cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6264779
title: Fynbos networking and knowledge sharing workshop: feedback (Fynbos Forum 2013)

description: This presentation summarises feedback on the Fynbos Fynbos networking and knowledge sharing workshop run at the Fynbos Forum meeting at Kirstenbosch in 2013.
DOI: 10.6084/m9.figshare.6264767
title: Untitled Item

description: raw data, syntax and output
DOI: 10.6084/m9.figshare.6264758
title: data for the 2018 GRL paper

description: Here we provide the time series showed by Fig. 1 (bedload transport rate) and Fig.5 (spatio-temporal evolution of bed profiles) and the Matlab scripts used to plot them.<br>
DOI: 10.6084/m9.figshare.6264758.v1
title: data for the 2018 GRL paper

description: Here we provide the time series showed by Fig. 1 (bedload transport rate) and the Matlab script used to plot it.<br>
DOI: 10.6084/m9.figshare.6264728
title: Supplementary Material for: A Retrospective Cohort Study on Patients with Hidradenitis Suppurativa

description: <b><i>Background:</i></b> Hidradenitis suppurativa (HS) is one of the most distressing conditions observed in dermatology and has a substantial negative effect on the quality of life of affected persons. <b><i>Objectives:</i></b> The aim of this study was to evaluate different treatment strategies in patients with HS. <b><i>Methods:</i></b> In a retrospective cohort, all patients with HS (July 2015 to March 2017) were reviewed. Collected data consisted of patients’ demographics, clinical characteristics, psychosocial situation, and previous and current treatments. In addition, therapy response to the most recent prescribed treatments was assessed. <b><i>Results:</i></b> 102 patients (38 females, 64 males; median age 37.5 years) were included in this study. 68.4% of patients had BMI ≥25, and 76.5% of patients were current smokers. Hurley stages I, II, and III were detected in 13.5, 53.1, and 33.3% of patients, respectively. The most commonly administered treatments were surgery (67.6%), nonantibiotic topical therapies (72.5%), antibiotic topical therapies (55.9%), systemic antibiotics (88.2%), and biologics in 11.8% of the patients. 84.6% of the patients showed a response (27.5 and 47.1% partial and complete response, respectively) to the treatments. <b><i>Conclusion:</i></b> HS as a chronic, recurrent inflammatory skin disease is associated with smoking and obesity. Application of systemic antibiotics is the most frequent treatment strategy for this disease. However, surgical intervention seems to be the most effective treatment strategy.
DOI: 10.6084/m9.figshare.6264728.v1
title: Supplementary Material for: A Retrospective Cohort Study on Patients with Hidradenitis Suppurativa

description: <b><i>Background:</i></b> Hidradenitis suppurativa (HS) is one of the most distressing conditions observed in dermatology and has a substantial negative effect on the quality of life of affected persons. <b><i>Objectives:</i></b> The aim of this study was to evaluate different treatment strategies in patients with HS. <b><i>Methods:</i></b> In a retrospective cohort, all patients with HS (July 2015 to March 2017) were reviewed. Collected data consisted of patients’ demographics, clinical characteristics, psychosocial situation, and previous and current treatments. In addition, therapy response to the most recent prescribed treatments was assessed. <b><i>Results:</i></b> 102 patients (38 females, 64 males; median age 37.5 years) were included in this study. 68.4% of patients had BMI ≥25, and 76.5% of patients were current smokers. Hurley stages I, II, and III were detected in 13.5, 53.1, and 33.3% of patients, respectively. The most commonly administered treatments were surgery (67.6%), nonantibiotic topical therapies (72.5%), antibiotic topical therapies (55.9%), systemic antibiotics (88.2%), and biologics in 11.8% of the patients. 84.6% of the patients showed a response (27.5 and 47.1% partial and complete response, respectively) to the treatments. <b><i>Conclusion:</i></b> HS as a chronic, recurrent inflammatory skin disease is associated with smoking and obesity. Application of systemic antibiotics is the most frequent treatment strategy for this disease. However, surgical intervention seems to be the most effective treatment strategy.
DOI: 10.6084/m9.figshare.6264722
title: Supplementary Material for: Individual Negative Affective Trajectories Can Be Detected during Different Depressive Relapse Prevention Strategies

description: Medicine
DOI: 10.6084/m9.figshare.6264722.v1
title: Supplementary Material for: Individual Negative Affective Trajectories Can Be Detected during Different Depressive Relapse Prevention Strategies

description: Medicine
DOI: 10.6084/m9.figshare.6264632
title: Supplementary Material for: Determination of the Endocannabinoids Anandamide and 2-Arachidonoyl Glycerol with Gas Chromatography-Mass Spectrometry: Analytical and Preanalytical Challenges and Pitfalls

description: <b><i>Background:</i></b> The endocannabinoids anandamide (<i>N</i>-arachidonoyl ethanolamide [AEA]) and 2-arachidonoyl glycerol (2-AG) are involved in the regulation of neuronal, immune, metabolic, vascular, and reproductory functions. <b><i>Methods:</i></b> The development and validation of an analytical method for the determination of AEA and 2-AG in human plasma based on liquid-liquid extraction and gas chromatography-mass spectrometry after silylation is described and (pre)-analytical pitfalls are identified. <b><i>Results:</i></b> In contrast to 2-AG, AEA was unstable in whole blood and increased by a factor of 2.3 within 3 h on ice. AEA was stable in plasma on ice for 4 h while 2-AG tended to decrease. Excellent stability at room/ambient temperature was found for both derivatized compounds over 45 h. Furthermore, 3 freeze-thaw cycles revealed a complex pattern: endogenous AEA was stable in plasma but slightly increased in spiked samples (+12.8%), while endogenous 2-AG concentrations increased by 51% and declined by 24% in spiked samples. A long-term study over 4 weeks at –80°C showed that low endogenous AEA and spiked 2-AG concentrations were stable. However, spiked AEA tended to increase (+19%) and endogenous 2-AG significantly increased by 50% after 2 weeks. Food intake 2 h before blood collection showed no effect on AEA concentrations, whereas 2-AG increased significantly by a factor of 3. <b><i>Conclusions:</i></b> Overall, limited in vitro and/or in vivo/ex vivo chemical stability of endocannabinoids has to be taken into account.
DOI: 10.6084/m9.figshare.6264623
title: Argulus japonicus EDS analysis of sections

description: Weight percentages for trace elements in sections of <i>Argulus japonicus</i> analysed using SEM-EDS
DOI: 10.6084/m9.figshare.6264623.v1
title: Argulus japonicus EDS analysis of sections

description: Weight percentages for trace elements in sections of <i>Argulus japonicus</i> analysed using SEM-EDS
DOI: 10.6084/m9.figshare.6263975
title: V&V Symposium Handout

description: Abstract and supplementary information pertaining to presentation: "An Agile Verification and Validation Process for Generating Regulatory-Grade Evidence".
DOI: 10.6084/m9.figshare.6263975.v1
title: V&V Symposium Handout

description: Abstract and supplementary information pertaining to presentation: "An Agile Verification and Validation Process for Generating Regulatory-Grade Evidence".
DOI: 10.6084/m9.figshare.6263972
title: ASSESSMENT DALAM PENDIDIKAN ANAK USIA DINI

description: Abstrak Pendidikan merupakan suatu upaya untuk memanusiakan manusia. Artinya, melalui proses pendidikan diharapkan terlahir manusia-manusia yang baik. Standar manusia yang “baik” berbeda pada setiap masyarakat, bangsa atau negara. Hal tersebut disebabkan oleh perbedaan pandangan filsafah yang menjadi keyakinannya.Perbedaan filsafah yang dianut dari suatu bangsa akan membawaperbedaan dalam orientasi atau tujuan pendidikannya. Anak usia dini merupakan anak-anak yang berusia 0-8 tahun, di mana pada periode itu jiwa anak-anak masih bersifat utuh bulat atau total dan belum nampak diferensiasi tri sakti manusia yaitu pikiran, rasa dan kemauan. Pada masa kanak-kanak segala dorongan, nafsu diperlukan untuk memenuhi segala keinginannnya. Selain adanya kekuatan yang ada dalam jiwanya, anak-anak juga memiliki pancaindera yang merupakan sumber kekuatan untuk memasukkan alam luar ke dalam jiwanya . Penyusunan perencanaan, pelaksanaan proses, dan penilaian merupakan rangkaian program pendidikan yang utuh, dan merupakan satu kesatuan yang tidak bisa dipisahkan satu dengan yang lainnya. Dalam hal ini, penilaian dapat juga sebagai Assessment. Assessment dibuat untuk mengetahui kemajuan belajar anak dan sebagai teknik pelaporan. Assessment merupakan cara untuk memantau perkembangan belajar anak melalui pemantauan terus menerus dalam berbagai konteks dan berdasarkan apa yang dapat dekerjakan dan dihasilkan. <br>
DOI: 10.6084/m9.figshare.6263972.v2
title: ASSESSMENT DALAM PENDIDIKAN ANAK USIA DINI

description: Abstrak Pendidikan merupakan suatu upaya untuk memanusiakan manusia. Artinya, melalui proses pendidikan diharapkan terlahir manusia-manusia yang baik. Standar manusia yang “baik” berbeda pada setiap masyarakat, bangsa atau negara. Hal tersebut disebabkan oleh perbedaan pandangan filsafah yang menjadi keyakinannya.Perbedaan filsafah yang dianut dari suatu bangsa akan membawaperbedaan dalam orientasi atau tujuan pendidikannya. Anak usia dini merupakan anak-anak yang berusia 0-8 tahun, di mana pada periode itu jiwa anak-anak masih bersifat utuh bulat atau total dan belum nampak diferensiasi tri sakti manusia yaitu pikiran, rasa dan kemauan. Pada masa kanak-kanak segala dorongan, nafsu diperlukan untuk memenuhi segala keinginannnya. Selain adanya kekuatan yang ada dalam jiwanya, anak-anak juga memiliki pancaindera yang merupakan sumber kekuatan untuk memasukkan alam luar ke dalam jiwanya . Penyusunan perencanaan, pelaksanaan proses, dan penilaian merupakan rangkaian program pendidikan yang utuh, dan merupakan satu kesatuan yang tidak bisa dipisahkan satu dengan yang lainnya. Dalam hal ini, penilaian dapat juga sebagai Assessment. Assessment dibuat untuk mengetahui kemajuan belajar anak dan sebagai teknik pelaporan. Assessment merupakan cara untuk memantau perkembangan belajar anak melalui pemantauan terus menerus dalam berbagai konteks dan berdasarkan apa yang dapat dekerjakan dan dihasilkan. <br>
DOI: 10.6084/m9.figshare.6263957
title: Simple Marks Analysis Spreadsheet

description: The Simple Marks Analysis (SMA) spreadsheet allows you to data mine you own class marks in a very simple manner. The results can point to plagiarism, group work of assignments, confused and overworked students, and tutor marking issues. <br>
DOI: 10.6084/m9.figshare.6263957.v1
title: Simple Marks Analysis Spreadsheet

description: The Simple Marks Analysis (SMA) spreadsheet allows you to data mine you own class marks in a very simple manner. The results can point to plagiarism, group work of assignments, confused and overworked students, and tutor marking issues. <br>
DOI: 10.6084/m9.figshare.6263894
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Mine Permit Boundaries (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br>This dataset is a GeoTIFF file of known mine permit boundaries (as issued by various state governments) clipped to the study area. Public mine permit data comes from various state agencies in Kentucky, Tennessee, Virginia, and West Virginia.
DOI: 10.6084/m9.figshare.6263894.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Mine Permit Boundaries (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br>This dataset is a GeoTIFF file of known mine permit boundaries (as issued by various state governments) clipped to the study area. Public mine permit data comes from various state agencies in Kentucky, Tennessee, Virginia, and West Virginia.
DOI: 10.6084/m9.figshare.6263888
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Most Recent Mining Year (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.<br>This specific dataset is a GeoTIFF file depicting when an area was most recently mined, from the period 1985 through 2015. The raster values depict the year that mining was most recently detected by the paper's processing model. A year of "1984" indicates mining that likely was most recently mined at some point prior to 1985. These pre-1985 mining data are derived from a prior study; see https://skytruth.org/wp/wp-content/uploads/2017/03/SkyTruth-MTR-methodology.pdf for more information. This dataset does not indicate for how long an area was a mine or when mining began in a given area.
DOI: 10.6084/m9.figshare.6263888.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Most Recent Mining Year (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.<br>This specific dataset is a GeoTIFF file depicting when an area was most recently mined, from the period 1985 through 2015. The raster values depict the year that mining was most recently detected by the paper's processing model. A year of "1984" indicates mining that likely was most recently mined at some point prior to 1985. These pre-1985 mining data are derived from a prior study; see https://skytruth.org/wp/wp-content/uploads/2017/03/SkyTruth-MTR-methodology.pdf for more information. This dataset does not indicate for how long an area was a mine or when mining began in a given area.
DOI: 10.6084/m9.figshare.6263885
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Yearly Mining Areas (simplified KML)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.<br>This specific dataset is a collection of "simplified" KML files (ideal for visualization but not data analysis) of the mining areas as determined by this study for each year from 1985 through 2015. Individual file names within the dataset indicate the specific year. These files show the mining “footprint” in Appalachia for that given year, indicating that mining was occurring in a given location during that year. These files do not, however, indicate the year at which mining began or ceased in any given location.
DOI: 10.6084/m9.figshare.6263885.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Yearly Mining Areas (simplified KML)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.<br>This specific dataset is a collection of "simplified" KML files (ideal for visualization but not data analysis) of the mining areas as determined by this study for each year from 1985 through 2015. Individual file names within the dataset indicate the specific year. These files show the mining “footprint” in Appalachia for that given year, indicating that mining was occurring in a given location during that year. These files do not, however, indicate the year at which mining began or ceased in any given location.
DOI: 10.6084/m9.figshare.6263882
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — First Mining Year (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.<br>This specific dataset is a GeoTIFF file depicting when an area was first mined, from the period 1985 through 2015. The raster values depict the year that mining was first detected by the paper's processing model. A year of "1984" indicates mining that likely started at some point prior to 1985. These pre-1985 mining data are derived from a prior study; see https://skytruth.org/wp/wp-content/uploads/2017/03/SkyTruth-MTR-methodology.pdf for more information. This dataset does not indicate for how long an area was a mine or when mining ceased in a given area.
DOI: 10.6084/m9.figshare.6263879
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Yearly Mining Areas (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.<br>This specific dataset is a collection of GeoTIFF files of the mining areas as determined by this study for each year from 1985 through 2015. Individual file names within the dataset indicate the specific year. These files show the mining “footprint” in Appalachia for that given year, indicating that mining was occurring in a given location during that year. These files do not, however, indicate the year at which mining began or ceased in any given location.
DOI: 10.6084/m9.figshare.6263879.v1
title: Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Yearly Mining Areas (GeoTIFF)

description: These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.<br>This specific dataset is a collection of GeoTIFF files of the mining areas as determined by this study for each year from 1985 through 2015. Individual file names within the dataset indicate the specific year. These files show the mining “footprint” in Appalachia for that given year, indicating that mining was occurring in a given location during that year. These files do not, however, indicate the year at which mining began or ceased in any given location.
DOI: 10.6084/m9.figshare.6263876
title: C. PIPIENS MERGED ANVIO PROFILES

description: <b>Anvi'o merged profiles for C. pipiens ovary samples.</b><br>Details of the study and a reproducible bioinformatics workflow:<br>http://merenlab.org/data/wolbachia-plasmid/<br><br>All public data files in this collection:<br>https://figshare.com/projects/A_Wolbachia_Plasmid_by_Reveillaud_and_Bordenstein_et_al_2018_/32069
DOI: 10.6084/m9.figshare.6263867
title: RAW C. PIPIENS ASSEMBLY OUTPUT

description: <b>The raw assembly of each individual C. pipiens ovary metagenomes.</b><b><br></b><b>The files contain contigs that are longer than 1 Kbp IDBA_UD assembled from these data.</b><br>Details of the study and a reproducible bioinformatics workflow:<br>http://merenlab.org/data/wolbachia-plasmid/<br><br>All public data files in this collection:<br>https://figshare.com/projects/A_Wolbachia_Plasmid_by_Reveillaud_and_Bordenstein_et_al_2018_/32069
DOI: 10.6084/m9.figshare.6263819.v1
title: Roc Data Table 1~3.zip

description: Data used in this research, results of ROC analysis, table of distinction of Roc analysis<br>SPSS result：Result of ROC analysisROC data: Identification data of each assessmentData 44: Three assessment data of 44 target children
DOI: 10.25377/sussex.6263816
title: Supplementary Material for paper: Who controls the city? A micro-historical case study of the spread of rioting across North London in August 2011

description: <b>Supplementary Material for paper appearing in the Journal "City" Nov 2019</b><br><br>Full triangulated account of the 2011 Enfield riot, including methodology and complete list of sources.<br><b>Abstract from research paper</b>In August 2011, over four days, rioting spread across several cities in England. While dominant explanations of the riots as “mindless criminality” have been widely contested in the academic literature, there is a lack of empirical evidence supporting alternative accounts. The present paper provides a micro-historical analysis of the patterns and sequences of collective behaviour during the spread of the 2011 riots across North London, drawing upon multiple data-sets (archive, interview, video, official report, news coverage). The analysis suggests that initial stages of escalation in the broader proliferation were the result of protagonists deliberately converging from areas of relative deprivation in order to create conflict, but that they did so as a meaningful social identity-based expression of power. We show how over time these motivations and patterns of collective action changed within the riot as a function of intergroup interactions and emergent affordances. On this basis we provide support for the argument that political, social and economic geography were key determining factors underpinning the spread of the 2011 riots. However, we also suggest that an adequate explanation must correspondingly take into account the interplay between social identity, the dynamics of intergroup interaction, and empowerment process that develop during riots themselves.<br><br><br>
DOI: 10.6084/m9.figshare.6263795
title: A spatio-temporal land use and land cover reconstruction for India (1960-2010)

description: Archive file containing the data, input data and scripts required to reproduce the analysis.
DOI: 10.6084/m9.figshare.6263750
title: Helmstedt Professor Catalog

description: This fileset contains the Helmstedt Professor Catalog dump and KB files.