cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6721886.v1
title: Additional file 4: of Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimerâ s disease-related proteins

description: Table S3. GO Elite analysis of differentially expressed proteins in WT, LPS-treated WT and 5xFAD mouse microglia. (XLSX 20 kb)
DOI: 10.6084/m9.figshare.6721856
title: Additional file 2: of INSaFLU: an automated open web-based bioinformatics suite “from-reads” for influenza whole-genome-sequencing-based surveillance

description: Table S1. A. INSaFLU genetic markers for type and subtype/lineage identification (“influenza_typing” database). B. INSaFLU genetic markers for the assignment of segments (and references) to draft contigs (“influenza_assign_segments2contigs” database). GISAID acknowledgement tables are included in these tables. (XLSX 45 kb)
DOI: 10.6084/m9.figshare.6721856.v1
title: Additional file 2: of INSaFLU: an automated open web-based bioinformatics suite “from-reads” for influenza whole-genome-sequencing-based surveillance

description: Table S1. A. INSaFLU genetic markers for type and subtype/lineage identification (“influenza_typing” database). B. INSaFLU genetic markers for the assignment of segments (and references) to draft contigs (“influenza_assign_segments2contigs” database). GISAID acknowledgement tables are included in these tables. (XLSX 45 kb)
DOI: 10.6084/m9.figshare.6721847
title: Additional file 2: of Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimerâ s disease-related proteins

description: Table S2. GO Elite analysis of all 4,133 proteins identified in mouse microglia by TMT mass spectrometry. (XLSX 63 kb)
DOI: 10.6084/m9.figshare.6721847.v1
title: Additional file 2: of Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimerâ s disease-related proteins

description: Table S2. GO Elite analysis of all 4,133 proteins identified in mouse microglia by TMT mass spectrometry. (XLSX 63 kb)
DOI: 10.6084/m9.figshare.6721838
title: Additional file 1: of Measuring sexual behaviour in Malawi: a triangulation of three data collection instruments

description: Data tools. (ZIP 2282 kb)
DOI: 10.6084/m9.figshare.6721838.v1
title: Additional file 1: of Measuring sexual behaviour in Malawi: a triangulation of three data collection instruments

description: Data tools. (ZIP 2282 kb)
DOI: 10.6084/m9.figshare.6721832
title: Additional file 1: of Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimerâ s disease-related proteins

description: Table S1. Log2 transformed protein abundance data and differential expression analyses. (XLSX 1522 kb)
DOI: 10.6084/m9.figshare.6721832.v1
title: Additional file 1: of Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimerâ s disease-related proteins

description: Table S1. Log2 transformed protein abundance data and differential expression analyses. (XLSX 1522 kb)
DOI: 10.6084/m9.figshare.6721655
title: PONE-03152 Data

description: This study evaluated the pattern of radiation-induced thyroid gland changes in 48 months after radiotherapy in NPC patients and the association of hypothyroidism incidence with thyroid dose.
DOI: 10.6084/m9.figshare.6721655.v1
title: PONE-03152 Data

description: This study evaluated the pattern of radiation-induced thyroid gland changes in 48 months after radiotherapy in NPC patients and the association of hypothyroidism incidence with thyroid dose.
DOI: 10.6084/m9.figshare.6721562
title: DeepGMAP-data-light.tar.gz

description: A lighter version of data set to try DeepGMAP
DOI: 10.6084/m9.figshare.6721562.v1
title: DeepGMAP-data-light.tar.gz

description: A lighter version of data set to try DeepGMAP
DOI: 10.6084/m9.figshare.6721559
title: xml_Yahara2018MGenNgo

description: The xml files used for the BEAST analyses<br>
DOI: 10.6084/m9.figshare.6721559.v1
title: xml_BEAST1.10.0_Yahara2018MGenNgo.tgz

description: The xml files used for the BEAST analyses<br>
DOI: 10.6084/m9.figshare.6721559.v2
title: xml_BEAST1.10.0_Yahara2018MGenNgo

description: The xml files used for the BEAST analyses<br>
DOI: 10.6084/m9.figshare.6721559.v3
title: xml_Yahara2018MGenNgo

description: The xml files used for the BEAST analyses<br>
DOI: 10.6084/m9.figshare.6721559.v5
title: xml_Yahara2018MGenNgo

description: The xml files used for the BEAST analyses<br>
DOI: 10.6084/m9.figshare.6721508
title: Theoretical ribosomal protein mass fingerprint of Stenotrophomonas maltophilia

description: This is a dataset containing the theoretical molecular mass of various ribosomal proteins in the large and small ribosome subunit of <i>Stenotrophomonas maltophilia.</i><br><br>
DOI: 10.6084/m9.figshare.6721508.v1
title: Theoretical ribosomal protein mass fingerprint of Stenotrophomonas maltophilia

description: This is a dataset containing the theoretical molecular mass of various ribosomal proteins in the large and small ribosome subunit of <i>Stenotrophomonas maltophilia.</i><br><br>
DOI: 10.6084/m9.figshare.6721505
title: RNAseq R/bash code

description: summary of command line and R code for C.reinhardtii RNAseq in Perlaza et al. <br>
DOI: 10.6084/m9.figshare.6721505.v1
title: RNAseq R/bash code

description: summary of command line and R code for C.reinhardtii RNAseq in Perlaza et al. <br>
DOI: 10.6084/m9.figshare.6721505.v2
title: RNAseq R/bash code

description: summary of command line and R code for C.reinhardtii RNAseq in Perlaza et al. <br>
DOI: 10.6084/m9.figshare.6721502
title: Theoretical ribosomal protein mass fingerprint of Staphylococcus xylosus

description: This is a dataset containing the theoretical molecular mass of various ribosomal proteins in the large and small ribosome subunit of <i>Staphylococcus xylosus.</i><br><br>
DOI: 10.6084/m9.figshare.6721502.v1
title: Theoretical ribosomal protein mass fingerprint of Staphylococcus xylosus

description: This is a dataset containing the theoretical molecular mass of various ribosomal proteins in the large and small ribosome subunit of <i>Staphylococcus xylosus.</i><br><br>
DOI: 10.6084/m9.figshare.6721499
title: Theoretical ribosomal protein mass fingerprint of Staphylococcus pasteuri

description: This is a dataset containing the theoretical molecular mass of various ribosomal proteins in the large and small ribosome subunit of <i>Staphylococcus pasteuri.</i><br><br>
DOI: 10.6084/m9.figshare.6721499.v1
title: Theoretical ribosomal protein mass fingerprint of Staphylococcus pasteuri

description: This is a dataset containing the theoretical molecular mass of various ribosomal proteins in the large and small ribosome subunit of <i>Staphylococcus pasteuri.</i><br><br>
DOI: 10.6084/m9.figshare.6721496
title: Dataset for: Relative abundance trends of bird populations in high intensity croplands in the Central US

description: Declining bird populations across the US have been noted in a number of studies. Although multiple explanations have been proposed as causes of these declines, agricultural intensification has often been suggested as a significant driver of bird population dynamics. We examined this relationship by comparing bird count data from the Breeding Bird Survey collected from 1995-2016 across 13 states in the central US, to corresponding categorical changes in land cover within a 2 km radius of each survey transect using spatially explicit USDA-NASS Cropland Data Layer. This approach allowed us to compare the slopes of counts for 31 species of birds between grassland- and cropland-dominated landscapes, and against increasing levels of cropland (all types combined) and pooled corn/soybean land cover types. Nearly all birds demonstrated significant responses to land cover changes. In all cases, the number of species exhibiting positive or negative responses was comparable and median differences in percent change per year ranged from -0.5 to 0.7 percent. Species that responded either positively or negatively did not appear to fall into any particular foraging guild. If changes in agricultural practices are a major cause of declines, we would expect to see it across the spatial scale studied and across the majority of species. While these results do not rule out that potential agricultural impacts, such as toxicity resultant from pesticide exposure, which may have species specific or localized impacts, a variety of factors related to habitat are likely the most significant contributor overall. Given these results over a large spatial scale basis (multi-state) and across numerous bird species, there is not a broad general trend of greater decline in crop intensive areas.
DOI: 10.6084/m9.figshare.6721496.v1
title: Dataset for: Relative abundance trends of bird populations in high intensity croplands in the Central US

description: Declining bird populations across the US have been noted in a number of studies. Although multiple explanations have been proposed as causes of these declines, agricultural intensification has often been suggested as a significant driver of bird population dynamics. We examined this relationship by comparing bird count data from the Breeding Bird Survey collected from 1995-2016 across 13 states in the central US, to corresponding categorical changes in land cover within a 2 km radius of each survey transect using spatially explicit USDA-NASS Cropland Data Layer. This approach allowed us to compare the slopes of counts for 31 species of birds between grassland- and cropland-dominated landscapes, and against increasing levels of cropland (all types combined) and pooled corn/soybean land cover types. Nearly all birds demonstrated significant responses to land cover changes. In all cases, the number of species exhibiting positive or negative responses was comparable and median differences in percent change per year ranged from -0.5 to 0.7 percent. Species that responded either positively or negatively did not appear to fall into any particular foraging guild. If changes in agricultural practices are a major cause of declines, we would expect to see it across the spatial scale studied and across the majority of species. While these results do not rule out that potential agricultural impacts, such as toxicity resultant from pesticide exposure, which may have species specific or localized impacts, a variety of factors related to habitat are likely the most significant contributor overall. Given these results over a large spatial scale basis (multi-state) and across numerous bird species, there is not a broad general trend of greater decline in crop intensive areas.
DOI: 10.5517/ccdc.csd.cc21zv96
title: CCDC 1906478: Experimental Crystal Structure Determination

description: Related Article: Qiong Wu, Yue Song, Ruotong Liu, Rui Wang, Wenjie Mei, Weiming Chen, Huanglan Yang, Xicheng Wang|2020|Bioorg.Chem.|102|104074|doi:10.1016/j.bioorg.2020.104074
DOI: 10.5517/ccdc.csd.cc24j7pm
title: CCDC 1981820: Experimental Crystal Structure Determination

description: Related Article: He-Long Peng, Xing-Yang Chen, Li-Ping Li, Bao-Hui Ye|2020|Inorg.Chim.Acta|513|119939|doi:10.1016/j.ica.2020.119939
DOI: 10.5517/ccdc.csd.cc24q6sw
title: CCDC 1987558: Experimental Crystal Structure Determination

description: Related Article: Alexander Gorbunov, Julia Kuznetsova, Ilya Deltsov, Anastasia Molokanova, Dmitry Cheshkov, Stanislav Bezzubov, Vladimir Kovalev, Ivan Vatsouro|2020|Org.Chem.Front.|7|2432|doi:10.1039/D0QO00650E
DOI: 10.5517/ccdc.csd.cc24q6vy
title: CCDC 1987560: Experimental Crystal Structure Determination

description: Related Article: Alexander Gorbunov, Julia Kuznetsova, Ilya Deltsov, Anastasia Molokanova, Dmitry Cheshkov, Stanislav Bezzubov, Vladimir Kovalev, Ivan Vatsouro|2020|Org.Chem.Front.|7|2432|doi:10.1039/D0QO00650E
DOI: 10.5517/ccdc.csd.cc24q6wz
title: CCDC 1987561: Experimental Crystal Structure Determination

description: Related Article: Alexander Gorbunov, Julia Kuznetsova, Ilya Deltsov, Anastasia Molokanova, Dmitry Cheshkov, Stanislav Bezzubov, Vladimir Kovalev, Ivan Vatsouro|2020|Org.Chem.Front.|7|2432|doi:10.1039/D0QO00650E
DOI: 10.5517/ccdc.csd.cc24x93h
title: CCDC 1993396: Experimental Crystal Structure Determination

description: Related Article: Xue-Fei Tian, Bao-Qian Ji, Lei Feng, Kai Sheng, Yan-Min Su, Marko Jagodič, Zvonko Jagličić, Chen-Ho Tung, Di Sun|2020|Dalton Trans.|49|10977|doi:10.1039/D0DT01573C
DOI: 10.5517/ccdc.csd.cc24z69n
title: CCDC 1995231: Experimental Crystal Structure Determination

description: Related Article: Andrei Nesmelov, David Lee, Christopher Bejger, Margaret Kocherga, Zachary Lyles, Madeline K. Greenier, Ashley Ariel Vitallo, Ghallia Kaouk, Daniel S. Jones, Thomas A. Schmedake|2020|Chem.Commun.|56|9846|doi:10.1039/D0CC02767G
DOI: 10.5517/ccdc.csd.cc257yt6
title: CCDC 2003648: Experimental Crystal Structure Determination

description: Related Article: Wen-Bin Chen, Li Zhong, Yun-Jing Zhong, Yi-Quan Zhang, Song Gao, Wen Dong|2020|Inorg.Chem.Front.|7|3136|doi:10.1039/D0QI00628A
DOI: 10.5517/ccdc.csd.cc257yv7
title: CCDC 2003649: Experimental Crystal Structure Determination

description: Related Article: Wen-Bin Chen, Li Zhong, Yun-Jing Zhong, Yi-Quan Zhang, Song Gao, Wen Dong|2020|Inorg.Chem.Front.|7|3136|doi:10.1039/D0QI00628A
DOI: 10.5517/ccdc.csd.cc25b6f5
title: CCDC 2005806: Experimental Crystal Structure Determination

description: Related Article: Feng-Jie Lai, Tsai-Wen Huang, Yu-Lun Chang, Heng-Yi Chang, Wei-Yi Lu, Shangwu Ding, Hsuan-Ying Chen, Chien-Chih Chiu, Kuo-Hui Wu|2020|Polymer|204|122860|doi:10.1016/j.polymer.2020.122860
DOI: 10.5517/ccdc.csd.cc25b6g6
title: CCDC 2005807: Experimental Crystal Structure Determination

description: Related Article: Feng-Jie Lai, Tsai-Wen Huang, Yu-Lun Chang, Heng-Yi Chang, Wei-Yi Lu, Shangwu Ding, Hsuan-Ying Chen, Chien-Chih Chiu, Kuo-Hui Wu|2020|Polymer|204|122860|doi:10.1016/j.polymer.2020.122860
DOI: 10.5517/ccdc.csd.cc25bd31
title: CCDC 2005982: Experimental Crystal Structure Determination

description: Related Article: Vivian Lioret, Yoann Rousselin, Richard A. Decréau|2020|Dyes Pigm.|183|108696|doi:10.1016/j.dyepig.2020.108696
DOI: 10.5517/ccdc.csd.cc25blpt
title: CCDC 2006186: Experimental Crystal Structure Determination

description: Related Article: Euan N. Bassey, Joseph A. M. Paddison, Evan N. Keyzer, Jeongjae Lee, Pascal Manuel, Ivan da Silva, Siân E. Dutton, Clare P. Grey, Matthew J. Cliffe|2020|Inorg.Chem.|59|11627|doi:10.1021/acs.inorgchem.0c01478
DOI: 10.5517/ccdc.csd.cc25blqv
title: CCDC 2006187: Experimental Crystal Structure Determination

description: Related Article: Euan N. Bassey, Joseph A. M. Paddison, Evan N. Keyzer, Jeongjae Lee, Pascal Manuel, Ivan da Silva, Siân E. Dutton, Clare P. Grey, Matthew J. Cliffe|2020|Inorg.Chem.|59|11627|doi:10.1021/acs.inorgchem.0c01478
DOI: 10.5517/ccdc.csd.cc25ch46
title: CCDC 2007037: Experimental Crystal Structure Determination

description: Related Article: Jean Fotie, Mercy Enechojo Agbo, Fengrui Qu, Trevor Tolar|2020|Tetrahedron Lett.|61|152300|doi:10.1016/j.tetlet.2020.152300
DOI: 10.5517/ccdc.csd.cc25ffxz
title: CCDC 2008921: Experimental Crystal Structure Determination

description: Related Article: Hadi Hachem, HengBo Cui, Takao Tsumuraya, Reizo Kato, Olivier Jeannin, Marc Fourmigué, Dominique Lorcy|2020|J.Mater.Chem.C|8|11581|doi:10.1039/D0TC02868A
DOI: 10.5517/ccdc.csd.cc25ffy0
title: CCDC 2008922: Experimental Crystal Structure Determination

description: Related Article: Hadi Hachem, HengBo Cui, Takao Tsumuraya, Reizo Kato, Olivier Jeannin, Marc Fourmigué, Dominique Lorcy|2020|J.Mater.Chem.C|8|11581|doi:10.1039/D0TC02868A
DOI: 10.5517/ccdc.csd.cc25ffz1
title: CCDC 2008923: Experimental Crystal Structure Determination

description: Related Article: Hadi Hachem, HengBo Cui, Takao Tsumuraya, Reizo Kato, Olivier Jeannin, Marc Fourmigué, Dominique Lorcy|2020|J.Mater.Chem.C|8|11581|doi:10.1039/D0TC02868A
DOI: 10.5517/ccdc.csd.cc25fg25
title: CCDC 2008926: Experimental Crystal Structure Determination

description: Related Article: Hadi Hachem, HengBo Cui, Takao Tsumuraya, Reizo Kato, Olivier Jeannin, Marc Fourmigué, Dominique Lorcy|2020|J.Mater.Chem.C|8|11581|doi:10.1039/D0TC02868A
DOI: 10.5517/ccdc.csd.cc25fnr1
title: CCDC 2009133: Experimental Crystal Structure Determination

description: Related Article: Natalia Muñoz-Patiño, Brenda N. Sánchez-Eguía, Daniela Araiza-Olivera, Marcos Flores-Alamo, Simón Hernández-Ortega, Diego Martínez-Otero, Ivan Castillo|2020|J.Inorg.Biochem.|211|111198|doi:10.1016/j.jinorgbio.2020.111198
DOI: 10.5517/ccdc.csd.cc24hmfq
title: CCDC 1981223: Experimental Crystal Structure Determination

description: Related Article: Alejandra Gómez, Geraldine Jara, Erick Flores, Tamara Maldonado, Fernando Godoy, Michelle Muñoz-Osses, Andrés Vega, Raul Mera, Carlos Silva, Jorge Pavez|2020|New J.Chem.|44|14171|doi:10.1039/D0NJ02328K
DOI: 10.5517/ccdc.csd.cc25g594
title: CCDC 2009615: Experimental Crystal Structure Determination

description: Related Article: Jason W. Schultz, Nigam P. Rath, Liviu M. Mirica|2020|Inorg.Chem.|59|11782|doi:10.1021/acs.inorgchem.0c01763
DOI: 10.5517/ccdc.csd.cc25ggsx
title: CCDC 2009909: Experimental Crystal Structure Determination

description: Related Article: Susanne Margot Rupf, Patrick Pröhm, Moritz Malischewski|2020|Chem.Commun.|56|9834|doi:10.1039/D0CC04226A
DOI: 10.5517/ccdc.csd.cc25ggty
title: CCDC 2009910: Experimental Crystal Structure Determination

description: Related Article: Susanne Margot Rupf, Patrick Pröhm, Moritz Malischewski|2020|Chem.Commun.|56|9834|doi:10.1039/D0CC04226A
DOI: 10.5517/ccdc.csd.cc25jl6j
title: CCDC 2011937: Experimental Crystal Structure Determination

description: Related Article: Lu Yang, Lan Qin, Yong Dou, Daopeng Zhang, Zhen Zhou, Suna Wang|2020|CrystEngComm|22|5411|doi:10.1039/D0CE00678E
DOI: 10.5517/ccdc.csd.cc25sznm
title: CCDC 2020011: Experimental Crystal Structure Determination

description: Related Article: Motofumi Osaki, Yoshinori Takashima, Shigehiro Kamitori, Hiroyasu Yamaguchi, Akira Harada|2020|J.Inclusion Phenom.Macrocyclic Chem.|96|93|doi:10.1007/s10847-019-00953-3
DOI: 10.6084/m9.figshare.12890780.v1
title: PDS Raw Data Set 4.27.18.sav

description: This is the data set of a quasi-experiment utilizing a pretest-post-test design. This study was conducted at a private, historically Black college in the southeastern region of the United States. Two investigators analyzed 81 student responses (experimental and control) on the Career Decision-Making Self-Efficacy Scale (CDMSE; Betz & Taylor, 2012) before and after they were exposed to a professional website creation experimental condition. The purpose of this data collection was to determine the extent to which engaging in this experimental condition impacted students' CDMSE scores. Permission to conduct this study was obtained from the university's institutional review board.
DOI: 10.6084/m9.figshare.12890780
title: PDS Raw Data Set 4.27.18.sav

description: This is the data set of a quasi-experiment utilizing a pretest-post-test design. This study was conducted at a private, historically Black college in the southeastern region of the United States. Two investigators analyzed 81 student responses (experimental and control) on the Career Decision-Making Self-Efficacy Scale (CDMSE; Betz & Taylor, 2012) before and after they were exposed to a professional website creation experimental condition. The purpose of this data collection was to determine the extent to which engaging in this experimental condition impacted students' CDMSE scores. Permission to conduct this study was obtained from the university's institutional review board.
DOI: 10.6084/m9.figshare.12890768
title: Whole genome sequencing data from maize landraces

description: Low-coverage (~1x) whole genome sequence data of 108 maize landraces from Mexico or South America. Landraces were sampled according to a latitudinally stratification strategy with pairs of highland and lowland landraces selected from the same 0.5 degree latitudinal band. Paired-end 150bp reads were generated on the Illumina HiSeq X 10. Raw reads are available here: /iplant/home/deruncie/HiLo/WGS
DOI: 10.6084/m9.figshare.6720596
title: Introduction to R using Stencila - starting file

description: This is a Rmd file based on Data Carpentry R lesson for Ecology which is used as a starting file for teaching data analysis with R using Stencila.
DOI: 10.6084/m9.figshare.6720596.v1
title: workshop-intro-to-R.Rmd

description: Test dataset
DOI: 10.6084/m9.figshare.6720596.v4
title: Introduction to R using Stencila - starting file

description: This is a Rmd file based on Data Carpentry R lesson for Ecology which is used as a starting file for teaching data analysis with R using Stencila.
DOI: 10.6084/m9.figshare.6720596.v3
title: Introduction to R using Stencila - starting file

description: This is a Rmd file based on Data Carpentry R lesson for Ecology which is used as a starting file for teaching data analysis with R using Stencila.
DOI: 10.6084/m9.figshare.6720251
title: Inserción laboral de investigadores

description: Informe de la 2da encuesta de inserción de investigadores hecha por ANIP a comienzos del 2018. La encuesta tiene como propósito reunir datos auto-reportados acerca de la situación académica y laboral de las investigadoras y los investigadores con posgrado.<br>Además, busca conocer la percepción de las investigadoras e investigadores sobre su propia inserción en el sistema chileno de investigación en ciencia y conocimiento.
DOI: 10.6084/m9.figshare.6720251.v1
title: Inserción laboral de investigadores

description: Informe de la 2da encuesta de inserción de investigadores hecha por ANIP a comienzos del 2018. La encuesta tiene como propósito reunir datos auto-reportados acerca de la situación académica y laboral de las investigadoras y los investigadores con posgrado.<br>Además, busca conocer la percepción de las investigadoras e investigadores sobre su propia inserción en el sistema chileno de investigación en ciencia y conocimiento.
DOI: 10.6084/m9.figshare.6720251.v2
title: Insercion laboral de investigadores

description: Informe de la 2da encuesta de inserción de investigadores hecha por ANIP a comienzos del 2018. La encuesta tiene como propósito reunir datos auto-reportados acerca de la situación académica y laboral de las investigadoras y los investigadores con posgrado.<br>Además, busca conocer la percepción de las investigadoras e investigadores sobre su propia inserción en el sistema chileno de investigación en ciencia y conocimiento.
DOI: 10.6084/m9.figshare.6720095
title: The Latent Scale Covariogram: A Tool for Exploring the Spatial Dependence Structure of Nonnormal Responses

description: When modeling spatially distributed normal responses <i>Y<sub>i</sub></i> in terms of vectors <b>x</b><sub><i>i</i></sub> of explanatory variables, one may fit a linear model assuming independence, and then use the empirical variogram of the residuals to determine an appropriate parametric form for the autocorrelation function. Suppose, however, that the responses are not normally distributed—for example, Poisson or Bernoulli. One may model spatial dependence using a hierarchical generalized linear model in which, conditional on a latent Gaussian field <b>Z</b> = {<i>Z<sub>i</sub></i>}, the <i>Y<sub>i</sub></i> have independent distributions from the exponential family, with an appropriate link function connecting their conditional means with the linear predictors <b>x</b><sup><i>t</i></sup><sub><i>i</i></sub><b>β</b> + <i>Z<sub>i</sub></i>. The question then is how to determine an appropriate model for the autocorrelation function of <b>Z</b>. The empirical variogram of the <i>Y<sub>i</sub></i> is no longer appropriate, since (unless the link function is the identity) it is on the wrong scale. We propose here an alternative, the latent scale covariogram, whose graph reflects the autocorrelation structure of the underlying normal field. We illustrate its use on several real datasets, together with a simulated dataset, and obtain results quite different from those obtained using the variogram. Supplementary materials for this article are available online.