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Data for: A spatial framework for prioritizing biochar application to arable land: a case study for Sweden

The uploaded data is related to the publication: A spatial framework for prioritizing biochar application to arable land: a case study for Sweden, and contains the following: (I) Raster files for three different biochar prioritization narratives. (II) High-resolution biochar use indication maps (in JPEG) for different prioritization narratives.

Statistical data collected in the case studies of the SPOT report - dataset

Statistical data was collected for fifteen case studies in the context of the SPOT project, funded by the European Commission within the framework programme Horizon 2020.

Results of SPOT surveys for tourists, residents and entrepreneurs in the case studies - dataset

This is a dataset of three surveys conducted within the scope of the SPOT project. The purpose of this dataset is to provide the results of the surveys for tourists, residents and entrepreneurs of the fifteen participating case studies.

openmit-licenseSep 2022View details →

Bigger telcos are not necessarily better for infrastructure: A case study of EU versus US markets

This is data and slides for an anticipated forthcoming publication. Telecommunications companies (telcos) provide infrastructure essential to the delivery of digital content. Further, investment in next-generation communication technologies is also seen as critical to overall competitiveness of a market.  This dataset results from an examination of the case to be made for European telco consolidation, through comparison with both telcos in the more-concentrated US market, and with other corporations involved in the information or ``eye-ball'' value chain. We find that both profits and growth for EU and US telcos are already comparable before investment in infrastructure, and that in line with standard theory, more value is returned to customers in the form of infrastructure investment in the less-concentrated, EU market. Profits are also in line with other companies in the value chain, with the notable exception of the extremely-concentrated digital ad exchanges segment.  The data for the charts was collected from Bloomberg, so we therefore have protected the primary datasheet, available on specific request. No discrepancies with information available from other public sources was identified in respect of the data on revenue. However, companies do not report operating profit (EBIT) and EBITDA systematically in the same manner. We based our calculation on the Bloomberg adjusted EBIT and EBITDA. We thank Benedikt Ströbl for comparing the Bloomberg revenue, EBIT and EBITDA figures with other available sources for all companies in the sample. In particular, the data from Bloomberg was compared to data from Alphaquery and 10-K and annual reports. The below is a non-exhaustive list of the data points for which the Bloomberg adjusted data displayed a delta compared to the data that could be collected from the public sources used for verification, where only some years displayed a delta in the data the year is specified in brackets: (i) in respect of EBIT: Publicis (2018, 2019), NYT (2017) and Axel Springer (2017 and 2019); (ii) in respect of EBITDA (additionally to EBIT list): Verizon, Bertelsmann (2018), Interpublic (2019). Finally, to avoid any confusion in respect of the segment data for Alphabet, the data is presented as retrieved from Bloomberg in full on the tab “Alphabet”, data from the SEC reports used on top of the Bloomberg data to estimate the EBITDA is also reproduced on this tab.

dryad40/100

Congruence among multiple indices of habitat preference for species facing human-induced rapid environmental change: A case study using the Brewer's sparrow

Accurate evaluations of habitat preference are key to understanding optimal conditions for wildlife survival and reproduction. Habitat selection, however, usually is evaluated using a single index of preference, and congruence among multiple, relevant indices of preference is examined rarely. We assessed the concordance between patterns of habitat preference using three different indices of breeding site preference in a migratory songbird. Specifically, we compared the chronology of territorial establishment, pair formation, and reproductive initiation of the Brewer's sparrow (Spizella breweri) along a gradient of surface disturbance associated with natural gas development in Wyoming, USA during 2019. We expected all three indices to demonstrate a preference for breeding sites with less surface disturbance, where reproductive success typically is higher. By contrast, all indices suggested suboptimal preference with respect to surface disturbance, with some discrepancy among them. The chronology of settlement and pairing did not vary across the disturbance gradient, whereas nest initiation tended to occur earlier at sites with more disturbance. If the pattern of suboptimal selection of breeding sites that we identified is generalizable across other populations of migratory birds affected by energy development, the resultant lower fitness in those areas may exacerbate population declines. Our results suggest that traditional, single-index approaches to the study of habitat selection, if chosen carefully, may provide adequate inference on habitat preferences. Different metrics, however, can lead to at least subtle differences in patterns of habitat selection. The simultaneous examination of multiple indices of preference across a diversity of systems would help clarify the contexts under which preference metrics can become decoupled.

opencc-zeroSep 2022View details →

Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)

This video is the seventh talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University) Bio: Bryant Lin, MD, MEng is a primary care physician, educator and researcher. The cornerstone of Dr. Lin's work is keeping medicine focused on humans - patients, providers, families and trainees - and not lost in technology and algorithms. His research and educational interests span (1) Developing and testing novel medical technologies, (2) Improving the health of Asian populations with Precision and Population Health, and (3) Increasing expression and interconnections in the Health Community with the Humanities and Arts. After receiving his undergraduate and master's degrees in Electrical Engineering and Computer Science from MIT, he completed his MD and internal Medicine training at Tufts University School of Medicine and Tufts Medical Center. He came to Stanford to serve as a Research Fellow in Cardiac Electrophysiology and Biodesign Fellow where he learned to identify unmet human-centered needs. Since completing his post-graduate training, he stayed at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine where he has invented and researched new medical technologies addressing unmet human-centered needs and started the Consultative Medicine Clinic evaluating patients with medical mysteries. He serves as the Training Director for the Joe and Linda Chlapaty DECIDE Center which has created a novel shared decision making tool for atrial fibrillation anticoagulation and is an investigator in several active clinical trials. Three years ago, he co-founded and currently co-directs, with Dr. Latha Palaniappan, the Center for Asian Health Research and Education (CARE) which aims to improve the health of Asians everywhere. Most recently, he has worked closely with the Medicine and the Muse leadership to help start the Stuck@Home concert series, the Stanford SoundWalk and the COVID Remembrance project. Dr. Lin has an active interest in storytelling and film-making. He co-directs an undergraduate seminar, MED 53Q “Storytelling in Medicine”, with Dr. Lauren Edwards and is working with a group of students on a documentary on end-of-life care at a JapaneseAmerican Senior Home in the Bay Area. Kevin Chang MD, MS, is a primary care physician. His focus in on patient care, population health and quality improvement, and medical education. He received his undergraduate degree and master's degree in biomedical engineering from Duke University and Stanford University respectively, and completed his MD at New York University, followed by his medical training at Stanford University. He has since stayed on at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine, where he also serves as the co-director of the resident physician Internal Medicine clinic. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/ozk1iJYC1yk

Fig. 6 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria

Fig. 6. Abundance (mean number of burrows/100 × 5 m transect) of S. citellus in 4 colonies in the study area in summer (for the period 2017–2021) N = Luda Yana; –– l –– = Belotrup; ---- l ---- = Panagyurski kolonii; u = Beli Manastiri

Fig. 5 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria

Fig. 5. Changes in the habitat suitability in the study area (white – not suitable, black – high suitability) of European souslik assessed by maxent modelling based on data from 2006–2018 (B) and extrapolated for the period 1985–2005 (A). The results are presented in

Fig. 3 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria

Fig. 3. Negative and positive anomalies (white and black bars) of the Mean Annual Temperature time series for the period of 1985–2018 (data from the meteorological station Sofia)

Fig. 2 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria

Fig. 2. Changes in the number of grazing livestock in the southern central Bulgarian planning region for the period 2001–2018

Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.

Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).

Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021.

Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany).

Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3.

opencc-by-4.0Oct 2022View details →

Quantification of recyclability indicators for three case studies

Supplementary data table belonging to the original research publication entitled Early-stage assessment of minor metal recyclability.

opencc-by-sa-4.0Oct 2017View details →

PLOS ONE – a case study of citation analysis of research papers based on the data in an open citation index (The OpenCitations Corpus)

This is a dataset used in and produced by research described in article "PLOS ONE - a case study of citation analysis of research papers based on the data in an open citation index (The OpenCitations Corpus)" that is translation of the original Polish text "PLOS ONE – studium przypadku analizy cytowań prac naukowych na podstawie danych otwartego indeksu cytowań (OpenCitations Corpus)" published by EBiB bulletin (2017, No 176). Data were extracted, as nodes (PLOS_cited_nodes.csv) and edges (PLOS_edges.csv) files from the OpenCitations Corpus (http://opencitations.net/download) on 2017.07.25 and describe all cited papers published by PLOS ONE (nodes), and all citing relations (edges). The research was conducted using Gephi (https://gephi.org/) platform so the same source data are also avaiable as GEXF file (for "one-click" import capabilities). In addition, the same data are published in NET format (but be warned that due to this format limitations, information about the publication year of papers has been lost) used by PAJEK platform, as it is very popular tool for analysis of network data. Published figures have prefix names corresponding to figures captions in the original paper, where they have been thoroughly discussed. This data set contains also the additional figure not published in the article, showing most cited paper with citing chains of articles of lenght not greater than 3. These pictures have much better quality than those published in the article, which allows for "drill down"/zoom-in analysis and large format printing.

opencc-by-4.0Dec 2017View details →

Gamifying Moodle with Badges and Progress Bars: A Case Study

Dataset, questenaire and text analyses of a case study with a mixed methods approach about gamifiying Moodle with badges and progress bars in distance higher education.

opencc-by-4.0May 2024View details →

Basic-ECVs for Case Studies (monthly timeseries)

Monthly timeseries of basic-ecvs (.csv) spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data: <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47</li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</li> </ul> Acknowledgement also to: <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul>