DOI: 10.25574/57045
title: Chandra X-ray Observatory ObsId 57045
DOI: 10.25574/57049
title: Chandra X-ray Observatory ObsId 57049
DOI: 10.25574/57051
title: Chandra X-ray Observatory ObsId 57051
DOI: 10.5519/qd.ku3gx18h
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2110 records)
DOI: 10.25574/57055
title: Chandra X-ray Observatory ObsId 57055
DOI: 10.25574/57057
title: Chandra X-ray Observatory ObsId 57057
DOI: 10.25574/57061
title: Chandra X-ray Observatory ObsId 57061
DOI: 10.25574/57063
title: Chandra X-ray Observatory ObsId 57063
DOI: 10.25574/57068
title: Chandra X-ray Observatory ObsId 57068
DOI: 10.25574/57071
title: Chandra X-ray Observatory ObsId 57071
DOI: 10.25574/57077
title: Chandra X-ray Observatory ObsId 57077
DOI: 10.25574/57079
title: Chandra X-ray Observatory ObsId 57079
DOI: 10.25574/57081
title: Chandra X-ray Observatory ObsId 57081
DOI: 10.25574/57083
title: Chandra X-ray Observatory ObsId 57083
DOI: 10.25574/57084
title: Chandra X-ray Observatory ObsId 57084
DOI: 10.25574/57086
title: Chandra X-ray Observatory ObsId 57086
DOI: 10.25574/57090
title: Chandra X-ray Observatory ObsId 57090
DOI: 10.25574/57092
title: Chandra X-ray Observatory ObsId 57092
DOI: 10.25574/57095
title: Chandra X-ray Observatory ObsId 57095
DOI: 10.25574/57097
title: Chandra X-ray Observatory ObsId 57097
DOI: 10.25574/57100
title: Chandra X-ray Observatory ObsId 57100
DOI: 10.25574/57102
title: Chandra X-ray Observatory ObsId 57102
DOI: 10.25574/57104
title: Chandra X-ray Observatory ObsId 57104
DOI: 10.25574/57106
title: Chandra X-ray Observatory ObsId 57106
DOI: 10.25574/57108
title: Chandra X-ray Observatory ObsId 57108
DOI: 10.25574/57110
title: Chandra X-ray Observatory ObsId 57110
DOI: 10.25574/57113
title: Chandra X-ray Observatory ObsId 57113
DOI: 10.25574/57115
title: Chandra X-ray Observatory ObsId 57115
DOI: 10.25574/57118
title: Chandra X-ray Observatory ObsId 57118
DOI: 10.25574/57121
title: Chandra X-ray Observatory ObsId 57121
DOI: 10.25574/57123
title: Chandra X-ray Observatory ObsId 57123
DOI: 10.25574/57125
title: Chandra X-ray Observatory ObsId 57125
DOI: 10.25574/57130
title: Chandra X-ray Observatory ObsId 57130
DOI: 10.25574/57132
title: Chandra X-ray Observatory ObsId 57132
DOI: 10.25574/57134
title: Chandra X-ray Observatory ObsId 57134
DOI: 10.25574/57137
title: Chandra X-ray Observatory ObsId 57137
DOI: 10.25574/57140
title: Chandra X-ray Observatory ObsId 57140
DOI: 10.25574/57142
title: Chandra X-ray Observatory ObsId 57142
DOI: 10.25574/57144
title: Chandra X-ray Observatory ObsId 57144
DOI: 10.25574/57146
title: Chandra X-ray Observatory ObsId 57146
DOI: 10.25574/57160
title: Chandra X-ray Observatory ObsId 57160
DOI: 10.25574/57162
title: Chandra X-ray Observatory ObsId 57162
DOI: 10.25574/57165
title: Chandra X-ray Observatory ObsId 57165
DOI: 10.25574/57167
title: Chandra X-ray Observatory ObsId 57167
DOI: 10.25574/57170
title: Chandra X-ray Observatory ObsId 57170
DOI: 10.25574/57173
title: Chandra X-ray Observatory ObsId 57173
DOI: 10.25574/57178
title: Chandra X-ray Observatory ObsId 57178
DOI: 10.25574/57180
title: Chandra X-ray Observatory ObsId 57180
DOI: 10.25574/57185
title: Chandra X-ray Observatory ObsId 57185
DOI: 10.25574/57187
title: Chandra X-ray Observatory ObsId 57187
DOI: 10.25574/57189
title: Chandra X-ray Observatory ObsId 57189
DOI: 10.25574/57191
title: Chandra X-ray Observatory ObsId 57191
DOI: 10.25574/57193
title: Chandra X-ray Observatory ObsId 57193
DOI: 10.25574/57195
title: Chandra X-ray Observatory ObsId 57195
DOI: 10.25574/57197
title: Chandra X-ray Observatory ObsId 57197
DOI: 10.25574/57199
title: Chandra X-ray Observatory ObsId 57199
DOI: 10.25574/57201
title: Chandra X-ray Observatory ObsId 57201
DOI: 10.25574/57203
title: Chandra X-ray Observatory ObsId 57203
DOI: 10.25574/57207
title: Chandra X-ray Observatory ObsId 57207
DOI: 10.25574/57209
title: Chandra X-ray Observatory ObsId 57209
DOI: 10.25574/57212
title: Chandra X-ray Observatory ObsId 57212
DOI: 10.25574/57214
title: Chandra X-ray Observatory ObsId 57214
DOI: 10.25574/57216
title: Chandra X-ray Observatory ObsId 57216
DOI: 10.25574/57220
title: Chandra X-ray Observatory ObsId 57220
DOI: 10.25574/57223
title: Chandra X-ray Observatory ObsId 57223
DOI: 10.25574/57225
title: Chandra X-ray Observatory ObsId 57225
DOI: 10.25574/57227
title: Chandra X-ray Observatory ObsId 57227
DOI: 10.25574/57229
title: Chandra X-ray Observatory ObsId 57229
DOI: 10.25574/57231
title: Chandra X-ray Observatory ObsId 57231
DOI: 10.25574/57233
title: Chandra X-ray Observatory ObsId 57233
DOI: 10.25574/57235
title: Chandra X-ray Observatory ObsId 57235
DOI: 10.25574/57237
title: Chandra X-ray Observatory ObsId 57237
DOI: 10.25574/57239
title: Chandra X-ray Observatory ObsId 57239
DOI: 10.25574/57242
title: Chandra X-ray Observatory ObsId 57242
DOI: 10.25574/57244
title: Chandra X-ray Observatory ObsId 57244
DOI: 10.25574/57246
title: Chandra X-ray Observatory ObsId 57246
DOI: 10.25574/57249
title: Chandra X-ray Observatory ObsId 57249
DOI: 10.25550/1s-9z2y
title: MicroCT Scans of 6 week old Eya1 heterozygous mice and wild type littermates
DOI: 10.6084/m9.figshare.20765488.v2
title: Downscaling Gridded Rainfall using SRGAN

description: This repository contains codes and supporting data for the article 'Generating Perceptually Realistic High-resolution Rainfall Maps using Deep Learning-based Generative Adversarial Network'.
DOI: 10.6084/m9.figshare.20765929.v1
title: Raw counts and metadata

description: Table reporting in A the sample data and the sRNA-Seq raw counts of 8q24 miRNAs obtained in tissues samples of CRC patients (B), stool samples of CRC patients (C), plasma samples of CRC partients (D) urine samples of BLCA patients (E) and plasma samples of BLCA patients (F).
DOI: 10.6084/m9.figshare.20765929
title: Raw counts and metadata

description: Table reporting in A the sample data and the sRNA-Seq raw counts of 8q24 miRNAs obtained in tissues samples of CRC patients (B), stool samples of CRC patients (C), plasma samples of CRC partients (D) urine samples of BLCA patients (E) and plasma samples of BLCA patients (F).
DOI: 10.34740/kaggle/dsv/4139013
title: Smartphone Human Fall Dataset
DOI: 10.6084/m9.figshare.20406651
title: hCD8 cells in LN229 tumors after viral injection.pzfx

description: hCD8 cells infiltrating LN229 tumors ipsi- and contralateral after viral infection.
DOI: 10.6084/m9.figshare.20406660
title: hCD45 cells in LN229 tumors after viral injection.pzfx

description: hCD45 immune cells infiltrating LN229 tumors ipsi- and contralateral after viral injection.
DOI: 10.6084/m9.figshare.20406648
title: hCD4 cells in LN229 tumors after viral injection.pzfx

description: hCD4 immune cells infiltrating LN229 tumors ipsi- and contralateral after viral injection.
DOI: 10.6084/m9.figshare.20406651.v1
title: hCD8 cells in LN229 tumors after viral injection.pzfx

description: hCD8 cells infiltrating LN229 tumors ipsi- and contralateral after viral infection.
DOI: 10.6084/m9.figshare.20406660.v1
title: hCD45 cells in LN229 tumors after viral injection.pzfx

description: hCD45 immune cells infiltrating LN229 tumors ipsi- and contralateral after viral injection.
DOI: 10.6084/m9.figshare.20406639
title: hCD45 cells in U87MG tumors after Nivolumab monotherapy.pzfx

description: hCD45 immune cell infiltration in U87MG tumors ipsi and contralateral after Nivolumab monotherapy.
DOI: 10.6084/m9.figshare.20406642
title: Tumor Volume of U87MG tumors after Nivolumab monotherapy.pzfx

description: Tumor volume of U87MG tumors ipsi- and contralateral after Nivolumab monotherapy.
DOI: 10.6084/m9.figshare.20406645
title: hCD3 cells in LN229 tumors after viral injection.pzfx

description: hCD3 immue cell infiltration in LN229 tumors ipsi- and contralateral after viral injection.
DOI: 10.6084/m9.figshare.20406648.v1
title: hCD4 cells in LN229 tumors after viral injection.pzfx

description: hCD4 immune cells infiltrating LN229 tumors ipsi- and contralateral after viral injection.
DOI: 10.6084/m9.figshare.20406633
title: LN229 Calreticulin after viral infection.wsp

description: Calreticulin cell surface expression on LN229 cells after viral infection.
DOI: 10.6084/m9.figshare.20406636
title: Viral replication in PBMCs.xls

description: Viral replication in human PBMCs after viral infection.
DOI: 10.5281/zenodo.3908927
title: Paleoclimate Data-Model Comparison and the Role of Climate Forcings over the Past 1500 Years

description: The past 1500 years provide a valuable opportunity to study the response of the climate system to external forcings. However, the integration of paleoclimate proxies with climate modeling is critical to improving the understanding of climate dynamics. In this paper, a climate system model and proxy records are therefore used to study the role of natural and anthropogenic forcings in driving the global climate. The inverse and forward approaches to paleoclimate data-model comparison are applied, and sources of uncertainty are identified and discussed. In the first of two case studies, the climate model simulations are compared with multiproxy temperature reconstructions. Robust solar and volcanic signals are detected in Southern Hemisphere temperatures, with a possible volcanic signal detected in the Northern Hemisphere. The anthropogenic signal dominates during the industrial period. It is also found that seasonal and geographical biases may cause multiproxy reconstructions to overestimate the magnitude of the long-term preindustrial cooling trend. In the second case study, the model simulations are compared with a coral d18O record from the central Pacific Ocean. It is found that greenhouse gases, solar irradiance, and volcanic eruptions all influence the mean state of the central Pacific, but there is no evidence that natural or anthropogenic forcings have any systematic impact on El Nino-Southern Oscillation. The proxy climate relationship is found to change over time, challenging the assumption of stationarity that underlies the interpretation of paleoclimate proxies. These case studies demonstrate the value of paleoclimate data-model comparison but also highlight the limitations of current techniques and demonstrate the need to develop alternative approaches.