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Air and soil temperature data from the Reference Stand network at the Andrews Experimental Forest, 1971 to present
The current network of temperature measurement sites are designed to represent spatial variability of air and soil temperature in rugged mountain topography, and serve as second-level stations to capture specific microclimate temperatures in conjunction with a network of Benchmark Meteorological Stations (MS001). The air and soil thermograph network has been reduced from the historical network of 37 sites originally established. Currently there are 10 measurement sites with two of these sites measuring relative humidity in addition to air and soil temperature. An original network of 19 sites (RS01-RS19) were established during the International Biome Program in the early 1970's. Emphasis on phenology, plant moisture stress, and leaf nutrient content led to extending this network of air and soil temperature measurement. A plant community classification system (Dyrness et al., 1971) was used as a primary means of stratification, and a set of permanent vegetation plots (Reference Stands) was installed to represent forest communities with distinct vegetation and hypothesized different environments (Dyrness et al., 1974). A thermograph network was installed within the reference stands in the early 1970's (Zobel et al., 1974), and vegetation standing crop, tree growth and mortality, and plant succession were also measured. The majority of these sites were established to monitor micro-meteorological data under the canopy. The purpose of this network was to provide air and soil temperature data for modeling photosynthesis, respiration, phenology, and decomposition, and to measure environmental gradients.
Ecosystem metabolism and associated environmental data for a forested, meadow and reforested reach of White Clay Creek, Chester Co., Pennsylvania; 1971-1975 and 1997-2010
Ecosystem metabolism data for a 3rd-order Piedmont stream were collected during two periods: P1- April 1971 – Dec 1975, and P2- May 1997 – January 2010. Measures were made in a meadow and a forested reach during each period and in a reforested (formerly meadow) reach during the latter years of P2. During P1, measures were made by transferring streambed substrata to chambers in water jackets located on the streambank and measuring dissolved oxygen changes over diel periods. During P2, open system measures of dissolved O2 change were made for several days in warm and cold seasons, with reaeration determined from a propane injection experiment. Metabolism estimates were determined from diel curves of dissolved O2 change. Photosynthetically active radiation (PAR) and chlorophyll were measured concurrent with many measurements in P1 and all measures during P2, and temperature with all measures. Water chemistry parameters (NH4-N, NO3-N, PO4-P, SiO2, Cl, SO4, total alkalinity, pH) associated with each run are included in the data set, as are days since storm of various thresholds. Field procedures, analytical methods and data analyses are detailed in Bott, T.L. & J. D. Newbold, 2023. A multi-year analysis of factors affecting ecosystem metabolism in forested and meadow reaches of a Piedmont Stream. Hydrobiologia
BioVars - bioclimatic datasets for Europe based on a large regional climate ensemble for periods between 1971 to 2098
<p>We present 26 bio-climatic variables that are calculated based on a large ensemble consisting of 70 bias-adjusted GCM-RCM (Global Climate Model – Regional Climate Model) simulations for 1971 to 2098. Both, the historic and the projection periods were calculated using the same models to ensure consistency between the periods. The variables are validated against E-OBS observations from which we calculated the same bio-climatic variables. For projection periods we chose 20 year ranges between 2021 to 2098. Here, we offer two versions of them 1) variables separated into RCP 2.6, 4.5 and 8.5 including the 5th, 50th and 95th percentiles among the realisations and within the RCPS. And 2) variables per realisation separately. We then extracted the temporal 5th, 50th and 95th percentile per period as representing values. Each zipped file contains these 26 bio-climatic variables according to their aggregation. The variables and the units are explained within the data descriptor publication. </p> <p> </p> <p><strong>File descriptions</strong></p> <ul> <li>bioVars_1971-2000_met.tar.gz >> Projections per realisations for period 1971-2000</li> <li>bioVars_2021-2040_met.tar.gz >> Projections per realisations for period 2021-2040</li> <li>bioVars_2041-2060_met.tar.gz >> Projections per realisations for period 2041-2060</li> <li>bioVars_2061-2080_met.tar.gz >> Projections per realisations for period 2061-2080</li> <li>bioVars_2079-2098_met.tar.gz >> Projections per realisations for period 2079-2098</li> <li>bioVars_2021-2040_rcp.tar.gz >> Projections per RCP for period 2021-2040</li> <li>bioVars_2041-2060_rcp.tar.gz >> Projections per RCP for period 2041-2060</li> <li>bioVars_2061-2080_rcp.tar.gz >> Projections per RCP for period 2061-2080</li> <li>bioVars_2079-2098_rcp.tar.gz >> Projections per RCP for period 2079-2098</li> <li>validation.tar.gz >> Validation using E-OBS (v20.0) and Worldclim (version 2.1)</li> </ul> <p> </p> <p><strong>References</strong> <br>Reichmuth, A., Rakovec, O., Boeing, F. <em>et al.</em> BioVars - A bioclimatic dataset for Europe based on a large regional climate ensemble for periods in 1971–2098. <em>Sci Data</em> <strong>12</strong>, 217 (2025). https://doi.org/10.1038/s41597-025-04507-w</p>
Plant & lichen species composition data for Saddle Nodal Plots, 1971 - ongoing.
To study long-term changes in alpine tundra plant communities, thirty permanently marked plots were surveyed in 1971. These same plots were surveyed at twenty, thirty, forty and fifty year intervals from the initial sampling date; in 1991, 2001, 2011, and 2021. Due to covid and other events, data for the 50-year time point was collected over three summers, from 2021-2023. Plots are one by ten meters, divided into ten subplot quadrats, and marked with rebar. The presence or absence of each vascular plant species was recorded per plot. The cover was recorded for each species that appeared in a one meter by ten centimeter strip at the base of each quadrat within each plot. The cover of non-vascular plants, soil, rocks, and lichens was also recorded within the strip in most sampling years. Lichen were also surveyed in 1971 and 2021.These data have been used to study the changes in vascular plant and lichen communities and cover across habitats following a moisture gradient on the saddle portion of Niwot Ridge; these habitats include dry fellfield, dry meadow, moist meadow, wet meadow, snow bank, and shrub tundra. While we have observed some changes in species richness and cover in the plots, overall the plant communities seem fairly stable over time. Lichen species richness has increased in some habitats, while some lichen species have been disappearing from others.
ERA5-Land selected indicators daily aggregates for Africa, 1971
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1971.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Trento 1936 - Building 1971
<u>Coordinates</u>: N/A <br><u>Length</u>: 13.22 m<br><u>Width</u>: 6.41 m<br><u>Height</u>: 6.08 m<br><u>Points</u>: 8 <br><u>Vertices</u>: 36 <br><u>Primitives</u>: 12 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.obj</th><th>.xml</th></tr><tr><td><a href="https://zenodo.org/api/records/12688437/files/building_1971.obj/content">building_1971.obj</a></td><td></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971.obj/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12688437/files/building_1971.glb/content">building_1971.glb</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12688437/files/11578325_edm.xml/content">11578325_edm.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12688437/files/11578325_edm.xml/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12688437/files/11578325_metsmods.xml/content">11578325_metsmods.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12688437/files/11578325_metsmods.xml/content">Link</a></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12688437/files/building_1971_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12549640">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12688437">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Data and figures for "Atlas of Science Collaboration, 1971–2020"
<p><strong>Abstract</strong></p><p>The evolving landscape of interinstitutional collaborative research across 15 natural science disciplines is explored using the open data sourced from OpenAlex. This extensive exploration spans the years from 1971 to 2020, facilitating a thorough investigation of leading scientific output producers and their collaborative relationships based on coauthorships. The findings are visually presented on world maps and other diagrams, offering a clear and insightful portrayal of notable variations in both national and international collaboration patterns across various fields and time periods. These visual representations serve as valuable resources for science policymakers, diplomats and institutional researchers, providing them with a comprehensive overview of global collaboration and aiding their intuitive grasp of the evolving nature of these partnerships over time.</p><p> </p><p><strong>Intended Readership</strong></p><ul><li>The booklet, entitled<i> '</i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i>'</i>, aims to offer a broad overview of international and interinstitutional research collaboration, shedding light on its present status and evolution on a global scale. While it might not delve into intricate scholarly or academic data analysis, it remains a valuable resource for those seeking a general understanding of the collaborative relationships that have been established between research institutions in the world of science.</li><li>The intended readership including science and technology (S&T) policymakers and diplomats, government research and development (R&D) agencies, international organisations, S&T think tanks, as well as institutional research divisions of universities or R&D institutions.</li></ul><p> </p><p><strong>Data Source</strong></p><ul><li>The<i> </i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i> </i>is based on data retrieved from <a href="https://docs.openalex.org/">OpenAlex</a>, a free and open (the CC0 license) catalogue of the world's scholarly papers, researchers, journals and institutions. Launched in January 2022, OpenAlex replaced <a href="https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/">Microsoft Academic Graph (MAG)</a>, which retired at the beginning of 2022.</li><li>OpenAlex collects information on scientific publications, including journal articles, non-journal articles, preprints, conference papers, books and datasets—hereafter collectively referred to as 'works'—from various platforms such as <a href="https://www.crossref.org/">Crossref</a>, <a href="https://orcid.org/">ORCID</a>, <a href="https://ror.org/">ROR</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/">PubMed</a>, preprint servers like <a href="https://arxiv.org/">arXiv</a>, and institutional or disciplinary repositories like <a href="https://zenodo.org/">Zenodo</a>. For comparison with other scholarly data sources such as <a href="https://www.scopus.com/">Scopus</a>, <a href="https://clarivate.com/products/scientific-and-academic-research/research-discovery-and-workflow-solutions/webofscience-platform/">Web of Science</a> and <a href="https://www.dimensions.ai/">Dimensions</a>, please refer to <a href="https://openalex.org/about#comparison">OpenAlex's website</a>.</li><li>OpenAlex offers extensive coverage of meta-information across a diverse spectrum of works, encompassing not only journal publications but also non-journal works, non-English works and contributions from the Global South. This attribute proves beneficial by providing a more precise augmentation of the extent of R&D activities, along with their associated scholarly outputs. This is especially crucial in fields where journals are not the predominant channel for disseminating research outcomes. Furthermore, OpenAlex effectively captures outputs in the preprint format, which might persist for varying durations, spanning from months to years or even indefinitely, without necessarily transitioning into journal publications.</li><li>The present edition (August 2023) of the<i> </i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i> </i>was compiled using data obtained via the <a href="https://docs.openalex.org/how-to-use-the-api/api-overview">OpenAlex API</a> during the period from the 12th to the 15th of August 2023. It is essential to note that OpenAlex is an ongoing project, continuously updating its data and improving its system. Consequently, the visualisations in this booklet may not provide the most comprehensive view or accurate data. Expect more accurate results when acquiring data in the future as OpenAlex undergoes further upgrades. Revised editions of the<i> Atlas of Science Collaboration </i>may be made available on <a href="https://zenodo.org/">Zenodo</a> or other open platforms beyond this release.</li></ul><p> </p><p><strong>R&D Disciplines</strong></p><ul><li>In this current edition, the primary focus centres around the level-1 'concepts' listed in the following table sourced from the OpenAlex classification, as previously explored in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a>. Each level-1 concept is accompanied by 'related concepts', which can offer a finer or broader delineation compared to the level-1 concept. Using this characteristic, an enhanced notion of R&D discipline is constructed by including all associated subconcepts of level 2 or higher for each of the 15 level-1 concepts. For instance, our defined discipline of 'Artificial Intelligence' includes OpenAlex's level-2 concepts of '<a href="https://explore.openalex.org/concepts/C50644808">Artificial Neural Network</a>' and '<a href="https://explore.openalex.org/concepts/C108583219">Deep Learning</a>', but not the level-0 concepts of '<a href="https://explore.openalex.org/concepts/C41008148">Computer Science</a>' or '<a href="https://explore.openalex.org/concepts/C33923547">Mathematics</a>'.</li></ul><p> </p><p> OpenAlex Concept / Identifier / Discipline Code </p><ol><li>Artificial intelligence / <a href="https://explore.openalex.org/concepts/C154945302">C154945302</a> / "ai"</li><li>Quantum mechanics / <a href="https://explore.openalex.org/concepts/C62520636">C62520636</a> / "quantum"</li><li>Biotechnology / <a href="https://explore.openalex.org/concepts/C150903083">C150903083</a> / "bio"</li><li>Nanotechnology / <a href="https://explore.openalex.org/concepts/C171250308">C171250308</a> / "nano"</li><li>Agricultural engineering / <a href="https://explore.openalex.org/concepts/C88463610">C88463610</a> / "agri"</li><li>Particle physics / <a href="https://explore.openalex.org/concepts/C109214941">C109214941</a> / "particle"</li><li>Aerospace engineering / <a href="https://explore.openalex.org/concepts/C146978453">C146978453</a> / "aerospace"</li><li>Nuclear engineering / <a href="https://explore.openalex.org/concepts/C116915560">C116915560</a> / "nuclear"</li><li>Marine engineering / <a href="https://explore.openalex.org/concepts/c199104240">C199104240</a> / "marine"</li><li>Neuroscience / <a href="https://explore.openalex.org/concepts/c169760540">C169760540</a> / "neuro"</li><li>Condensed matter physics / <a href="https://explore.openalex.org/concepts/C26873012">C26873012</a> / "condensed"</li><li>Environmental engineering / <a href="https://explore.openalex.org/concepts/C87717796">C87717796</a> / "envi"</li><li>Earth science / <a href="https://explore.openalex.org/concepts/c1965285">C1965285</a> / "earth"</li><li>Astronomy / <a href="https://explore.openalex.org/concepts/c1276947">C1276947</a> / "astro"</li><li>Pure mathematics / <a href="https://explore.openalex.org/concepts/C202444582">C202444582</a> / "math"</li></ol><p> </p><p><strong>Analysis and Visualisation</strong></p><ul><li>First, the<i> World Map of Science Collaboration</i> ('<strong>wmap_bilat</strong>' folder) divides the period from 1971 to 2020 into four intervals: 1971–1990, 1991–2000, 2001–2010 and 2011–2020. For each period and discipline, bubbles represent the top 199 research institutions in terms of work production. Additionally, for the top 50 research institutions, their locations are connected on the world map using great circle curves (the shortest route between them) to illustrate bilateral coauthorship relationships. Coauthorship relationships with fewer than five coauthored papers are not displayed. The background world map utilises the world data from the <a href="https://cran.r-project.org/package=maps">maps</a> package in R. The connection visualisation between two research institutions leverages the gcIntermediate() function from the <a href="https://cran.r-project.org/package=geosphere">geosphere</a> package in R. The sizes of the bubbles are proportional to the volume of work and can be compared across the different period panels.</li><li>Second, the<i> Top 30 Productive Institutions on the World Map </i>('<strong>wmap_topinst</strong>' folder) displays the leading 30 institutions in terms of work production on the World Map for each discipline and the three respective periods: 1991–2000, 2001–2010 and 2011–2020. The background world map employs the world data from the <a href="https://cran.r-project.org/package=maps">maps</a> package in R along with the <a href="https://cran.r-project.org/package=ggplot2">ggplot2</a> package16 in R. The sizes of the bubbles are proportional to the volume of work, standardised within each period panel, and cannot be compared across panels.</li><li>Third, the<i> Interregional Collaboration Matrix Diagram </i>('<strong>halfmat</strong>' folder) exhibits a half-matrix diagram at the country level for each discipline and the three respective periods: 1991–2000, 2001–2010 and 2011–2020. It counts the number of bilateral coauthorship relationships represented on the World Map. Each bubble's size (area) displayed in the matrix cell is proportional to the number of bilateral coauthorship relationships. This edition particularly focuses on five pivotal parties: the US, China, EU27, the UK and Japan. These parties were specifically selected due to their substantial contributions to work production across all scientific fields from 1971 to 2020. These choices also align with the nations acclaimed as the 'Big 5' science nations (the US, China, Germany, the UK and Japan) in the <a href="https://www.nature.com/articles/d41586-022-00569-7"><i>Nature Index</i></a>. Please note that the Matrix Diagram only takes into account the top 50 institutions in terms of work production for each period and discipline. Therefore, if a cell shows zero (as small dots), it does not necessarily imply the absence of coauthorship relationships for the corresponding bilateral pair.</li><li>Forth, the<i> Interinstitutional Collaboration Dendrogram </i>('<strong>cdend</strong>' folder) elucidates the development and evolution of interinstitutional research collaboration clusters spanning the last five decades. This is accomplished through hierarchical clustering analysis of institutions, considering the top 50 institutions in terms of work production across the four periods: 1971–1990, 1991–2000, 2001–2010 and 2011–2020.<ul><li>The method used for hierarchical clustering analysis is the same as developed in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a>. The distance between institutions X and Y is defined as the number of works with nationalities from both X and Y divided by the total number of works with nationalities from at least one of X and Y, subtracted from 1. Hierarchical clustering analysis was performed on the distance matrix using the hclust function implemented in R with the ward.D2 option (i.e. the original Ward's method) specified.</li><li>The method of dendrogram visualisation is primarily derived from an example detailed on the <a href="https://cran.r-project.org/web/packages/dendextend/vignettes/dendextend.html">dendextend website</a>. Circular dendrograms were created using the <a href="https://cran.r-project.org/package=dendextend">dendextend</a> and <a href="https://cran.r-project.org/package=circlize">circlize</a> packages in R. As one moves inward from the outer edge of the circle towards its centre, institutions or clusters of institutions that are in closer proximity to each other merge earlier.</li><li>To indicate the country where the institutions are located, the country names are included at the beginning of the terms of research institutions, using the two-letter <a href="https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2">ISO3166-1alpha-2</a> code. The accompanied circularised bar graphs represent the number of works for the institutions involved. <a href="https://ror.org/">ROR</a>s are used as the canonical identifiers of the research institutions. Readers of this booklet in PDF format can click on the ROR-based URL ('https://ror.org/...') in the diagrams to view the corresponding ROR webpage from their browser.</li></ul></li><li>Additionally, for each discipline and the respective periods of 1971–1990, 1991–2000, 2001–2010 and 2011–2020, the top 100 institutions in terms of work production are displayed in tabular format ('<strong>table</strong>' folder), showing their respective country codes and production volumes. If multiple research institutions have equal production volumes during each period, they are organised alphabetically by country codes and then by organisation names. Even if distinct rankings are shown, they lack significance and are treated as ties.</li></ul><p> </p><p><strong>Important Notes</strong></p><ul><li>It is worth reiterating that the data from OpenAlex used to compile the <a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a>, even when incorporating bibliometric data related to past works, lacks consistent finality. As of the data acquisition for this version (August 2023), OpenAlex encompassed information regarding approximately 240 million works, with an additional influx of about 50,000 new data entries related to works being added daily. Furthermore, for a substantial portion of these works, information regarding the corresponding institution to which the authors belong remains unknown. As a result, should the same analyses as those embedded within this booklet be replicated in the future, although the qualitative extent of change remains uncertain, it is undeniable that quantitatively distinct data will be acquired. Nonetheless, for individuals seeking an understanding of the global scope and evolution of international and interinstitutional collaborative research, the potential availability of this booklet or an enhanced, continuously updated evidence base holds inherent value.</li><li>Further, it is worth reiterating that the term 'works' encompasses a wide variety of scholarly publications. The analyses conducted in the compilation of this booklet do not take into consideration whether these works are peer-reviewed articles or not, nor do they encompass considerations of their prominence, impact or quality. It is emphasised that the primary intent behind the visualisations in this booklet is to quantitatively capture the momentum of scholarly knowledge production outputs from diverse research institutions, and to identify how productive institutions collaborate internationally and interinstitutionally. Caution must be exercised, with acknowledgment that relying solely on the quantity of scholarly output produced by institutions falls short in encompassing discussions about their research potential, contributions to academia, or their relative superiority or inferiority. Further, it is recommended to consider the limitations discussed in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a> when using this booklet.</li></ul><p> </p><p><strong>Miscellaneous</strong></p><ul><li>It is important to note that some research institutions may encounter difficulties in accurately assessing the actual production volume at the institutional level within each analysis period due to challenges related to name disambiguation and the influence of historical organisational changes in bibliometric databases.</li><li>For the Interinstitutional Collaboration Dendrograms and the rankings of the top 100 productive institutions, entities like universities and R&D institutions are primarily identified using the nomenclature employed in OpenAlex. However, certain portions have been presented through abbreviations or acronyms, both for illustrative purposes and to effectively accommodate limited space. For instance, 'University of' is abbreviated as 'U.', 'Institution' and 'Institute' as 'Inst', 'National Laboratory' as 'NL', and 'Science' and 'Technology' as 'Sci' and 'Tech', correspondingly, among others. Should readers possess more fitting suggestions for abbreviations specific to particular organisations, or any other ideas aimed at enhancing the content of this booklet, we would greatly appreciate their input.</li></ul><p> </p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 1971
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1971.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Invertebrates of the Andrews Experimental Forest: An annotated list of insects and other arthropods, 1971 to 2002
This publication is not a pro forma species list; rather, it has been generated as the result of diverse ecological studies centered on and around the Andrews Forest beginning in 1971. No attempt has been made to exhaustively collect the area with methodologies appropriate to each invertebrate group. This list provides some insight into the enormous invertebrate diversity present in the coniferous forests of the Pacific Northwest. It provides reference material for investigators who might be engaged in ecological investigations. We hope that these data, set in an ecological context, will stimulate collaboration and facilitate the design of future research.
Average monthly and annual precipitation spatial grids. (1971-2000 and 1980-1989), Andrews Experimental Forest
These files are spatially gridded precipitation of average monthly and annual precipitation for the climatological periods 1971-2000 and 1980-89, Andrews Experimental Forest. The original 1980-89 grids were updated for a greater time span and also the extent of the coverage is increased. Interpolation of point station measurements to a spatial grid was done using the PRISM model, developed by Christopher Daly of the PRISM Group at Oregon State University. PRISM interpolation accounts for the effects of elevation on the spatial patterns of precipitation. Grid resolution is 100 meters. Station data used in the interpolation were obtained from current and historic rain gauge stations within the forest. These grids represent the first significant effort to map climatological precipitation in the Andrews Forest. Further information on PRISM can be found at http://prism.oregonstate.edu/
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), Andrews Experimental Forest
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), adjusted for the effects of solar radiation and sky view factors, Andrews Experimental Forest. Maps were created using PRISM (Parameter-elevation Regressions on Independent Slopes Model), developed by Dr. Christopher Daly at Oregon State University’s PRISM Climate Group in 2010 (prism.oregonstate.edu). Grids were exported into ASCII format from GRASS GIS software; values are in degrees C x 100. Spatial resolution is 50 meters. Two sets of temperature values are available: (1) values derived from an interpolation of point station temperature values accounting for elevation; and (2) values from (1), adjusted for effects of solar radiation exposure and sky view factors. Radiation exposure and sky view factors were calculated from a two-stream solar radiation model that accounts for elevation, slope, aspect, and shading from adjacent pixels on a 50-m digital elevation model. Temperature data were obtained from selected benchmark and reference stand climate stations within the HJ Andrews, as well as National Weather Service Cooperative (COOP) and USDA NRCS Snow Telemetry (SNOTEL) stations in the vicinity. Due to the sparseness of the station data outside the Andrews, values outside the Andrews are considered to have high uncertainty. Temperature values assume an open site with no canopy cover, so are not appropriate for describing temperatures within the forest canopy. See MS033 for radiation grids used to make the radiation adjustments.
Land Use, Anderson Level I - Ipswich Watershed - 1971 - Idrisi Raster File
This layer shows land use for the Ipswich Study area, based upon MassGIS classification and grouped in accordance with the Anderson: Level I convention. It is intended to be used in connection with other Ipswich Study Area maps.
Compilation of Land Use Data in 21 and 37 Category Classifications - Ipswich and Parker River Watersheds - 1971, 1985, 1991, and 1999 - Vector Shapefile.
The MassGIS Land Use datalayer has 37 land use classifications interpreted from 1:25,000 aerial photography. This layer contains data for 21 and 37 category classifications for the years of 1971, 1985, 1991, and 1999. Coverage is complete for all towns that fall partially or completely within the Ipswich River and/or Parker River watersheds. Data compiled for 1971, 1985, 1991, and 1999.
Land Use, Anderson Level I, 7 Categories - Ipswich and Parker River Watersheds - 1971 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 7 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1971. This datalayer has complete information.
Land Use, 21 Categories - Ipswich and Parker River Watersheds - 1971 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 21 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1971. This datalayer has complete information.
Figs 23-24. Pseudopecoeloides scomberi Hafeezullah, 1971 in Seven species of Pseudopecoeloides Yamaguti, 1940 (Digenea, Opecoelidae) from temperate marine fishes of Australia, including five new species
Figs 23-24. Pseudopecoeloides scomberi Hafeezullah, 1971 ex Scomberoides lysan. 23. Whole-mount lateroventral view. 24. Ventral view of posterior end showing uroproct. Scale bars: 23, 500 µm; 24, 100 µm.
Figure 6. Cymonomus bathamae Dell, 1971 in The Cymonomid Crabs of New Zealand and Australia (Crustacea: Brachyura: Cyclodorripoida)
Figure 6. Cymonomus bathamae Dell, 1971. (A–F) spent female paratype, cl 3.9 mm, pcl 3.3 mm, cw 3.6 mm, Chatham Rise, New Zealand, NIWA 68007. (G–I) male, cl 6.0 mm, pcl 3.3 mm, cw 3.5 mm, Chatham Rise, New Zealand, NIWA 31653. (J) ovigerous female paratype, cl 4.4 mm, pcl 3.7 mm, cw 4.1 mm, off Otago, New Zealand, NIWA 68021. (A) dorsal habitus; (B) posterior abdomen; (C, J) right epistomial spine mesial to base of antenna; (D) fronto-orbital region; (E) right maxilliped 3; (F) thoracic sternite 3; (G) right G1, abdominal view; (H) right G2, abdominal view; (I) pleotelson. Scale: A–B = 2.0 mm; C, J = 0.5 mm; D–I = 1.0 mm.
Figure 2. Cymonomus aequilonius Dell, 1971 in The Cymonomid Crabs of New Zealand and Australia (Crustacea: Brachyura: Cyclodorripoida)
Figure 2. Cymonomus aequilonius Dell, 1971, female holotype, cl 7.1 mm, pcl 5.7 mm, cw 6.4 mm, Bay of Plenty, New Zealand, NMNZ Cr1866. (A) dorsal habitus; (B) posterior abdomen; (C) right epistomial spine mesial to base of antenna; (D) fronto-orbital region; (E) right maxilliped 3; (F) thoracic sternite 3. Scale: A, B = 2.0 mm; C = 0.5 mm; D–F = 1.0 mm.
Fig. 42. Distribution maps. A–B. Laephotis angolensis Monard, 1935. C–D. Laephotis botswanae Setzer, 1971. E–F in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 42. Distribution maps. A–B. Laephotis angolensis Monard, 1935. C–D. Laephotis botswanae Setzer, 1971. E–F. Mimetillus moloneyi (Thomas, 1891). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Figure 10 in Taxonomy of the genus Oriverutus Siddiqi, 1971 (Nematoda: Dorylaimida: Nordiidae)
Figure 10. Parapalus arboricola Loof and Zullini, 2000 (light microscopy). (A–C) Neck region of female, male and J4, respectively; (D) J4, anterior region in median, lateral view; (E) Male, anterior region in median, lateral view; (F) Male, lip region and amphid fovea in submedian, lateral view; (H) Vagina; (I) Male, spicules and caudal region. Scale bars: A–C = 100 µm; D–F, H = 5 µm; G = 10 µm; I = 20 µm.
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International Brain Laboratory public data
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