cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4620610
title: R script Gonzalez Bergonzoni from Small birds, big effects: the little auk (<i>Alle alle</i>) transforms high Arctic ecosystems

description: In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (<i>Alle alle</i>), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (<i>δ</i><sup>15</sup>N and <i>δ</i><sup>13</sup>C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical–chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated <i>δ</i><sup>15</sup>N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal <i>δ</i><sup>15</sup>N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland.
DOI: 10.6084/m9.figshare.4620604
title: NOW-isotopic summary from Small birds, big effects: the little auk (<i>Alle alle</i>) transforms high Arctic ecosystems

description: In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (<i>Alle alle</i>), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (<i>δ</i><sup>15</sup>N and <i>δ</i><sup>13</sup>C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical–chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated <i>δ</i><sup>15</sup>N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal <i>δ</i><sup>15</sup>N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland.
DOI: 10.6084/m9.figshare.4620610.v1
title: R script Gonzalez Bergonzoni from Small birds, big effects: the little auk (<i>Alle alle</i>) transforms high Arctic ecosystems

description: In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (<i>Alle alle</i>), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (<i>δ</i><sup>15</sup>N and <i>δ</i><sup>13</sup>C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical–chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated <i>δ</i><sup>15</sup>N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal <i>δ</i><sup>15</sup>N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland.
DOI: 10.6084/m9.figshare.4620604.v1
title: NOW-isotopic summary from Small birds, big effects: the little auk (<i>Alle alle</i>) transforms high Arctic ecosystems

description: In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (<i>Alle alle</i>), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (<i>δ</i><sup>15</sup>N and <i>δ</i><sup>13</sup>C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical–chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated <i>δ</i><sup>15</sup>N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal <i>δ</i><sup>15</sup>N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland.
DOI: 10.6084/m9.figshare.4620595
title: Wikimedia Nigeria Phone Survey 2016

description: Nigeria phone survey 2016<br>In the spring of 2016, the Wikimedia Foundation partnered with a phone survey company and conducted a large-scale survey to learn more about technology and Wikipedia use in Nigeria.<br>The 19 questions in the survey covered:* Internet use* Mobile phone use (smartphones & basic voice/SMS phones)* Awareness and use of Wikipedia* General demographics<br>This was a large-scale IVR phone survey, gathering over 2700 completed survey responses from randomly generated numbers across Nigeria. Voice (IVR) surveys were chosen to include respondents who may not have internet access. This approach allowed us to measure internet and smartphone penetration, along with answering other Wikipedia related questions. Also, the scale and methodology of the survey kept the margin of error low (&lt;2%) for questions asked of all respondents.<br>Questions this survey was designed to answer* What is the actual number of people who use the internet? * Real-world behavior makes this difficult to measure from industry reports, since people might have access to the internet through school, friends, internet cafés, public Wifi, etc.* What do people mostly use the internet for?* How many people use smartphones?* Do people with smartphones use the internet from just Wifi? Or just cellular service?* How many people thought they didn’t use the internet, but do use Facebook or WhatsApp?* How many people had heard of Wikipedia? What did they use it for? How often?* If they had heard of Wikipedia, but weren’t using it, why not?<br>
DOI: 10.6084/m9.figshare.4620595.v1
title: Wikimedia Nigeria Phone Survey 2016

description: Nigeria phone survey 2016<br>In the spring of 2016, the Wikimedia Foundation partnered with a phone survey company and conducted a large-scale survey to learn more about technology and Wikipedia use in Nigeria.<br>The 19 questions in the survey covered:* Internet use* Mobile phone use (smartphones & basic voice/SMS phones)* Awareness and use of Wikipedia* General demographics<br>This was a large-scale IVR phone survey, gathering over 2700 completed survey responses from randomly generated numbers across Nigeria. Voice (IVR) surveys were chosen to include respondents who may not have internet access. This approach allowed us to measure internet and smartphone penetration, along with answering other Wikipedia related questions. Also, the scale and methodology of the survey kept the margin of error low (&lt;2%) for questions asked of all respondents.<br>Questions this survey was designed to answer* What is the actual number of people who use the internet? * Real-world behavior makes this difficult to measure from industry reports, since people might have access to the internet through school, friends, internet cafés, public Wifi, etc.* What do people mostly use the internet for?* How many people use smartphones?* Do people with smartphones use the internet from just Wifi? Or just cellular service?* How many people thought they didn’t use the internet, but do use Facebook or WhatsApp?* How many people had heard of Wikipedia? What did they use it for? How often?* If they had heard of Wikipedia, but weren’t using it, why not?<br>
DOI: 10.6084/m9.figshare.4620325
title: Stocks Data

description: This is the stocks data used for analysis of monotonicity in the RSDM paper. The data was acquired from the available web API's for both https://finance.yahoo.com and investing.com
DOI: 10.6084/m9.figshare.4620268
title: Earth Analytics Python | California NEON SJER & SOAP Spatial, Field and Lidar Data

description: This teaching data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>We have recently added an additional vector layer so that cropping raster data can be taught using this data set as well.<br>
DOI: 10.6084/m9.figshare.4620217
title: D-PLACE database files

description: This data set contains a dump of the D-PLACE tree of human societies, represented in the original Newick format, and represented as CSV and SQLite compatible format.<br>These data were compiled by, and downloaded, from D-PLACE.org, which makes the data available under a CC-BY-NC license, under the proviso that the following publication is cited:<br><b>Kathryn R. Kirby, Russell D. Gray, Simon J. Greenhill, Fiona M. Jordan, Stephanie Gomes-Ng, Hans-Jörg Bibiko, Damián E. Blasi, Carlos A. Botero, Claire Bowern, Carol R. Ember, Dan Leehr, Bobbi S. Low, Joe McCarter, William Divale, and Michael C. Gavin.</b> (2016). D-PLACE: A Global Database of Cultural, Linguistic and Environmental Diversity. <i>PLoS ONE,</i> 11(7): e0158391. doi:10.1371/journal.pone.0158391.<br>
DOI: 10.6084/m9.figshare.4620268.v1
title: Week 4: Earth Analytics teaching data subset - lidar and insitu for California

description: This data subset contains a series of raster and vector files designed to be used to teach spatial data wrangling in R. This week has a focus on uncertainty<br>
DOI: 10.6084/m9.figshare.4620214
title: Green Genes database files

description: This data set contains the green genes tree 'gg_13_5_otus_99_annotated', a version of it converted to comma separated data, and a SQLite-compatible database file.
DOI: 10.6084/m9.figshare.4620268.v2
title: Spatial, Field and Lidar Data for California | Earth Analytics Course Data

description: This data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>
DOI: 10.6084/m9.figshare.4620268.v3
title: Spatial, Field and Lidar Data for California | Earth Analytics Course Data

description: This data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>
DOI: 10.6084/m9.figshare.4620268.v4
title: Spatial, Field and Lidar Data for California | Earth Analytics Course Data

description: This data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>
DOI: 10.6084/m9.figshare.4620124
title: Dataset for: A Statistical Approach to Combining Multi-Source Information in One-Class Classifiers

description: A new method is introduced for combining information from multiple sources to support one-class classification. The contributing sources may represent measurements taken by different sensors of the same physical entity, repeated measurements by a single sensor, or numerous features computed from a single measured image or signal. The approach utilizes the theory of statistical hypothesis testing, and applies Fisher’s approach to combining p-values, modified to handle non-independent sources. Classifier outputs take the form of fused p-values, which may be used to gauge the consistency of unknown entities with one or more class hypotheses. The approach enables rigorous assessment of classification uncertainties, and allows for traceability of classifier decisions back to the constituent sources, both of which are important for high-consequence decision support. Application of the technique is illustrated on two challenge problems, one for skin segmentation and the other for terrain labelling. The method is seen to be particularly effective for relatively small training samples.
DOI: 10.6084/m9.figshare.4620124.v1
title: Dataset for: A Statistical Approach to Combining Multi-Source Information in One-Class Classifiers

description: A new method is introduced for combining information from multiple sources to support one-class classification. The contributing sources may represent measurements taken by different sensors of the same physical entity, repeated measurements by a single sensor, or numerous features computed from a single measured image or signal. The approach utilizes the theory of statistical hypothesis testing, and applies Fisher’s approach to combining p-values, modified to handle non-independent sources. Classifier outputs take the form of fused p-values, which may be used to gauge the consistency of unknown entities with one or more class hypotheses. The approach enables rigorous assessment of classification uncertainties, and allows for traceability of classifier decisions back to the constituent sources, both of which are important for high-consequence decision support. Application of the technique is illustrated on two challenge problems, one for skin segmentation and the other for terrain labelling. The method is seen to be particularly effective for relatively small training samples.
DOI: 10.6084/m9.figshare.4620268.v5
title: Earth Analytics Python | California NEON SJER & SOAP Spatial, Field and Lidar Data

description: This data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>
DOI: 10.6084/m9.figshare.4619932
title: GammaModelsForPRL

description: Models for the PeVatron PRLresolution 10
DOI: 10.6084/m9.figshare.4619902
title: Data_PlosOne.xlsx

description: This dataset contains the data for Global temperatures, SOI, NAO and PDO. For the authoritative datasets please visit the following links http://hadobs.metoffice.com/hadcrut3/diagnostics/index.html http://www.cgd.ucar.edu/cas/catalog/climind/soi.html http://www.cru.uea.ac.uk/cru/data/nao/ http://jisao.washington.edu/pdo/
DOI: 10.6084/m9.figshare.4619932.v1
title: GammaModelsForPRL

description: Models for the PeVatron PRLresolution 10
DOI: 10.6084/m9.figshare.4620268.v6
title: Earth Analytics Python | California NEON SJER & SOAP Spatial, Field and Lidar Data

description: This data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>
DOI: 10.6084/m9.figshare.12909584
title: Table S.6 The robustly optimal wrapping parameters resulting in pl=4 to 16 global film layers with the robustness margins for cylindrical bale wrapped with IntelliWrap method

description: <b>Table </b><b>S.6</b> The wrapping parameters resulting in pl=4 to 16 global film layers with the component and global robustness margins for exemplary cylindrical bale wrapped with IntelliWrap method; film width 0.5 m.
DOI: 10.6084/m9.figshare.4619902.v1
title: Data_PlosOne.xlsx

description: This dataset contains the data for Global temperatures, SOI, NAO and PDO. For the authoritative datasets please visit the following links http://hadobs.metoffice.com/hadcrut3/diagnostics/index.html http://www.cgd.ucar.edu/cas/catalog/climind/soi.html http://www.cru.uea.ac.uk/cru/data/nao/ http://jisao.washington.edu/pdo/
DOI: 10.6084/m9.figshare.4619896.v1
title: Curated transcriptome assembly (peptides)

description: Data citation 2: Curated mountain hare transcriptome assembly fasta file (peptides).
DOI: 10.6084/m9.figshare.12909584.v1
title: Table S.6 The robustly optimal wrapping parameters resulting in pl=4 to 16 global film layers with the robustness margins for cylindrical bale wrapped with IntelliWrap method

description: <b>Table </b><b>S.6</b> The wrapping parameters resulting in pl=4 to 16 global film layers with the component and global robustness margins for exemplary cylindrical bale wrapped with IntelliWrap method; film width 0.5 m.
DOI: 10.6084/m9.figshare.4619884.v1
title: Curated transcriptome assembly (CDS)

description: Data citation 2: Curated mountain hare transcriptome assembly fasta file (CDS).
DOI: 10.6084/m9.figshare.4620268.v7
title: Earth Analytics Python | California NEON SJER & SOAP Spatial, Field and Lidar Data

description: This teaching data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>
DOI: 10.6084/m9.figshare.4619881.v1
title: Raw transcriptome assembly of Alpine mountain hares

description: Data citation 2: Raw assembly of RNA-sequencing reads from mountain hares sampled in the Alps.
DOI: 10.6084/m9.figshare.4619875.v1
title: Mountain hare raw transcriptome assembly

description: Data citation 2: Raw assembly of RNA-sequencing reads from mountain hares (sampled in Ireland and in the Alps).
DOI: 10.6084/m9.figshare.4619878.v1
title: Raw transcriptome assembly of Irish mountain hares

description: Data citation 2: Raw assembly of RNA-sequencing reads from mountain hares sampled in Ireland.
DOI: 10.6084/m9.figshare.4620268.v9
title: Earth Analytics Python | California NEON SJER & SOAP Spatial, Field and Lidar Data

description: This teaching data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>We have recently added an additional vector layer so that cropping raster data can be taught using this data set as well.<br>
DOI: 10.6084/m9.figshare.4617886
title: BARCS Academic Substance Use Data

description: Raw data used towards PLOS One Publication titled Longitudinal influence of alcohol and marijuana use on academic performance in college students
DOI: 10.6084/m9.figshare.4619785.v1
title: FASTQC base quality reports

description: Data citation 2: Pre and post-cleaning FASTQC base quality report.
DOI: 10.6084/m9.figshare.4620268.v8
title: Earth Analytics Python | California NEON SJER & SOAP Spatial, Field and Lidar Data

description: This teaching data subset contains<br><br>1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). <br>2. Some other general spatial boundary layers from natural earth<br>3. NEON lidar data and insitu measurements for SOAP and SJER sites. <br><br>The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. <br><br>We have recently added an additional vector layer so that cropping raster data can be taught using this data set as well.<br>
DOI: 10.6084/m9.figshare.4616611
title: PLOS_ONE_BOR_2017_DATA.xlsx

description: Connected to PLOS ONE 2017 paper: <b>Theta-burst transcranial magnetic stimulation to the prefrontal or parietal cortex does not impair metacognitive visual awareness</b><b></b>
DOI: 10.6084/m9.figshare.4616593
title: Selected network structural metrics (NESTA archive)

description: This small dataset, and corresponding figures, supports the main conclusions drawn in:<br><br>P. Cuffe, "Assortativity Anomalies in a Large Test System," in <em>IEEE Transactions on Power Systems</em>, vol. 31, no. 5, pp. 4169-4170, Sept. 2016.<br>doi: 10.1109/TPWRS.2015.2499700<br><br>These metrics indicate that 'nesta_case9241_pegase.m' is an outlier in terms of network structure, and thus may not be representative of a realistic electrical transmission system.<br><br>Regarding the rich club coefficient, see: V. Colizza, A. Flammini, M. A. Serrano, and A. Vespignani, “Detecting rich-club ordering in complex networks,” <i>Nature Physics</i>, vol. 2, no. 2, pp. 110–115, 2006. <br><br><br>