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Summaries of temperature and water table depth prior to peat sampling in Stordalen Mire, 2011-2017
<div> <p>This dataset provides summaries of temperature (T) and water table depth (WTD) conditions prior to the collection of peat samples from Stordalen Mire, Sweden, in July of 2011-2017. These summaries include the following files:</p> <h2><strong>t_wtd_summaries_July2011-2017samplings.csv</strong></h2> </div> <p>This file gives summary statistics over various time intervals for the following environmental measurements:</p> <ul> <li><strong>AirTemperature</strong>: Mean daily air temperature (°C), obtained from automatic sensors at the nearby Abisko Scientific Research Station (ANS) (station ID 188790; the source file [ANS_Daily_Wx_Jul84_Dec17.txt] is not included due to sharing restrictions).</li> <li><strong>WTD</strong>: Water table depths (cm), obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (from Patrick Crill et al.).</li> </ul> <p>The time intervals for these summaries are defined relative to the peat sampling date at each site (see <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>), which varies by site and year. The specific intervals are defined as follows:</p> <ul> <li><strong>7d</strong>: 7 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>14d</strong>: 14 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>21d</strong>: 21 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>28d</strong>: 28 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>growing</strong>: Time from beginning of growing season (defined as June 1) until (and including) the sampling date.</li> <li><strong>all_growing</strong>: Entire growing season (June 1 – Sept. 30).</li> </ul> <p>For clarity, the start and end dates for each time interval (inclusive) are also given under the columns <strong>Start_Date</strong> and <strong>End_Date</strong>, where End_Date=<strong>Sampling_Date</strong> for all intervals except all_growing.</p> <p>Summary statistics for each interval include: measurement count (<strong>n</strong>), median (<strong>median</strong>), mean (<strong>mean</strong>), and standard deviation (<strong>sd</strong>), and are given under the column names beginning with these statistic labels.</p> <p><em>IMPORTANT NOTE: </em>For temperature, these statistics are calculated based on the average temperature measured on each day, meaning that<strong> </strong><em>the standard deviations do NOT account for within-day temperature variation.</em> To provide short-term (1 day) temperature variation context for each sampling date, the within-day mean, minimum, and maximum air temperatures for the sampling date only (taken directly from the corresponding row & columns in the source ANS data file) are provided in the columns <strong>samplingdate_mean_AirTemperature</strong>, <strong>samplingdate_min_AirTemperature</strong>, and <strong>samplingdate_max_AirTemperature</strong>.</p> <div> <div> <h2><strong>wtd_summaries_July2011-2017samples.csv</strong></h2> </div> <p>This file gives the percentage of time that each peat sample's depth midpoint (<strong>DepthAvg__</strong>) was at or below the water table depth (WTD), over each of the longer time intervals (≥21 days) defined above for the temperature & WTD summaries. (Intervals <21 days are not included due to the lower frequency of WTD measurements, which results in low <em>n</em> for shorter intervals.)</p> <p>The first few columns are taken directly from the <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>, for the samples collected in July of 2011-2017 from the MainAutochamber sites. The last set of columns include the following, with the time interval labels (defined as in the above temperature summaries) appended at the end of each column name:</p> <ul> <li><strong>n_WTD_*</strong>: Number of WTD measurements used in the calculation.</li> <li><strong>pct_time_below_WTD_*</strong>: Fraction (relative to 1) of measured WTDs over the given time interval that were at or above the DepthAvg__ for each sample, which equates to the fraction of measurement timepoints during which the given sample was at or below the WTD. This is the same method used for calculating "% Time below water table" in Figure 6 of <a href="https://doi.org/10.1038/s41396-018-0065-5">Singleton et al. (2018)</a>. For palsa sites, this value is automatically set to 0 based on the lack of a water table at all timepoints in the analysis.)</li> </ul> <p>As above, the WTD values used for these calculations were obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (Patrick Crill et al.).</p> <h1>Funding acknowledgments</h1> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>This research was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p> <p>The temperature summary has been made possible by data provided by Abisko Scientific Research Station and the Swedish Infrastructure for Ecosystem Science (SITES).</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> </div>
Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report
<p>This is a comprehensive data repository of the <em>data management survey</em> carried out in Autumn of 2023 through a collaboration between the <a href="https://opensciencestudies.eu/">PHIL_OS</a> project and the <a href="https://agres.elgo.gr/">Research Directorate of the Hellenic Agricultural Organization ELGO-DIMITRA</a>.</p> <p>Please cite as: </p> <blockquote> <p>Tsiroukis F., Leonelli S. and ELGO-DIMITRA (2024) <em>Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report.</em> PHIL_OS Report. DOI: 10.5281/zenodo.14003418</p> </blockquote>
Classification of web-based Digital Humanities projects leveraging information visualisation techniques
<h2>Description</h2> <p>This dataset contains a list of 186 Digital Humanities projects leveraging information visualisation methods. Each project has been classified according to visualisation and interaction techniques, narrativity and narrative solutions, domain, methods for the representation of uncertainty and interpretation, and the employment of critical and custom approaches to visually represent humanities data.</p> <p> </p> <h2>Classification schema: categories and columns</h2> <p>The <code>project_id</code> column contains unique internal identifiers assigned to each project. Meanwhile, the <code>last_access</code> column records the most recent date (in DD/MM/YYYY format) on which each project was reviewed based on the web address specified in the <code>url</code> column.<br>The remaining columns can be grouped into descriptive categories aimed at characterising projects according to different aspects:</p> <p> </p> <p><strong>Narrativity.</strong> It reports the presence of information visualisation techniques employed within narrative structures. Here, the term narrative encompasses both author-driven linear data stories and more user-directed experiences where the narrative sequence is determined by user exploration [1]. We define 2 columns to identify projects using visualisation techniques in narrative, or non-narrative sections. Both conditions can be true for projects employing visualisations in both contexts. Columns:</p> <ul> <li> <p><code>non_narrative</code> (boolean)</p> </li> <li> <p><code>narrative</code> (boolean)</p> </li> </ul> <p> </p> <p><strong>Domain.</strong> The humanities domain to which the project is related. We rely on [2] and the chapters of the first part of [3] to abstract a set of general domains. Column:</p> <ul> <li> <p><code>domain</code> (categorical):</p> </li> <ul> <li> <p>History and archaeology</p> </li> <li> <p>Art and art history</p> </li> <li> <p>Language and literature</p> </li> <li> <p>Music and musicology</p> </li> <li> <p>Multimedia and performing arts</p> </li> <li> <p>Philosophy and religion</p> </li> <li> <p>Other: both extra-list domains and cases of collections without a unique or specific thematic focus.</p> </li> </ul> </ul> <p> </p> <p><strong>Visualisation of uncertainty and interpretation.</strong> Buiding upon the frameworks proposed by [4] and [5], a set of categories was identified, highlighting a distinction between precise and impressional communication of uncertainty. Precise methods explicitly represent quantifiable uncertainty such as missing, unknown, or uncertain data, precisely locating and categorising it using visual variables and positioning. Two sub-categories are interactive distinction, when uncertain data is not visually distinguishable from the rest of the data but can be dynamically isolated or included/excluded categorically through interaction techniques (usually filters); and visual distinction, when uncertainty visually “emerges” from the representation by means of dedicated glyphs and spatial or visual cues and variables. On the other hand, impressional methods communicate the constructed and situated nature of data [6], exposing the interpretative layer of the visualisation and indicating more abstract and unquantifiable uncertainty using graphical aids or interpretative metrics. Two sub-categories are: ambiguation, when the use of graphical expedients—like permeable glyph boundaries or broken lines—visually convey the ambiguity of a phenomenon; and interpretative metrics, when expressive, non-scientific, or non-punctual metrics are used to build a visualisation. Column:</p> <ul> <li> <p><code>uncertainty_interpretation</code> (categorical):</p> </li> <ul> <li> <p>Interactive distinction</p> </li> <li> <p>Visual distinction</p> </li> <li> <p>Ambiguation</p> </li> <li> <p>Interpretative metrics</p> </li> </ul> </ul> <p> </p> <p><strong>Critical adaptation.</strong> We identify projects in which, with regards to at least a visualisation, the following criteria are fulfilled: 1) avoid repurposing of prepackaged, generic-use, or ready-made solutions; 2) being tailored and unique to reflect the peculiarities of the phenomena at hand; 3) avoid simplifications to embrace and depict complexity, promoting time-consuming visualisation-based inquiry. Column:</p> <ul> <li> <p><code>critical_adaptation</code> (boolean)</p> </li> </ul> <p> </p> <p><strong>Non-temporal visualisation techniques.</strong> We adopt and partially adapt the terminology and definitions from [7]. A column is defined for each type of visualisation and accounts for its presence within a project, also including stacked layouts and more complex variations. Columns and inclusion criteria:</p> <ul> <li> <p><code>plot</code> (boolean): visual representations that map data points onto a two-dimensional coordinate system.</p> </li> <li> <p><code>cluster_or_set</code> (boolean): sets or cluster-based visualisations used to unveil possible inter-object similarities.</p> </li> <li> <p><code>map</code> (boolean): geographical maps used to show spatial insights. While we do not specify the variants of maps (e.g., pin maps, dot density maps, flow maps, etc.), we make an exception for maps where each data point is represented by another visualisation (e.g., a map where each data point is a pie chart) by accounting for the presence of both in their respective columns.</p> </li> <li> <p><code>network</code> (boolean): visual representations highlighting relational aspects through nodes connected by links or edges.</p> </li> <li> <p><code>hierarchical_diagram</code> (boolean): tree-like structures such as tree diagrams, radial trees, but also dendrograms. They differ from networks for their strictly hierarchical structure and absence of closed connection loops.</p> </li> <li> <p><code>treemap</code> (boolean): still hierarchical, but highlighting quantities expressed by means of area size. It also includes circle packing variants.</p> </li> <li> <p><code>word_cloud</code> (boolean): clouds of words, where each instance’s size is proportional to its frequency in a related context</p> </li> <li> <p><code>bars</code> (boolean): includes bar charts, histograms, and variants. It coincides with “bar charts” in [7] but with a more generic term to refer to all bar-based visualisations.</p> </li> <li> <p><code>line_chart</code> (boolean): the display of information as sequential data points connected by straight-line segments.</p> </li> <li> <p><code>area_chart</code> (boolean): similar to a line chart but with a filled area below the segments. It also includes density plots.</p> </li> <li> <p><code>pie_chart</code> (boolean): circular graphs divided into slices which can also use multi-level solutions.</p> </li> <li> <p><code>plot_3d</code> (boolean): plots that use a third dimension to encode an additional variable.</p> </li> <li> <p><code>proportional_area</code> (boolean): representations used to compare values through area size. Typically, using circle- or square-like shapes.</p> </li> <li> <p><code>other</code> (boolean): it includes all other types of non-temporal visualisations that do not fall into the aforementioned categories.</p> </li> </ul> <p> </p> <p><strong>Temporal visualisations and encodings.</strong> In addition to non-temporal visualisations, a group of techniques to encode temporality is considered in order to enable comparisons with [7]. Columns:</p> <ul> <li> <p><code>timeline</code> (boolean): the display of a list of data points or spans in chronological order. They include timelines working either with a scale or simply displaying events in sequence. As in [7], we also include structured solutions resembling Gantt chart layouts.</p> </li> </ul> <ul> <li> <p><code>temporal_dimension</code> (boolean): to report when time is mapped to any dimension of a visualisation, with the exclusion of timelines. We use the term “dimension” and not “axis” as in [7] as more appropriate for radial layouts or more complex representational choices.</p> </li> <li> <p><code>animation</code> (boolean): temporality is perceived through an animation changing the visualisation according to time flow.</p> </li> <li> <p><code>visual_variable</code> (boolean): another visual encoding strategy is used to represent any temporality-related variable (e.g., colour).</p> </li> </ul> <p> </p> <p><strong>Interaction techniques.</strong> A set of categories to assess affordable interaction techniques based on the concept of user intent [8] and user-allowed data actions [9]. The following categories roughly match the “processing”, “mapping”, and “presentation” actions from [9] and the manipulative subset of methods of the “how” an interaction is performed in the conception of [10]. Only interactions that affect the visual representation or the aspect of data points, symbols, and glyphs are taken into consideration. Columns:</p> <ul> <li> <p><code>basic_selection</code> (boolean): the demarcation of an element either for the duration of the interaction or more permanently until the occurrence of another selection.</p> </li> <li> <p><code>advanced_selection</code> (boolean): the demarcation involves both the selected element and connected elements within the visualisation or leads to brush and link effects across views. Basic selection is tacitly implied.</p> </li> <li> <p><code>navigation</code> (boolean): interactions that allow moving, zooming, panning, rotating, and scrolling the view but only when applied to the visualisation and not to the web page. It also includes “drill” interactions (to navigate through different levels or portions of data detail, often generating a new view that replaces or accompanies the original) and “expand” interactions generating new perspectives on data by expanding and collapsing nodes.</p> </li> <li> <p><code>arrangement</code> (boolean): methods to organise visualisation elements (symbols, glyphs, etc.) or multi-visualisation layouts spatially through drag and drop or according to a criterion via more automatic triggers.</p> </li> <li> <p><code>change</code> (boolean): visual encoding alterations involving different aspects of visualisation as a whole: the same content is presented with another visualisation technique; the change involves symbols or glyphs aspect (colour, size, shape, etc.); the visualisation type is unaltered, but the layout variant changes (e.g., to stacked layouts); or other changes like axes inversion and scale modifications. The presence of all the visualisation techniques involved in a change is reported.</p> </li> <li> <p><code>visualisation_filter</code> (boolean): filters to exclude or include visualisation elements with respect to defined criteria, without reloading or generating a new visualisation. Unlike options triggering the fetch of new data to alter the visualisation content, filters seamlessly operate on existing visual elements.</p> </li> <li> <p><code>collection_filter</code> (boolean): the interaction with visualised elements acts as a filter for a related collection or list of items (e.g., clicking a region on a map filters a list of items according to spatial metadata).</p> </li> <li> <p><code>aggregation</code> (boolean): changes to the granularity of visual elements according to a variable. It produces either visual data summarisations or segregations.</p> </li> <li> <p><code>btfw_interaction</code> (boolean): to identify the use of “breaking the fourth wall interactions” as defined [11]. It applies only to narratives.</p> </li> </ul> <p> </p> <p><strong>Narrative flow factors.</strong> Other categories aim to identify patterns in the design of narrative solutions. It is worth noticing that a project with multiple and diverse narratives can potentially report multiple design choices for the same column. Part of the factors and definitions from [12] are here re-used and adapted.</p> <p><em>Story layout </em>columns define the layout, or genre, of the narrative format:</p> <ul> <li> <p><code>document_layout</code> (boolean)</p> </li> <li> <p><code>slideshow_layout</code> (boolean)</p> </li> <li> <p><code>hybrid_layout</code> (boolean): mixing document and slideshow layouts.</p> </li> <li> <p><code>other_layout</code> (boolean): more complex solutions.</p> </li> </ul> <p><em>Role of visualisation</em> columns describe the role visualisations detain with respect to the entire story, in particular, with reference to the textual part of the narratives:</p> <ul> <li><code>equal_role</code> (boolean): visualisations and text play an equal role in the narrative.</li> <li><code>figure_role</code> (boolean): visualisations are supporting elements compared to the role of text.</li> <li><code>annotated_role</code> (boolean): visualisations are the drivers of the narrative.</li> </ul> <p><em>Story progression</em> columns categorise the shape of possible story paths:</p> <ul> <li> <p><code>linear_progression</code> (categorical): strongly author-driven or user-directed narrative. Possible values specify the potential to skip certain parts while not having a fully explorative experience:</p> </li> <ul> <li> <p>Skip</p> </li> <li> <p>No-skip</p> </li> </ul> <li> <p><code>user_directed</code> (bool): users can select a path among multiple alternatives and compose narrative pieces, providing a broder degree of interaction and exploration possibilities [1]. If a linear path can be suggested, here it remains merely one option among many others. Differently from a linear-skip approach, it has a low level of guidance oriented towards linear navigation.</p> </li> </ul> <p><em>Navigation input </em>columns define the ways users can move through the narrative:</p> <ul> <li> <p><code>button_input</code> (boolean)</p> </li> <li> <p><code>scroll_input</code> (boolean)</p> </li> <li> <p><code>slider_input</code> (boolean)</p> </li> </ul> <p><em>Navigation progress </em>columns describe methods through which the reader perceives its placement within the narrative:</p> <ul> <li> <p><code>text_progression</code> (boolean): text or numbers act as signifiers for user position.</p> </li> <li> <p><code>dots_progression</code> (boolean)</p> </li> <li> <p><code>visualisation_progression</code> (boolean): the visualisation used in the narrative, or a visualised progress widget acts as a signifier for user position.</p> </li> </ul> <p><em>Level of control </em>columns describe how much control a reader has over the text, visualisations, and animated transitions. Control could be discrete (D) when it triggers the motion, continuous (C) when it can act throughout all the keyframes, or hybrid (H) if it supports aspects of both. When animation is absent, control can be not available (NA). In particular, while visualisation control is related to the visualisation as a whole (e.g., the entire scatter plot moving up or down the page), the animated transition is related to more specific, data-relevant motion.<br>Columns:</p> <ul> <li> <p><code>text_control</code> (categorical):</p> </li> <ul> <li> <p>D</p> </li> <li> <p>C</p> </li> <li> <p>H</p> </li> </ul> <li> <p><code>visualisation_control</code> (categorical):</p> </li> <ul> <li> <p>D</p> </li> <li> <p>C</p> </li> <li> <p>H</p> </li> </ul> <li> <p><code>animation_control</code> (categorical):</p> </li> <ul> <li> <p>D</p> </li> <li> <p>C</p> </li> <li> <p>H</p> </li> <li> <p>NA</p> </li> </ul> </ul> <p> </p> <h2>References</h2> <p>[1] E. Segel and J. Heer, “Narrative Visualization: Telling Stories with Data,” IEEE Trans. Visual. Comput. Graphics, vol. 16, no. 6, pp. 1139–1148, 2010, doi: 10.1109/TVCG.2010.179.</p> <p>[2] M. Terras, J. Nyhan, and E. Vanhoutte, Defining Digital Humanities: A Reader. Routledge, 2016.</p> <p>[3] S. Schreibman, R. G. Siemens, and J. Unsworth, Eds., A companion to digital humanities. in Blackwell companions to literature and culture, no. 26. Malden, MA: Blackwell Pub, 2004.</p> <p>[4] C. Kinkeldey, A. M. MacEachren, and J. Schiewe, “How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies,” The Cartographic Journal, vol. 51, no. 4, pp. 372–386, 2014, doi: 10.1179/1743277414Y.0000000099.</p> <p>[5] G. Panagiotidou, H. Lamqaddam, J. Poblome, K. Brosens, K. Verbert, and A. Vande Moere, “Communicating Uncertainty in Digital Humanities Visualization Research,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 635–645, Jan. 2023, doi: 10.1109/TVCG.2022.3209436.</p> <p>[6] J. Drucker, “Humanities Approaches to Graphical Display,” Digital Humanities Quarterly, vol. 5, no. 1, 2011, Accessed: Sep. 17, 2024. [Online]. Available: <a href="https://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html">https://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html</a></p> <p>[7] F. Windhager et al., “Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges,” IEEE Trans. Visual. Comput. Graphics, vol. 25, no. 6, pp. 2311–2330, Jun. 2019, doi: 10.1109/TVCG.2018.2830759.</p> <p>[8] J. S. Yi, Y. A. Kang, J. Stasko, and J. A. Jacko, “Toward a Deeper Understanding of the Role of Interaction in Information Visualization,” IEEE Trans. Visual. Comput. Graphics, vol. 13, no. 6, pp. 1224–1231, 2007, doi: 10.1109/TVCG.2007.70515.</p> <p>[9] E. Dimara and C. Perin, “What is Interaction for Data Visualization?,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 119–129, Jan. 2020, doi: 10.1109/TVCG.2019.2934283.</p> <p>[10] M. Brehmer and T. Munzner, “A Multi-Level Typology of Abstract Visualization Tasks,” IEEE Trans. Visual. Comput. Graphics, vol. 19, no. 12, pp. 2376–2385, 2013, doi: 10.1109/TVCG.2013.124.</p> <p>[11] Y. Shi, T. Gao, X. Jiao, and N. Cao, “Breaking the Fourth Wall of Data Stories Through Interaction,” IEEE Trans. Visual. Comput. Graphics, pp. 1–11, 2022, doi: 10.1109/TVCG.2022.3209409.</p> <p>[12] S. McKenna, N. Henry Riche, B. Lee, J. Boy, and M. Meyer, “Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences,” Computer Graphics Forum, vol. 36, no. 3, pp. 377–387, 2017, doi: 10.1111/cgf.13195.</p> <p> </p> <h2>Fundings</h2> <p>Project funded by the European Union – NextGenerationEU under the National Recovery and Resilience Plan (NRRP), Investment I.4.1 - Borse PNRR Patrimonio Culturale.</p>
Dataset for "An Alternative Chlorine-Assisted Optimization of CdS/Sb2Se3 Solar Cells: Towards Understanding of Chlorine Incorporation Mechanism"
<p>The current strategies in the development of Sb2Se3 thin film solar cells involve fabrication and optimization of<br>superstrate and substrate device architectures, with the preferable choice for TiO2 and CdS heterojunction layers.<br>For CdS-based superstrate cells, several studies reported the necessity to apply CdCl2 or other metal halide-based<br>post-deposition treatment (PDT), highlighting improvement of CdS/Sb2Se3 device efficiency. However, the need,<br>effect, and mechanism of such PDT are very often not described. Additionally, the fact that many groups have not<br>succeeded in demonstrating its benefits suggests that this strategy is not straightforward, requiring a deeper<br>understanding towards a more unified concept. The present study proposes an alternative approach to the<br>challenging CdCl2 PDT of CdS in CdS/Sb2Se3 device, involving controllable Cl incorporation in CdS films by<br>systematically varying the concentration of NH4Cl in the CBD precursor solution from 1 to 8 mM. Structural and<br>electrical characterizations are correlated with advanced measurements of Scanning Kelvin Probe, surface<br>photovoltage, and atomic force microscopy to understand the impact of Cl incorporation on the properties of CdS<br>films and CdS/Sb2Se3 devices. The validity of Cl incorporation in the CdS lattice and interdiffusion processes at<br>the CdS-Sb2Se3 interface is confirmed by secondary ion mass spectrometry analysis. It is demonstrated that<br>incorporation of 1 mM of NH4Cl, as a Cl source in CBD CdS, can boost the PCE of CdS/Sb2Se3 by ~20 %. With this<br>approach, we offer new perspectives on the optimization methodology for Cl-based CdS/Sb2Se3 device processing<br>and complementary understanding of the physiochemistry behind these processes.</p>
Dataset: The Role of News Consumption on Influencers' Facebook Pages in Threat Perception and Political Conservatism During Times of COVID-19: A Comparative Study between the USA, Spain, and Egypt
<p>Este archivo ofrece los datos en bruto de una encuesta examina el impacto del consumo de noticias en las páginas de Facebook de los influencers en la motivación del conservadurismo político durante amenazas como el terrorismo o las pandemias. Muestra: N=1309, jóvenes de entre 18 y 35 años en Estados Unidos, España y Egipto. Trabajo de campo realizado entre el 10 de agosto de 2021 y el 5 de septiembre de 2021.</p> <p><span>Dataset correspondiente al proyecto El rol de la ciudadanía en la comunicación política digital CI-COMPOL (PID2020-119492GB-I00) financiado por MCIN/AEI/10.13039/501100011033/. IP: Andreu Casero-Ripollés, Departamento de Ciencias de la Comunicación, Universitat Jaume I de Castellón</span></p>
Dataset: Percepciones de la ciudadanía sobre el uso político de los servicios móviles de mensajería instantánea y sus efectos sobre la democracia
<p>Los datos en bruto procedentes de esta encuesta se refieren al contexto de España. Su objetivo es conocer las opiniones y actitudes de la ciudadanía en relación con dos aspectos: a) percepción de los servicios móviles de mensajería instantánea como herramientas de participación política; y b) percepción de los efectos que estas plataformas tienen sobre la democracia. La muestra total es de 1106 ciudadanos residentes en España. El trabajo de campo se realizó en mayo de 2023 mediante un panel online administrado por Qualtrics.</p> <p><span>Dataset correspondiente al proyecto El rol de la ciudadanía en la comunicación política digital CI-COMPOL (PID2020-119492GB-I00) financiado por MCIN/AEI/10.13039/501100011033/. IP: Andreu Casero-Ripollés, Departamento de Ciencias de la Comunicación</span></p>
Dataset for: Associating Mechano-electrochemical Phenomena to Stochastic Current Events in Micro-Electrochemical Cells Containing TiNb2O7 Particles
<p>This is the raw data used in a manuscript that will be submitted to ChemElectroChem. If you have any questions, please email the creators.</p>
Scripts for Patton et al 2020; Science DOI: 10.1126/science.abb9772
<p>Scripts used for all data anaysis for Patton et al. 2020 <em>Science </em>3<span>70: eabb9772. </span><span>DOI: 10.1126/science.abb9772</span></p>
Survey Data on Current Open Access Terms and Future Trends (2024)
<p><strong>Description:</strong><br>This dataset contains the analysis, codebook, and raw survey data from the 2024 survey <em>"Open Access – Current Terms and Future Areas of Focus"</em>. The survey aimed to gather perspectives from Open Access experts in the German-speaking region, focusing on the evaluation of current Open Access terminology, concepts, and emerging trends.</p> <p>The survey highlights how Open Access terminology has evolved over the past two decades and explores current perceptions regarding key terms in the Open Access discourse, as well as the anticipated future developments in this field. A total of 131 complete responses (<em>N=131</em>) were collected, providing valuable insights into the views of professionals working in Open Access publishing, information infrastructures, and scientific publishing houses.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>codebook_oa_2024_2024-11-21.xlsx</strong>: The codebook, including detailed explanations of the variables, codes, and definitions used in the survey.</li> <li><strong>survey_results_oa_2024_2024-11-21.xlsx</strong>: Anonymized raw data from the survey, including both quantitative and qualitative responses from the participants.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: CSV file containing the key terms and concepts identified by participants in response to the question on Open Access terminology.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: An additional CSV file with detailed classification and analysis of the terms related to Open Access, including their frequency and significance based on participant responses.</li> </ol> <p><strong>Methodology:</strong><br>The survey was conducted via an online questionnaire distributed from September 7 to October 15, 2024, to professionals working in Open Access, both within information infrastructures (e.g., libraries) and in academic publishing houses. The survey gathered both qualitative and quantitative data, focusing on how Open Access terminology is understood and its future developments. The data were cleaned, anonymized, and analyzed using appropriate statistical and content analysis methods.</p> <p><strong>Purpose and Use:</strong><br>This dataset is valuable for researchers and professionals studying Open Access terminology, trends, and future developments. It provides insights into the current understanding of Open Access within the academic community and can be used for comparative studies, policy analysis, and future Open Access research.</p>
Pawpaws prevent predictability: A locally-dominant tree alters understory beta-diversity and community assembly
<p>Data used in "Pawpaws Prevent Predictability: A locally-dominant tree alters understory beta-diversity and community assembly" (Wassel and Myers) accepted for publication in Ecosphere.<br><br><strong>Metadata for Zenodo.pdf </strong>contains more information on the following data files including descriptions of the columns. </p> <p>The file <strong>understory_abundance_data2021.csv</strong> contains all species abundances in 1x1m plots. This data was used for analyses in publication. Each row is a plot, each column is a speceis or plot descriptor, values for columns 5 and higher are species abundances. Data was collected July-August 2021 by Anna Wassel in Missouri, USA. </p> <p>The file <strong>understory_species_list2021.csv </strong>contains a list of the species codes used in the first file with their scientific names and their status as herbs or woody. This was used to filter out herbaceous species from the data set for herbaceous-only analyses. </p> <p> </p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
A Decade of Progress: Open Data Practices in Bioscience at the University of Edinburgh
<p><strong>General Information:</strong></p> <p>This reposotory contains the outcomes of a project executed at the Biosciences Institutes of the University of Edinburgh. This research project assesses the openness and FAIRness (Findable, Accessible, Interoperable, and Reusable) of data linked to publications from these institutes. Here, you will find datasets, analytical codes, and figures that detail our project’s methodology and results aiming to enhance data-sharing practices and promote the adherence to FAIR principles within and beyond our community. </p> <p>This repository is linked to a publication that has been submitted to: Proceedings of the Royal Sociaty B - Biological Sciences</p> <p>The main project: You can find the main repository and workspace of this project on Github containing the data and code of this project and all the previous related projects: <a href="https://github.com/BioRDM/InsightsOfOpenPracticesInBiosciences">Here</a></p> <p><strong>The Protocol:</strong></p> <p>The protocol for this project can be found on Protocol.io, where detailed step-by-step guidelines are provided to ensure that the research methods are transparent and reproducible. <a href="https://www.protocols.io/view/a-protocol-for-assessing-open-data-practices-honou-kxygxyxmdl8j/v2" rel="nofollow">https://www.protocols.io/view/a-protocol-for-assessing-open-data-practices-honou-kxygxyxmdl8j/v2</a></p> <p>The main project</p> <p><strong>Contact us:</strong></p> <p>for General Queries, Collaboration and Data Management: <em>bio_rdm@ed.ac.uk (<a href="https://biology.ed.ac.uk/research/facilities/research-data-management">BioRDM</a>) </em>or the Principal Investigator and Corresponding Author: Andrew Millar (<em>andrew.millar@ed.ac.uk</em>) - Orcid: 0000-0003-1756-3654</p> <p><strong>Data Collection</strong></p> <p>The Dataset of this project was collected in two different periods by the honour students (Creasey, de Ugarte, Strevens, Usman, Yun Wong) in our department:<br>- Project one from Januray 2023 to June 2023<br>- Project two from January 2024 to June 2024</p>
Raw data mzXML and MATLAB code for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea
<p>MATLAB code and raw data mzXML for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea.</p> <p>Seawater samples were collected during three distinct periods: early winter (December 2019), late winter (March 2021), and spring (May 2021). The sampling transect extended from the northern Barents Sea into the Nansen Basin (76°N – 83°N) as part of <em>The Nansen Legacy</em> project (Research Council of Norway, RCN #276730). The molecular composition of dissolved organic matter (DOM) was analyzed using an Orbitrap mass spectrometer.</p>
RoHuCAD: Robots and Humans Collaborative Anomaly Detection
<h1>RoHuCAD: Robots and Humans Collaborative Anomaly Detection</h1> <p>RoHuCAD is a dataset of human-robot collaboration in a robotic workshop (check <code>workshop_layout.png</code>). Two robots (collaborative manipulator - cobot, autonomous mobile robot - AMR) assist three human operators in assembly of electronic devices.</p> <p>There are two 8-min long recordings in the dataset. They mostly follow the same scenario, with slightly different anomalies. The data is in ROS Noetic rosbag format.</p> <h2>Included data </h2> <ul> <li>RGBD camera data (color + depth) <ul> <li>3 cameras: <a href="https://www.intelrealsense.com/depth-camera-d435i/">Intel Realsense D435i</a></li> <li>color and depth data at 6 frames per second</li> <li>Intrinsic calibration data</li> <li>Extrinsic calibration data (positions and orientations)</li> </ul> </li> <li>Information about positions of robots <ul> <li>AMR: <a href="https://www.ez-wheel.com/en/development-kit-for-agv-and-amr">Ez-Wheel SWD® Starter Kit</a></li> <li>Cobot: <a href="https://www.universal-robots.com/products/ur10-robot/">Universal Robots UR10e</a></li> </ul> </li> </ul> <h2>Annotations</h2> <p>Annotations of specific anomalies are included (CSV file with columns: event_id, tstart, tend, event_type, person_id, camera_id)</p> <ul> <li>Gestures / poses <ul> <li>BENT</li> <li>T-POSE (hands horizontally to the sides)</li> <li>L+R-UP (both hands up)</li> <li>RH-UP (right hand up)</li> <li>LH-UP (left hand up)</li> <li>SQUAT</li> <li>HI-POSE (waving)</li> </ul> </li> <li>Unsafe behaviour <ul> <li>Human in robot working area</li> <li>Standing back to (moving) robot</li> <li>Looking at phone</li> <li>Human in the way of AMR</li> </ul> </li> <li>Normal activities <ul> <li>Assembling/Working</li> <li>Loading/unloading AMR</li> </ul> </li> </ul> <h2>ROS topics</h2> <ul> <li><code>/tf </code></li> <li><code>/tf_static</code></li> <li><code>/joint_states</code></li> <li>cam_ws2_box <ul> <li><code>/cam_ws2_box/color/camera_info</code></li> <li><code>/cam_ws2_box/color/image_raw/compressed</code></li> <li><code>/cam_ws2_box/depth_registered/camera_info</code></li> <li><code>/cam_ws2_box/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta2_ws2 <ul> <li><code>/cam_ta2_ws2/color/camera_info</code></li> <li><code>/cam_ta2_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta2_ws2/depth_registered/camera_info</code></li> <li><code>/cam_ta2_ws2/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta1_ws2 <ul> <li><code>/cam_ta1_ws2/color/camera_info</code></li> <li><code>/cam_ta1_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/camera_info</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/image_raw</code></li> </ul> </li> </ul> <h2>Acknowledgement</h2> <p>The work leading to these results has received funding from the European Union’s Horizon Europe research and innovation programme within the ULTIMATE project under the Grant Agreement no 101070162.</p>
Pan‐Arctic Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw
<p>The datasets are issued from the combination of records of the ESA EO4PAC and Permafrost_cci and HORIZON 2020 Nunataryuk projects. The EO4PAC project aimed to develop a new generation of geospatial products for the observation of permafrost and associated changes from space with a special focus on the coastal Arctic. Four components were considered in the creation of the datasets:</p> <p>(1) Landsat-7/8 for the detection of coastline changes over the 2000-2020 period (Tanguy et al., 2024).</p> <p>(2) Sentinel-1/2 for the detection and mapping of coastal infrastructures (Bartsch et al. 2024), updating Wang et al. (2021).</p> <p>(3) Permafrost_cci timeseries for retrieval of trends of ground temperature and active layer thickness for the 2000-2020 period (Obu et al. 2021a,b), evaluated based on Martin et al (2023) and CALM et al. (2024).</p> <p>(4) Sea level rise by 2100 (Garner et al. 2022).</p> <p>The respective output provides a consistent mapping of settlements along arctic and permafrost-dominated coasts (2), and associated coastline and permafrost conditions changes during the last 20 years (1, 3). Combined together, an assessment of Arctic infrastructures at risk due to permafrost change (GT, ALT) and coastline erosion was possible, the latter with projections for the years 2030, 2050 and 2100.<a name="_heading=h.jkogrw14ymt"></a></p> <p>References</p> <p>Bartsch, Annett, Pointner, Georg, & Nitze, Ingmar. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (Version 2) [Data set]. Zenodo. https://zenodo.org/records/10160636.</p> <p>CALM, GTN-P, Wieczorek, M., Heim, B., Streletskiy, D., Bartsch, A., 2024, GTN-P CALM: 34 years of Active Layer Thickness (ALT) across latitudinal and elevational gradients in the Northern Hemisphere [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.972777</p> <p>Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2022). IPCC AR6 sea level projections [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6382554">https://doi.org/10.5281/zenodo.6382554</a></p> <p>Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Rader, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.956039, In: Boike, Julia; Hammar, Jennika; Goldau, Maybrit; Miesner, Frederieke; Anselm, Norbert (2024): Circumarctic seasonal measurements of permafrost parameters (thaw depth, snow depth, vegetation and tree height, water level and soil properties) [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.971787</p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A., Vieira, G., Whalen, D., Heim, B., Wieszorek, M., Grosse, G.: Pan‐Arctic Assessment of Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw, Earth’s Future, 10.1029/2024EF005013.</p> <p>Wang, S., Ramage, J., Bartsch, A., & Efimova, A. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4529610" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.4529610</a></p>
13/1 Sferamundi di Grecia. Prima parte - Progetto Mambrino Digital Library
<p>Dataset of the digital scholarly edition of the Italian book of chivalry <em>13/1 Sferamundi di Grecia. Prima parte</em>.</p> <p>It contains:</p> <ul> <li>transcription and commentary XML-TEI files (source.xml and commentary.xml)</li> <li>the eBook (in multiple formats)</li> <li>plain text file for computational anaysis</li> </ul> <p>The edition is part of the Progetto Mambrino Digital Library and has been developed within the PRIN 2017 Mapping Chivalry (Prot. 2017JA5XAR), in the context of the Project of Excellence "Inclusive Humanities" (2023-2027) of the Department of Foreign Languages and Literatures of the University of Verona.</p>
Rural population count at 1 km for 2000-2020 based on WorldPop and GHS-SMOD urbanization level
<p>Rural population count at 1 km grid in EPSG:4326 for 2000-2020 (annual). This is only an estimate of the rural population. This probably misses many rural areas, especially in the tropics. The maps were derived using two data sources:</p> <ol> <li><a href="https://hub.worldpop.org/geodata/listing?id=64">WorldPop population counts at 1 km</a>;</li> <li><a href="https://human-settlement.emergency.copernicus.eu/download.php?ds=smod">GHS-SMOD urbanization levels at 1 km</a>;</li> </ol> <p>Rural population is estimated using the following translation rules for GHS-SMOD (note: these are arbitrary rules based on the GHS-SMOD documentation):</p> <ul> <li>Class 30: “Urban Centre grid cell” = 0% rural</li> <li>Class 23: “Dense Urban Cluster grid cell” = 0.5% rural</li> <li>Class 22: “Semi-dense Urban Cluster grid cell” = 2% rural</li> <li>Class 21: “Suburban or per-urban grid cell” = 15% rural</li> <li>Class 13: “Rural cluster grid cell” = 95% rural</li> <li>Class 12: “Low Density Rural grid cell” = 100% rural</li> <li>Class 11: “Very low density rural grid cell” = 100% rural</li> </ul> <p>The nighttime images are based on: <a href="https://doi.org/10.5281/zenodo.7750174">https://doi.org/10.5281/zenodo.7750174</a></p> <ul> <li>Schiavina, Marcello; Melchiorri, Michele; Pesaresi, Martino (2023): GHS-SMOD R2023A - GHS settlement layers,<br>application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A,<br>multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:<br>10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA PID: <a href="http://data.europa.eu/89h/a0df7a6f-49de-46ea-%209bde-563437a6e2ba">http://data.europa.eu/89h/a0df7a6f-49de-46ea-</a><br><a href="http://data.europa.eu/89h/a0df7a6f-49de-46ea-%209bde-563437a6e2ba">9bde-563437a6e2ba</a></li> </ul>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 8-bit Sub-Volumes
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of each dataset is 500x1000x1000. Below is a summary of the pixel-sizes and associated datasets on Zenodo.</p> <blockquote> <p>Key:</p> <ul> <li>160695 = 0.3125 Micron = https://zenodo.org/records/13327692</li> <li>169066 = 0.8125 Micron = https://zenodo.org/records/13327682</li> <li>169067 = 1.625 Micron = https://zenodo.org/records/13327651</li> <li>169068 = 2.6 Micron = https://zenodo.org/records/12206815</li> </ul> </blockquote> <p>The purpose of this dataset is to provide an easy to download sub-volumes of the larger (>50GB) datasets in the above Zenodo entries.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p>
Data associated with the following publication: Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel
<p>This data set contains the supporting data associated with the following publication:</p> <p>Morgenthaler, T., Loebach, J., Lynch, H., Pentland, D., Kottorp, A., & Schulze, C. (in press). Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel. Children, Youth and Environments. [DOI was not yet available when the data set was published]</p> <p>The data set includes the following files:</p> <ul> <li>read me file [contains all relevant information to understand and reuse this data set] </li> <li>13 additional files [for description, see read me file]</li> </ul> <p>For more information, please contact the lead researcher, Thomas Morgenthaler (tom.morgenthaler@gmail.com or 121101888@umail.ucc.ie)</p> <p> </p>
Circumarctic Landcover Units
<p>Landcover units have been derived from Copernicus Sentinel-1 und Sentinel-2 data acquired between 2016 and 2024 for the Arctic tundra biome and selected adjacent areas. 23 units of which 20 represent different vegetation characteristics and soil conditions are differentiated. The units have been identified with K-means over a representative transect and in a second step retrieved across the entire Arctic north of the treeline. The description of the units is based on several thousand samples from vegetation surveys and soil probes.</p> <p>The units are supplied at 10m resolution in 17 irregular tiles. Auxiliary data for quality information and input acquisition dates are supplied in a separate file (polygons with attributes). Units are documented in detail in the product user guide (PUG) and in Bartsch, A., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., and Leibman, M.: Circumarctic land cover diversity considering wetness gradients, Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, 2024.</p> <p>For separation of artificial surfaces it is recommended to combine the units with: Bartsch, A., Widhalm, B., von Baeckmann, C., Efimova, A., Tanguy, R., & Pointner, G. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (v2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10160636" rel="noopener">https://doi.org/10.5281/zenodo.10160636</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.