cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1004125
title: Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets - Supplementary Information

description: Supplementary information dataset for the following article: R. Connolly and M. Connolly (2014). <em>Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets</em>. Open Peer Rev. J., 34 (Clim. Sci.), ver 0.1 (non peer-reviewed draft) <strong>Abstract of article</strong> The extent to which two widely-used monthly temperature datasets are affected by urbanization bias was considered. These were the Global Historical Climatology Network (GHCN) and the United States Historical Climatology Network (USHCN). These datasets are currently the main data sources used to construct the various weather station-based global temperature trend estimates. Although the global network nominally contains temperature records for a large number of rural stations, most of these records are quite short, or are missing large periods of data. Only eight of the records with data for at least 95 of the last 100 years are for completely rural stations. In contrast, the U.S. network is a relatively rural dataset, and less than 10% of the stations are highly urbanized. However, urbanization bias is still a significant problem, which seems to have introduced an artificial warming trend into current estimates of U.S. temperature trends. The homogenization adjustments developed by the National Climatic Data Center to reduce the extent of non-climatic biases in the networks were found to be inadequate, inappropriate and problematic for urbanization bias. As a result, the current estimates of the amount of “global warming” since the Industrial Revolution have probably been overestimated.
DOI: 10.6084/m9.figshare.1004113
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004112
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004113.v1
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004112.v1
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004111.v1
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004125.v3
title: Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets - Supplementary Information

description: Supplementary information dataset for the following article: R. Connolly and M. Connolly (2014). <em>Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets</em>. Open Peer Rev. J., 34 (Clim. Sci.), ver 0.1 (non peer-reviewed draft) <strong>Abstract of article</strong> <strong><br></strong> The extent to which two widely-used monthly temperature datasets are affected by urbanization bias was considered. These were the Global Historical Climatology Network (GHCN) and the United States Historical Climatology Network (USHCN). These datasets are currently the main data sources used to construct the various weather station-based global temperature trend estimates. Although the global network nominally contains temperature records for a large number of rural stations, most of these records are quite short, or are missing large periods of data. Only eight of the records with data for at least 95 of the last 100 years are for completely rural stations. In contrast, the U.S. network is a relatively rural dataset, and less than 10% of the stations are highly urbanized. However, urbanization bias is still a significant problem, which seems to have introduced an artificial warming trend into current estimates of U.S. temperature trends. The homogenization adjustments developed by the National Climatic Data Center to reduce the extent of non-climatic biases in the networks were found to be inadequate, inappropriate and problematic for urbanization bias. As a result, the current estimates of the amount of “global warming” since the Industrial Revolution have probably been overestimated.
DOI: 10.6084/m9.figshare.1004125.v4
title: Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets - Supplementary Information

description: Supplementary information dataset for the following article: R. Connolly and M. Connolly (2014). <em>Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets</em>. Open Peer Rev. J., 34 (Clim. Sci.), ver 0.1 (non peer-reviewed draft) <strong>Abstract of article</strong> The extent to which two widely-used monthly temperature datasets are affected by urbanization bias was considered. These were the Global Historical Climatology Network (GHCN) and the United States Historical Climatology Network (USHCN). These datasets are currently the main data sources used to construct the various weather station-based global temperature trend estimates. Although the global network nominally contains temperature records for a large number of rural stations, most of these records are quite short, or are missing large periods of data. Only eight of the records with data for at least 95 of the last 100 years are for completely rural stations. In contrast, the U.S. network is a relatively rural dataset, and less than 10% of the stations are highly urbanized. However, urbanization bias is still a significant problem, which seems to have introduced an artificial warming trend into current estimates of U.S. temperature trends. The homogenization adjustments developed by the National Climatic Data Center to reduce the extent of non-climatic biases in the networks were found to be inadequate, inappropriate and problematic for urbanization bias. As a result, the current estimates of the amount of “global warming” since the Industrial Revolution have probably been overestimated.
DOI: 10.6084/m9.figshare.1004125.v5
title: Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets - Supplementary Information

description: Supplementary information dataset for the following article: R. Connolly and M. Connolly (2014). <em>Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets</em>. Open Peer Rev. J., 34 (Clim. Sci.), ver 0.1 (non peer-reviewed draft) <strong>Abstract of article</strong> The extent to which two widely-used monthly temperature datasets are affected by urbanization bias was considered. These were the Global Historical Climatology Network (GHCN) and the United States Historical Climatology Network (USHCN). These datasets are currently the main data sources used to construct the various weather station-based global temperature trend estimates. Although the global network nominally contains temperature records for a large number of rural stations, most of these records are quite short, or are missing large periods of data. Only eight of the records with data for at least 95 of the last 100 years are for completely rural stations. In contrast, the U.S. network is a relatively rural dataset, and less than 10% of the stations are highly urbanized. However, urbanization bias is still a significant problem, which seems to have introduced an artificial warming trend into current estimates of U.S. temperature trends. The homogenization adjustments developed by the National Climatic Data Center to reduce the extent of non-climatic biases in the networks were found to be inadequate, inappropriate and problematic for urbanization bias. As a result, the current estimates of the amount of “global warming” since the Industrial Revolution have probably been overestimated.
DOI: 10.6084/m9.figshare.1004085.v1
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004066
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004067
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004065
title: Gaussian Job Archive for C50H68N4O11Zn2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004066.v1
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004067.v1
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004086.v1
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004038
title: Gaussian Job Archive for C5H8O4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004051
title: Photon count data

description: Raw photon count data for the publication “Quantitative measurement of brightness from living cells in the presence of photodepletion”. Data are stored as 16-bit unsigned binary integers without header. The data are grouped according to the figure in which they appear and represent a ‘minimal data set’. Each folder is named by the figure number. Details regarding the sample and data acquisition parameters can be found in the publication.
DOI: 10.6084/m9.figshare.1004064
title: Gaussian Job Archive for C52H74N4O10Zn2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004038.v1
title: Gaussian Job Archive for C5H8O4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004065.v1
title: Gaussian Job Archive for C50H68N4O11Zn2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004025
title: Has poor station quality biased U.S. temperature estimates? Supplementary Information

description: Supplementary information dataset for the following article: R. Connolly and M. Connolly (2014). <em>Has poor station quality biased U.S. temperature trends?</em> Open Peer Rev. J., 11 (Clim. Sci.), ver 0.1 (non peer-reviewed draft) <strong>Abstract of article</strong> Two independent surveys have found that about 70% of the thermometer stations in the U.S. Historical Climatology Network (USHCN) dataset are currently poorly or badly sited. Previous investigations into how this poor siting has affected estimates of U.S. temperature trends have led to apparently contradictory conclusions. However, in this study, these contradictions are resolved, and it is shown that poor station quality has introduced a noticeable warming bias into temperature trend estimates for the U.S. For the unadjusted station records, this poor siting increased the mean temperature trends by about 32%. When time-of-observation adjustments were applied to the records, this increased temperature trends by about 39%, and so the relative fraction of the trends due to the siting bias decreased. However, the siting biases were still substantial, and increased trends by about 18%. The step-change homogenization algorithm which had been developed to remove non-climatic biases such as siting biases was shown to be seriously problematic. Instead of correcting the poorly- and badly-sited station records to match the trends of the well-sited stations, it appears to have blended the temperature records of all stations to match the trends of the poorly-sited stations. It seems likely that similar poor siting biases also exist in global thermometer datasets, and this has probably led to an overestimation of the amount of “global warming” since the 19th century.
DOI: 10.6084/m9.figshare.1004037
title: Gaussian Job Archive for C5H8O4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004051.v2
title: Photon count data

description: Raw photon count data for the publication “Quantitative measurement of brightness from living cells in the presence of photodepletion”. Data are stored as 16-bit unsigned binary integers without header. The data are grouped according to the figure in which they appear and represent a ‘minimal data set’. Each folder is named by the figure number. Details regarding the sample and data acquisition parameters can be found in the publication.
DOI: 10.6084/m9.figshare.1004064.v1
title: Gaussian Job Archive for C52H74N4O10Zn2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004026.v1
title: Gaussian Job Archive for C6H8O13P2(4-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004037.v1
title: Gaussian Job Archive for C5H8O4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004051.v1
title: Raw data

description: This contains raw data for 'Quantitative measurement of brightness from living cells in the presence of photodepletion'.
DOI: 10.6084/m9.figshare.1004011
title: Gaussian Job Archive for C5H8O4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004015.v1
title: Gaussian Job Archive for C5H8O4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004023.v1
title: Gaussian Job Archive for C5H8O4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1003979
title: Gaussian Job Archive for C3H3N3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1003981
title: Gaussian Job Archive for H2N6(2+)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1004025.v1
title: Has poor station quality biased U.S. temperature estimates? Supplementary Information

description: Supplementary information dataset for the following article: R. Connolly and M. Connolly (2014). <em>Has poor station quality biased U.S. temperature trends?</em> Open Peer Rev. J., 11 (Clim. Sci.), ver 0.1 (non peer-reviewed draft)
DOI: 10.6084/m9.figshare.1003979.v1
title: Gaussian Job Archive for C3H3N3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1003981.v1
title: Gaussian Job Archive for H2N6(2+)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1003946
title: Gaussian Job Archive for C42H66N4O9Zn2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1003948
title: Gaussian Job Archive for C50H68N4O11Zn2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1003898
title: Delayed viability selection in kittiwakes

description: The files include code and data for the ms "Food supplementation early in life delays viability selection in a long-lived animal". A copy of the ms can be requested from the owner of the figshare account. chicks.only = data slopevar1.df = data plot.ttest.txt = data Midreturn_analysis.r = R code and explanation of all the analyses
DOI: 10.6084/m9.figshare.1003946.v1
title: Gaussian Job Archive for C42H66N4O9Zn2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1003852
title: GRAD 521 Research Data Management Course Assignments

description: Homework, midterm and final exam assignments for a series of lectures from a 10-week, 2-credit graduate-level course in research data management.The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines." Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation. The course was offered for the first time during the Winter 2014 term (January - March). See the associated syllabus, lesson plans and lectures.
DOI: 10.6084/m9.figshare.1003854
title: 10kV power supply for a Nier type ion source

description: This is a schematic for a 10kV power supply for a Nier type electron impact ion source. The power supply provides the following potentials: 1) Source HV (0-10kV) 2) Focus (0-10kV) 3) Delta Focus (+/- 0 - 500V around the focus potential) 4) Electron energy (0 - 120eV) 5) Ion repeller (-30 - +30V) 6) Trap voltage (+20 - +60V)
DOI: 10.6084/m9.figshare.1003857
title: Data for: "Forgotten treasures: the fate of data in animal behavior studies"

description: Supporting material for the article. Please refer to the file 'read_me_metadata.txt' for detailed information (i.e., metadata) of each file in the dataset. <strong>Abstract. </strong>The majority of publications across many disciplines in biology do not make their data available in repositories. On the other hand, there are several advantages associated with archiving data in open access repositories and technological resources are available to do so. To date no study has estimated the frequency of data from animal behavior publications made available in digital repositories or supplementary materials. In order to evaluate how much data from those studies are available, we surveyed publications from two well-known journals in the field. Since journal policy is an important factor influencing the availability of data from publications, we listed the data policy of journals that publish animal behavior research. We found that only a small proportion of the articles published in 2013 made even part of their data available and journals do not require data to be archived prior or after publication. If not deposited in repositories, data supporting those findings will most likely be lost due to lack of usage, inability to access obsolete storage devices or even the death of the authors. Although it is difficult to appreciate the relevance of data for future studies at the time of a research article's publication, such data may inspire fruitful opportunities that we cannot afford to lose. We discuss the benefits of making data available, review resources for data archiving and provide practical guidelines. We hope that raising awareness about this problem will help foster a data sharing culture among animal behaviorists.
DOI: 10.6084/m9.figshare.1003898.v1
title: Delayed viability selection in kittiwakes

description: The files include code and data for the ms "Food supplementation early in life delays viability selection in a long-lived animal". A copy of the ms can be requested from the owner of the figshare account. chicks.only = data slopevar1.df = data plot.ttest.txt = data Midreturn_analysis.r = R code and explanation of all the analyses
DOI: 10.6084/m9.figshare.1003948.v1
title: Gaussian Job Archive for C50H68N4O11Zn2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1003835
title: GRAD 521 Research Data Management Lectures

description: This is a series of lectures from a 10-week, 2-credit graduate-level course in research data management. <br><br>The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines." <br><br>Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation. <br><br>The course was offered for the first time during the Winter 2014 term (January - March). See the associated syllabus, lesson plans and assignments.
DOI: 10.6084/m9.figshare.1003852.v1
title: GRAD 521 Research Data Management Course Assignments

description: Homework, midterm and final exam assignments for a series of lectures from a 10-week, 2-credit graduate-level course in research data management.The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines." Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation. The course was offered for the first time during the Winter 2014 term (January - March). See the associated syllabus, lesson plans and lectures.
DOI: 10.6084/m9.figshare.1003834
title: GRAD 521 Research Data Management Syllabus and Lesson Plans

description: These are the syllabus and lesson plans for a series of lectures from a 10-week, 2-credit graduate-level course in research data management. The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines." Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation. The course was offered for the first time during the Winter 2014 term (January - March). See the associated lectures and assignments.