Time series of Queensland production, consumption and prices for all energy sources 1970-2017
1970-2017年昆士兰州所有能源生产、消费和价格的时间序列
{"title":"Queensland Energy DataBase","authors":"L. Molyneaux","doi":"10.14264/UQL.2019.364","DOIUrl":"https://doi.org/10.14264/UQL.2019.364","url":null,"abstract":"Time series of Queensland production, consumption and prices for all energy sources 1970-2017","PeriodicalId":243136,"journal":{"name":"UQ eSpace","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132977789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Micrometeorological Data Collected at Heron Island","authors":"H. McGowan","doi":"10.14264/uql.2019.14","DOIUrl":"https://doi.org/10.14264/uql.2019.14","url":null,"abstract":"","PeriodicalId":243136,"journal":{"name":"UQ eSpace","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122109505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yibi Chen, Raúl A. González-Pech, Timothy G. Stephens, D. Bhattacharya, C. Chan
Six previously published genomes of Symbiodiniaceae (formerly Symbiodinium) that have been further filtered for putative contaminant sequences, and re-annotated using a consistent, customised gene prediction workflow. The six taxa are Symbiodinium microadriaticum, Symbiodinium tridacnidorum, Breviolum minutum, Cladocopium goreaui, Cladocopium sp. C92 and Fugacium kawagutii. This dataset contains, for each of the six taxa, the filtered genome sequences (in FASTA), the genome annotation (in GFF3 format), as well as the revised, predicted coding sequences (in FASTA) and the associated protein sequences (in FASTA). See README.txt for details.
{"title":"Revised genome sequences and annotations of six Symbiodiniaceae taxa","authors":"Yibi Chen, Raúl A. González-Pech, Timothy G. Stephens, D. Bhattacharya, C. Chan","doi":"10.14264/uql.2019.745","DOIUrl":"https://doi.org/10.14264/uql.2019.745","url":null,"abstract":"Six previously published genomes of Symbiodiniaceae (formerly Symbiodinium) that have been further filtered for putative contaminant sequences, and re-annotated using a consistent, customised gene prediction workflow. The six taxa are Symbiodinium microadriaticum, Symbiodinium tridacnidorum, Breviolum minutum, Cladocopium goreaui, Cladocopium sp. C92 and Fugacium kawagutii. This dataset contains, for each of the six taxa, the filtered genome sequences (in FASTA), the genome annotation (in GFF3 format), as well as the revised, predicted coding sequences (in FASTA) and the associated protein sequences (in FASTA). See README.txt for details.","PeriodicalId":243136,"journal":{"name":"UQ eSpace","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125849127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Owen, É. Lèbre, D. Northcott, J. Harris, M. Huda, Brunilde Verrier
{"title":"Global mining induced displacement and resettlement (MIDR) events dataset.","authors":"J. Owen, É. Lèbre, D. Northcott, J. Harris, M. Huda, Brunilde Verrier","doi":"10.14264/uql.2019.764","DOIUrl":"https://doi.org/10.14264/uql.2019.764","url":null,"abstract":"","PeriodicalId":243136,"journal":{"name":"UQ eSpace","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Beyer, E. Kennedy, S. Wood, M. Puotinen, W. Skirving, O. Hoegh‐Guldberg
The 50 Reefs analysis (Beyer et al. 2018) was based on metrics associated with five major themes: historical (1985-2017) thermal conditions (13 metrics) (Coral Reef Watch), predicted future thermal condition estimated from general climate model (GCM) projections (8 metrics and 19 GCMs), larval connectivity and settlement (2 metrics) (Wood et al. 2014), cyclone threat (3 metrics) (Carrigan & Puotinen 2011) and recent (previous two summers) thermal conditions (4 metrics). See the attached document for a detailed description and justification of these metrics. The tabular dataset contains values for these 174 metric (columns) for each of the ~54,586 0.05 (approximately 25 km2) degree raster cells (rows) identified as containing coral habitat in the Coral Reef Watch dataset (see attached document for details). These variables were standardised to mean 0 unit variance for the analysis by subtracting the mean and dividing by the standard deviation; the table presents the original non-transformed versions of the data. The table also contains: (1) an ID field that assigns a unique numeric identification number to each row; (2) two fields representing the latitude and longitude of the centre of the cell; and (3) one field ‘score’ representing the aggregate score reported in the 50 Reefs analysis (Beyer et al. 2018). Hence, the table contains 178 fields in total.The geospatial data consists of three shapefiles: (1) CRW_coral_cell_centres.shp: a point dataset representing the centres of the 0.05 degree raster cells that the Coral Reef Watch dataset indicates may contain coral reefs. The key benefit of the point representation is that it facilitates visualisation of the data (each 0.05 is too small to be clearly visualised on a large scale map, but the size of the vector points can be readily adjusted in map-making software). The “ID” field in the attribute table of this shapefile corresponds to the ID values in the tabular dataset above. (2) 50Reefs_BCU_risk_sensitive.shp: a polygon dataset representing the locations of the biolcimatic units (BCUs) identified as the good compromise solution in the 50 Reefs analysis. These are labeled risk-sensitive as they represent a compromise between performance (quantified by exposure to climate change effects and by connectivity) and risk (variation in performance associated with uncertainty in future conditions). The risk-return trade-off was quantified using Modern Portfolio Theory (see Beyer et al. 2018 for details). (3) 50Reefs_BCU_risk_insensitive.shp: a polygon dataset representing the locations of the biolcimatic units (BCUs) identified as the high-performance, high-risk solution in the 50 Reefs analysis. These are labeled risk-insensitive because they do not account for correlations in the variance in uncertainty among BCUs (see Beyer et al. 2018 for details). For most purposes the first and second geospatial datasets will be the useful datasets, while the third geospatial dataset is included only for reference
50个珊瑚礁分析(Beyer et al. 2018)基于与五个主要主题相关的指标:历史(1985-2017)热条件(13个指标)(珊瑚礁观察)，根据一般气候模型(GCM)预测估计的未来热条件(8个指标和19个GCM)，幼虫连接和定居(2个指标)(Wood et al. 2014)，气旋威胁(3个指标)(Carrigan & Puotinen 2011)和最近(前两个夏天)热条件(4个指标)。请参阅所附文档，了解这些度量标准的详细描述和理由。表格数据集包含了174个度量(列)的值，这些度量(列)对应于珊瑚礁观察数据集中被识别为包含珊瑚栖息地的~54,586个0.05(约25平方公里)度的栅格单元(行)(详细信息见附件)。通过减去平均值并除以标准差，将这些变量标准化为分析的平均0单位方差;该表显示了数据的原始未转换版本。该表还包含:(1)一个ID字段，为每一行分配一个唯一的数字标识号;(2)两个字段表示单元格中心的纬度和经度;(3)一个字段“得分”代表50个珊瑚礁分析中报告的总分(Beyer et al. 2018)。因此，该表总共包含178个字段。地理空间数据由三个shapefile组成:(1)CRW_coral_cell_centres。shp:一个点数据集，表示珊瑚礁观察数据集显示可能包含珊瑚礁的0.05度栅格单元的中心。点表示的主要好处是它促进了数据的可视化(每个0.05都太小，无法在大比例尺地图上清晰地可视化，但矢量点的大小可以在地图制作软件中轻松调整)。这个shapefile的属性表中的“ID”字段对应于上面表格数据集中的ID值。(2) 50 reefs_bcu_risk_sensitive。shp:一个多边形数据集，表示在50个珊瑚礁分析中被确定为良好折衷解决方案的生物单元(bcu)的位置。这些指标被标记为风险敏感型，因为它们代表了绩效(通过暴露于气候变化影响和连通性来量化)和风险(与未来条件的不确定性相关的绩效变化)之间的折衷。风险回报权衡使用现代投资组合理论进行量化(详见Beyer et al. 2018)。(3) 50 reefs_bcu_risk_insensitive。shp:一个多边形数据集，表示在50个珊瑚礁分析中被确定为高性能，高风险解决方案的生物单元(bcu)的位置。这些被标记为风险不敏感，因为它们没有考虑到bcu之间不确定性差异的相关性(详见Beyer等人2018年)。在大多数情况下，第一和第二地理空间数据集将是有用的数据集，而第三地理空间数据集仅供参考。修订:本数据集于2020年1月15日进行了修订，以纠正表格和地理空间数据集中得分度量字段的错误(更多信息请参见PDF文档)。
{"title":"50 Reefs Global Coral Ocean Warming, Connectivity and Cyclone Dataset","authors":"H. Beyer, E. Kennedy, S. Wood, M. Puotinen, W. Skirving, O. Hoegh‐Guldberg","doi":"10.14264/uql.2019.782","DOIUrl":"https://doi.org/10.14264/uql.2019.782","url":null,"abstract":"The 50 Reefs analysis (Beyer et al. 2018) was based on metrics associated with five major themes: historical (1985-2017) thermal conditions (13 metrics) (Coral Reef Watch), predicted future thermal condition estimated from general climate model (GCM) projections (8 metrics and 19 GCMs), larval connectivity and settlement (2 metrics) (Wood et al. 2014), cyclone threat (3 metrics) (Carrigan & Puotinen 2011) and recent (previous two summers) thermal conditions (4 metrics). See the attached document for a detailed description and justification of these metrics. The tabular dataset contains values for these 174 metric (columns) for each of the ~54,586 0.05 (approximately 25 km2) degree raster cells (rows) identified as containing coral habitat in the Coral Reef Watch dataset (see attached document for details). These variables were standardised to mean 0 unit variance for the analysis by subtracting the mean and dividing by the standard deviation; the table presents the original non-transformed versions of the data. The table also contains: (1) an ID field that assigns a unique numeric identification number to each row; (2) two fields representing the latitude and longitude of the centre of the cell; and (3) one field ‘score’ representing the aggregate score reported in the 50 Reefs analysis (Beyer et al. 2018). Hence, the table contains 178 fields in total.The geospatial data consists of three shapefiles: (1) CRW_coral_cell_centres.shp: a point dataset representing the centres of the 0.05 degree raster cells that the Coral Reef Watch dataset indicates may contain coral reefs. The key benefit of the point representation is that it facilitates visualisation of the data (each 0.05 is too small to be clearly visualised on a large scale map, but the size of the vector points can be readily adjusted in map-making software). The “ID” field in the attribute table of this shapefile corresponds to the ID values in the tabular dataset above. (2) 50Reefs_BCU_risk_sensitive.shp: a polygon dataset representing the locations of the biolcimatic units (BCUs) identified as the good compromise solution in the 50 Reefs analysis. These are labeled risk-sensitive as they represent a compromise between performance (quantified by exposure to climate change effects and by connectivity) and risk (variation in performance associated with uncertainty in future conditions). The risk-return trade-off was quantified using Modern Portfolio Theory (see Beyer et al. 2018 for details). (3) 50Reefs_BCU_risk_insensitive.shp: a polygon dataset representing the locations of the biolcimatic units (BCUs) identified as the high-performance, high-risk solution in the 50 Reefs analysis. These are labeled risk-insensitive because they do not account for correlations in the variance in uncertainty among BCUs (see Beyer et al. 2018 for details). For most purposes the first and second geospatial datasets will be the useful datasets, while the third geospatial dataset is included only for reference","PeriodicalId":243136,"journal":{"name":"UQ eSpace","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122606100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A series of interviews with ABC journalists describing technological change in their careers
对ABC记者的一系列采访，描述了他们职业生涯中的技术变革
{"title":"Digital transformation at the ABC: A Case Study using Qualitative Interviews","authors":"H. Stewart","doi":"10.14264/uql.2019.761","DOIUrl":"https://doi.org/10.14264/uql.2019.761","url":null,"abstract":"A series of interviews with ABC journalists describing technological change in their careers","PeriodicalId":243136,"journal":{"name":"UQ eSpace","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131503342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Caesarean_data","authors":"S. Lawrence, D. Sturgess, E. Malacova, D. Reutens","doi":"10.14264/uql.2019.506","DOIUrl":"https://doi.org/10.14264/uql.2019.506","url":null,"abstract":"","PeriodicalId":243136,"journal":{"name":"UQ eSpace","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131035221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Study 1 and Study 2 data","authors":"M. Dare, J. Jetten","doi":"10.14264/uql.2019.785","DOIUrl":"https://doi.org/10.14264/uql.2019.785","url":null,"abstract":"","PeriodicalId":243136,"journal":{"name":"UQ eSpace","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128753263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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