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Pre-Colonial and Modern Tree Data from Nine Northeastern States 1620-2008
The northeastern United States is a predominately-forested region that, like most of the eastern U.S., has undergone a 400-year history of intense logging, land clearance for agriculture, and natural reforestation. This setting affords the opportunity to address a major ecological question: How similar are today’s forests to those existing prior to European colonization? Working throughout a nine-state region spanning Maine to Pennsylvania, we assembled a comprehensive database of archival land-survey records describing the forests at the time of European colonization. We compared these records to modern forest inventory data and described: (1) the magnitude and attributes of forest compositional change, (2) the geography of change and (3) the relationships between change and environmental factors and historical land use. We found that with few exceptions, notably the American chestnut, the same taxa that made up the pre-colonial forest still comprise the forest today, despite ample opportunities for species invasion and loss. Nonetheless, there have been dramatic shifts in the relative abundance of forest taxa. The magnitude of change is spatially clustered at local scales (less than 125-km) but exhibits little evidence of regional-scale gradients. Compositional change is most strongly associated with the historical extent of agricultural clearing. Throughout the region, there has been a broad ecological shift away from late successional taxa, such as beech and hemlock, in favor of early- and mid-successional taxa, such as red maple and poplar. Additionally, the modern forest composition is more homogeneous and less coupled to local climatic controls.
Modern China Geospatial Database - Main Dataset
<p>MCGD_Data_V2.2 contains all the data that we have collected on locations in modern China, plus a number of locations outside of China that we encounter frequently in historical sources on China. All further updates will appear under the name "MCGD_Data" with a time stamp (e.g., MCGD_Data2023-06-21)</p> <p>You can also have access to this dataset and all the datasets that the ENP-China makes available on GitLab: https://gitlab.com/enpchina/IndexesEnp</p> <p>Altogether there are 464,970 entries. The data include seven variables:<br>- Name: Place names and their variants in Chinese, pinyin, and any recorded transliteration<br>- Prov_Zh: Chinese province names in Chinese characters (新疆, 江蘇, 河北, etc.)<br>- Prov_Py: Chinese province names in pinyin<br>- LAT: Latitude coordinates<br>- LONG: Longitude coordinates<br>- LocID: Location identifiers<br>- NameID: Location name identifiers</p> <p>The Name IDs all start with H followed by seven digits. This is the internal ID system of MCGD.</p> <p>Locations IDs that start with "D" are data points extracted from China Historical GIS (Harvard University); those that start with "E" are locations extracted from the data points in Geonames or data points we have added from various map sources.</p> <p>One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates.</p> <p>From June 2021 onward, we have adopted a different file naming system to keep track of versions. From MCGD_Data_V1 we have moved to MCGD_Data_V2. In June 2022, we introduced time stamps, which result in the following naming convention: MCGD_Data_YYYY.MM.DD. </p> <p> </p> <p><strong>UPDATES</strong></p> <p><strong>MCGD_Data2025_08_06</strong> introduces a significant update with the addition of the <strong>‘Code’</strong> column. This column categorizes place names as follows:</p> <ul> <li> <p><strong>A</strong>: Canonical Chinese name</p> </li> <li> <p><strong>C</strong>: Alternative Chinese name</p> </li> <li> <p><strong>P</strong>: Romanized name in pinyin</p> </li> <li> <p><strong>W</strong>: Romanized name in another transliteration system</p> </li> </ul> <p>When the codes <strong>P</strong> or <strong>W</strong> are doubled (<strong>PP</strong>, <strong>WW</strong>), this indicates that the place name does not match any existing Chinese name in the dataset. These unmatched names will be reviewed and linked progressively, rather than through a systematic batch process, due to their high volume.The coding system is designed to facilitate name-matching operations between MCGD and place names extracted from historical sources using programming tools. It also enables filtering for more precise and efficient matching. The dataset contains a total of <strong>472,749 entries</strong>.</p> <p>MCGD_Data2025_02_28 includes a major change with the duplication of all the locations listed under Beijing, Shanghai, Tianjin, and Chongqing (北京, 上海, 天津, 重慶) and their listing under the name of the provinces to which they belonge origially before the creation of the four special municipalities after 1949. This is meant to facilitate the matching of data from historical sources. Each location has a unique NameID. Altogether there are 472,818 entries</p> <p>MCGD_Data2025_02_27 inclues an update on locations extracted from Minguo zhengfu ge yuanhui keyuan yishang zhiyuanlu 國民政府各院部會科員以上職員錄 (Directory of staff members and above in the ministries and committees of the National Government). Nanjing: Guomin zhengfu wenguanchu yinzhuju 國民政府文官處印鑄局國民政府文官處印鑄局, 1944). We also made corrections in the Prov_Py and Prov_Zh columns as there were some misalignments between the pinyin name and the name in Chines characters. The file now includes 465,128 entries.</p> <p>MCGD_Data2024_03_23 includes an update on locations in Taiwan from the Asia Directories. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown").</p> <p>MCGD_Data2023.12.22 contains all the data that we have collected on locations in China, whatever the period. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). The dataset also includes locations outside of China for the purpose of matching such locations to the place names extracted from historical sources. For example, one may need to locate individuals born outside of China. Rather than maintaining two separate files, we made the decision to incorporate all the place names found in historical sources in the gazetteer. Such place names can easily be removed by selecting all the entries where the 'Province' data is missing.</p>
House complex. Now hotel restaurant "El Buffi". 1929. Modernism.
<u>File Name</u>: PM_072999_E_Solsona <br><u>Sublocation</u>: Plaça de Sant Roc <br><u>Location</u>: Solsona <br><u>Province</u>: Catalunya, Lleida <br><u>Country</u>: Spain <br><u>Header</u>: Restaurant el buffi <br><u>Description</u>: House complex. Now hotel restaurant "El Buffi". 1929. Modernism. <br><u>Author</u>: photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert; pmrmaeyaert@gmail.com <br><u>Keywords</u>: Cultural heritage|Monuments; Cultural heritage|Monuments|Private house; Cultural heritage|Styles; Cultural heritage|Styles|Eclecticism; Cultural heritage|Styles|Modernism; Europe|Spain; Europe|Spain|Catalunya; Europe|Spain|Catalunya|Lleida; Europe|Spain|Catalunya|Lleida|Solsona; Cultural heritage <br><u>Date of Generation</u>: 2012-06-12T11:35:06.064
Modern China Geospatial Database - Republican China Dataset
<p><strong>MCGD_Rep</strong> is a sample of spatial data for China in the first half of twentieth century (1900-1949). The data was extracted from the MCGD Main Dataset. It is based mostly on the list of <em>xian</em> (county) seats in 1931 [Source: Zang, Lihe 臧励龢, ed. Zhongguo gujin diming da cidian 中国古今地名大辞典. Shanghai 上海: Commercial Press, 1931], with the addition of some external data [Source: Crow Newspaper Directories]. By and large, it presents a list of the major locations in China between 1900 and 1949. It contains 1,977 entries with the following variables: name in Chinese, name in pinyin; name of the province in Chinese and in pinyin; latitude and longitude, and Name ID and Location ID.</p>
Modern aridity in the Altai-Sayan Mountain Range derived from multiple millennial proxies
<p><em>1500-year stable carbon and oxygen isotopes in larch tree-ring cellulose from the Altai-Sayan Mountain Range region </em>(49-51N, 87-89 E)</p> <p><em>Regional summer (June-July-August) precipitation reconstruction for the Altai-Sayan Mountain Range region based on d<sup>13</sup>C in tree-ring cellulose (d<sup>13</sup>C<sub>cell </sub>) combined with Co/Inc and Rb/Sr from Teletskoe Lake core sediments (TLs).</em></p> <p><em>Regional summer air temperature (June-July-August) reconstruction based on d<sup>18</sup>O<sub> </sub>in tree-ring cellulose (d<sup>18</sup>O<sub>cell</sub>), tree-ring width (TRW), latewood density (MXD) and elemental concentrations (Ca, Ti, Br/Sr) in the Teletskoe Lake core sediments (TLs).</em></p>
Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations
<p>These are the raw or processed data used for a paper published in Chemical Geology by Debrie et al. (2022), entitled "Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations", <a href="https://doi.org/10.1016/j.chemgeo.2022.121059">https://doi.org/10.1016/j.chemgeo.2022.121059</a></p> <p>The data content is summarized in the List_description_of_data.xlsx file</p>
MOdern River archivEs of Particulate Organic Carbon: MOREPOC
<p>Modern River Archives of Particulate Organic Carbon (MOREPOC) version 1.1 is a new, open-access, georeferenced, global database, featuring data on POC in suspended particulate matter (SPM) collected at 233 locations across 121 major river systems. This database includes 3,546 SPM data entries, among which 3,053 with POC content, 3,402 with stable carbon isotope (δ<sup>13</sup>C) values, 2,283 with radiocarbon activity (Δ<sup>14</sup>C) values, 1,936 with total nitrogen content, and 299 with aluminum-to-silicon mass ratios (Al/Si). The MOREPOC database aims at being used by the Earth System community to build comprehensive and quantitative models for the mobilization, alteration, and fate of terrestrial POC.</p> <p>The supply of particulate organic carbon (POC) associated with terrigenous solids transported to the ocean by rivers plays a significant role in the global carbon cycle. To advance our understanding of the source, transport, and fate of fluvial POC from regional to global scales, databases of riverine POC are needed, including elemental and isotope composition data from contrasted river basins in terms of geomorphology, lithology, climate, and anthropogenic pressure. MOREPOC will benefit the scientific community carrying out research on riverine POC sources, transport, and fate, furthermore, helping inform and validate Earth system models to improve the ability to model and understand the global carbon cycle. Existing environmental raster global datasets for climate, geomorphology, lithology, tectonics, hydrology, and land use, also offer promising prospects for the use of MOREPOC for identifying the controls on POC fluxes and composition, in particular using advanced statistical analysis or machine learning techniques. Moreover, MOREPOC enables a better understanding of sources, transport, and fate of fluvial POC combined with some existing ocean sediment databases. Future updates of MOREPOC should include new bulk POC parameters as well as data on molecular fractions, thermal labile fractions, or specific components such as black carbon or fossil carbon, which should, in turn, provide additional insight into the alteration of riverine POC from source to sink, an essential feature of the global carbon cycle.</p> <p><strong>Data description</strong></p> <p>The MOREPOC database consists of two parts: 1) the master metadata (MOREPOC_v1.1); 2) the summarization of references and methods (MOREPOC_v1.1_RM). A Readme is provided to better understand all parameters provided in the MOREPOC v1.1 database. </p> <p>MOREPOC_v1.1 includes one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv), and GIS shapefile (compiled in .rar) using WGS84 coordinate system.</p> <ul> <li>MOREPOC_v1.1.xlsx</li> <li>MOREPOC_v1.1.csv</li> <li>MOREPOC_v1.1.rar (GIS shapefile)</li> </ul> <p>MOREPOC_v1.1_RM only provides one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv).</p> <ul> <li>MOREPOC_v1.1_RM.xlsx</li> <li>MOREPOC_v1.1_RM.csv</li> </ul> <p>The database structure of MOREPOC is listed in Table.1 to understand all provided parameters, more information can be found in the companion manuscript.</p> <table> <caption><strong>Table. 1 Description of the parameters of the MOREPOC v1.1 database.</strong></caption> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Description</strong></td> <td><strong>MOREPOC column name</strong></td> </tr> <tr> <td>River name</td> <td>Name of the major river basin</td> <td>bas_id</td> </tr> <tr> <td>Sub river name</td> <td>Name of the sampled river/stream</td> <td>riv_id</td> </tr> <tr> <td>Country</td> <td>Name of country or places</td> <td>country</td> </tr> <tr> <td>Continent</td> <td>Name of the continent</td> <td>cont</td> </tr> <tr> <td>Sampling site/code</td> <td>Expedition sampling ID</td> <td>code</td> </tr> <tr> <td>Sampling date</td> <td>Time (month/day/year) when the SPM sample was collected</td> <td>time_m/d/y</td> </tr> <tr> <td>Latitude</td> <td>Decimal latitude using WGS 1984</td> <td>lat</td> </tr> <tr> <td>Longitude</td> <td>Decimal longitude using WGS 1984</td> <td>lon</td> </tr> <tr> <td>Sampling technique</td> <td>Method of SPM sampling</td> <td>type_spm</td> </tr> <tr> <td>Size fraction of SPM</td> <td>Reported size fractions analyzed</td> <td>fra_spm</td> </tr> <tr> <td>SPM concentration (mg/L)</td> <td>The total dry weight of SPM in mg per liter water column</td> <td>conc_spm</td> </tr> <tr> <td>POC concentration (mg/L)</td> <td>The total dry weight of POC in mg per liter water column</td> <td>conc_poc</td> </tr> <tr> <td>POC content (%)</td> <td>The total POC content of SPM in wt %</td> <td>per_poc</td> </tr> <tr> <td>POC content uncertainty (1σ)</td> <td>The analytical uncertainty for POC content (1σ)</td> <td>perc_poc_1sd</td> </tr> <tr> <td>δ<sup>13</sup>C (‰)</td> <td>δ<sup>13</sup>C values of POC (carbonate removed) in ‰</td> <td>d13C_poc</td> </tr> <tr> <td>δ<sup>13</sup>C uncertainty (1σ)</td> <td>The analytical uncertainty for δ<sup>13</sup>C of POC</td> <td>d13C_1sd</td> </tr> <tr> <td>Δ<sup>14</sup>C (‰)</td> <td>Δ<sup>14</sup>C values of POC (carbonate removed) in ‰</td> <td>D14C_poc</td> </tr> <tr> <td>Δ<sup>14</sup>C uncertainty (1σ)</td> <td>The analytical uncertainty for Δ<sup>14</sup>C of POC</td> <td>D14C_1sd</td> </tr> <tr> <td>Fraction modern (Fm)</td> <td>Fraction modern of POC</td> <td>F14C</td> </tr> <tr> <td>Radiocarbon ages (year)</td> <td>Radiocarbon ages before present (1950)</td> <td>age_14C</td> </tr> <tr> <td>TN content (%)</td> <td>The total nitrogen content of SPM in wt %</td> <td>perc_tn</td> </tr> <tr> <td>C<sub>org</sub>/N mass ratio</td> <td>Mass ratio of POC to TN in SPM</td> <td>cn_ratio</td> </tr> <tr> <td>Al/Si mass ratio</td> <td>Mass ratio of Al to Si in SPM</td> <td>alsi_ratio</td> </tr> <tr> <td>Reference</td> <td>Full list of citations of the data source</td> <td>ref</td> </tr> <tr> <td>Complete reference</td> <td>Complete information for cited references</td> <td>ref_c</td> </tr> <tr> <td>Measured parameters</td> <td>Summarization of elemental and isotopic carbon parameters measured</td> <td>para_m</td> </tr> <tr> <td>Calculated parameters</td> <td>Summarization of elemental and isotopic carbon parameters calculated</td> <td>para_c</td> </tr> <tr> <td>Filter</td> <td>Filter used to obtain SPM</td> <td>filter</td> </tr> <tr> <td>Acid</td> <td>The acid type used to remove carbonate in SPM</td> <td>acid</td> </tr> <tr> <td>Carbonate removal method</td> <td>The method used to remove carbonate in SPM</td> <td>m_acid</td> </tr> <tr> <td>Acid concentration</td> <td>The concentration of adopted acid to remove carbonate in SPM</td> <td>conc_acid</td> </tr> <tr> <td>carbonate removal temperature</td> <td>The environmental temperature for acid to remove carbonate in SPM</td> <td>temp_acid</td> </tr> <tr> <td>Carbonate removal duration</td> <td>The reaction time used for acid to remove carbonate in SPM</td> <td>time_acid</td> </tr> <tr> <td>Note</td> <td>Additional information for carbonate removal process</td> <td>note</td> </tr> </tbody> </table> <p><strong>Contributing Data</strong></p> <p>Please contact Yutian Ke at <a href="mailto:yutianke@caltech.edu">yutianke@caltech.edu</a> or <a href="mailto:yutian.ke@universite-paris-saclay.fr">yutian.ke@universite-paris-saclay.fr</a> if you are interested in contributing your published or unpublished data to MOREPOC.</p> <p><strong>Citation</strong></p> <p>Ke, Y. T., Calmels, D., Bouchez, J., Cécile, Q.: MOdern River archivEs of Particulate Organic Carbon: MOREPOC, Dataset version 1.1, Zenodo [dataset], <a href="https://doi.org/10.5281/zenodo.6541925">https://doi.org/10.5281/zenodo.7055970</a>.</p>
Climate reconstructions for the SMPDSv1 modern pollen data set
<p>The dataset contains estimates of three bioclimatic variables at modern pollen sites from the SMPDSv1 modern pollen data set (Harrison, 2019). The bioclimatic variables are mean temperature of the coldest month (MTCO), growing degree days above 0°C (GDD0), and an annual Moisture Index, defined as the ratio of annual precipitation to annual potential evapotranspiration (MI). Estimates of these bioclimatic variables were derived using geographically-weighted regression of gridded climate data in order to correct for elevation differences between each pollen site and the corresponding grid cell. The climatological data (mean monthly temperature, precipitation, and fractional sunshine hours) were derived from the CRU CL v2.0 gridded dataset of modern (1961-1990) surface climate at 10 arc minute resolution (~18 km) (New et al., 2002).Geographically- weighted regression (GWR) was carried out in ArcGIS (v10.3, ESRI, 2014). A fixed bandwidth kernel of 1.06 ° (~140km) was used in the GWR because this optimized model diagnostics and reduced spatial clustering of residuals relative to other bandwidths. The climate of each pollen site was then estimated based on its longitude, latitude, and elevation. MTCO was taken directly from the GWR regression. GDD0 were estimated from daily data using a mean-conserving interpolation of the monthly mean temperatures. MI was calculated for each pollen site using code modified from SPLASH v1.0 (Davis et al., 2017) based on daily values of precipitation, temperature and sunshine hours again obtained using a mean-conserving interpolation of the monthly values of each.</p>
Rehti रहती (सिहोर जिला Madhya Pradesh). Hero stone incorporated into a modern shrine.
<p>Rehti रहती (सिहोर जिला Madhya Pradesh). Hero stone incorporated into a modern shrine, probably Paramāra period.</p>
Modern Fortran Survey 2019 Raw Data
<p>We've organized an online survey about usage of Modern Fortran features (2003/2008 standard) on https://modernfortran.limequery.org</p> <p>The survey was online from June 2019 to March 2020 and includes 140 complete responses and 85 incomplete.</p> <p>This package contains the raw data and the automatically generated statistics from it:</p> <ul> <li>html-forms.zip .. the survey exported as a static HTML page (the original survey hid subquestions based on previous answers)</li> <li>limesurvey-archive.lsa .. the complete survey (including the results) in LimeSurvey archive format (ZIP file with proprietary files)</li> <li>printed-forms.pdf .. the survey as PDF</li> <li>results-{all,complete-only,incomplete-only}.pdf .. the automatically generated statistics of the survey results including plots. "all": include all surveys, "complete-only": only surveys which were completed, "incomplete-only": only surveys which were not completed</li> <li>results-{complete,incomplete}_only.csv .. the results in CSV format for the respective subsets</li> </ul>
Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला). View of the ghāṭ from the north east, with resevoir, showing part of the modern dam.
<p>Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला). View of the ghāṭ from the north east, with resevoir, showing part of the modern dam.</p>
An agent-based model of the origins of modern linguistic complexity – supplementary information
<p>A central question in the evolution of human language is whether it emerged as a result of one specific event or from a mosaic-like constellation of different phenomena and their interactions. Three potential processes have been identified by recent research as the potential <em>primum mobile</em> for the origins of modern linguistic complexity: Self-domestication, characterized by a reduction in reactive aggression and often associated with a gracilization of the face; changes in early brain development manifested by globularization of the skull; and demographic expansion of H. sapiens during the Middle Pleistocene. We developed an agent-based model to investigate how these three factors influence transmission of information within a population. Our model shows that there is an optimal degree of both hostility and mental capacity at which the amount of transmitted information is the largest. It also shows that linguistic communi- ties formed within the population are strongest under circumstances where individuals have high levels of cognitive capacity available for information processing and there is at least a certain degree of hos- tility present. In contrast, we find no significant effects related to population size.</p>
Adaptive Introgression in Modern Human Circadian Rhythm Genes Datasets
<p><strong>README:</strong></p> <p>Modern human genetic data with evidence of adaptive introgression from Neanderthals or Denisovans within circadian rhythm genes. The data was generated from the phased gnomAD 1KGP + HGDP callset (Koenig <em>et al</em>., 2024) and introgressed segments were identified by SPrime (Browning <em>et al</em>., 2018). Genes of interest were downloaded from the Circadian Genome Database (CGDB) (Li <em>et al</em>., 2017). Additional variants, haplotypes, and genes that have been previously reported to influence circadian rhythm or chronotype that are thought to be derived from Neanderthals and Denisovans were compiled from Dannemann & Kelso (2017), McArthur et al. (2021), Dannemann et al. (2022), and Velazquez-Arcelay et al. (2023).</p> <p><strong>SPrime ND_Match Files</strong></p> <p>Raw SPrime identified files that we used for our entire analysis. These were modified to include the archaic allele, archaic allele frequency, and average introgressed segment allele frequency. Note that these have been lifted over (Hinrichs <em>et</em> <em>al</em>., 2006) from GRCh38 (hg38) to GRCh37 (hg19) coordinates to match the genome builds of the archaic samples used in our study. As such, any manually generated variant IDs (chromosome:position:ReferenceAllele_AlternativeAllele naming convention) may no longer match the position they are currently sitting on as they were generated with hg38 coordinates. However, all of these were subsequently filtered out of our final results and any proper SNP IDs (dbSNP labels) will be accurate.</p> <p><strong>Supplementary Tables</strong></p> <p>All supplementary tables have an associated README as the first sheet that explains in detail the contents.</p> <p><strong>NEXUS Files</strong></p> <p>NEXUS files were used to generate haplotype networks in PopArt (Leigh & Bryant, 2015). There is a larger, master haplotype file and a smaller subset file. The larger file contains 668 haplotypes from all populations generated in the phased gnomAD 1KGP + HGDP callset (Koenig <em>et al</em>., 2024) for the <em>SUSD1 </em>core haplotype. The smaller subset file is the top 50 haplotypes and ties based on frequency, all Oceanic haplotypes with frequencies of at least 2, and the Neanderthal and Denisovan haplotypes for <em>SUSD1</em>. </p> <p><strong>TRAITS file</strong></p> <p>Accompanies the NEXUS files to create pie graphs for the haplotype network and contains frequency counts of number of haplotypes per region.</p>
Research Data and Code for "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles"
<p>This dataset documents results and code for the paper "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles" by Stefan Heßbrüggen-Walter, forthcoming in *Synthese*. The data to be processed are contained in four files, derived from a larger dataset related to German dissertations and sourced from the national bibliography of 17th century German prints *VD 17* that will be released at a later date. More information can be found in the file `README.md`. </p>
Typology of academic disciplines in the Modern China Biographical Database
<p>This table presents the bilingual typology of academic disciplines used in the Modern China Biographical Database. It is based mainly on the typology created by Yuan T'ung-li in his three bibliographical volumes about the doctoral dissertations by Chinese students in the United States, the United Kingdom, and continental Europe. We adapted this typology to include other disciplines that were present in historical sources.</p> <p><strong>Dataset Description: Typology of Disciplines (Level 2)</strong></p> <p><strong>Overview:</strong> This dataset provides a bilingual typology of academic disciplines, specifically focusing on Level 2 classifications. The terms are extracted from various Chinese sources, with English translations provided. It is structured hierarchically, connecting each Level 2 discipline to broader categories (Level 1 and Level 0), facilitating multilingual academic classification.</p> <p><strong>Structure:</strong> The dataset consists of the following key columns:</p> <ul> <li> <p><strong>Level 2 Discipline (English & Chinese):</strong> The specific sub-discipline classification.</p> </li> <li> <p><strong>Level 1 Discipline (English & Chinese):</strong> A broader category that groups multiple Level 2 disciplines.</p> </li> <li> <p><strong>Level 0 Discipline (English & Chinese):</strong> The highest-level classification representing major academic domains.</p> </li> <li> <p><strong>Level 1 Code:</strong> A numerical or coded identifier for Level 1 disciplines, supporting structured data processing.</p> </li> </ul> <p><strong>Purpose & Applications:</strong></p> <ul> <li> <p><strong>Hierarchical Classification:</strong> Enables structured categorization of academic fields across multiple levels.</p> </li> <li> <p><strong>Multilingual Standardization:</strong> Supports bilingual terminology consistency in academic and research contexts.</p> </li> </ul> <p>Main sources:</p> <p> </p> <p>Yuan, T’ung-li. <em>A Guide to Doctoral Dissertations by Chinese Students in America, 1905-1960</em>. Washington, D.C.: Published under the auspices of the Sino-American Cultural Society, 1961.</p> <p>———. <em>A Guide to Doctoral Dissertations by Chinese Students in Continental Europe, 1907-1962</em>. S.l., 1964.</p> <p>———. <em>Doctoral dissertations by Chinese students in Great Britain and Northern Ireland, 1916-1961.</em> Uden sted og forlag, 1963.</p>
Dataset: Geochemische en petrografische analyseresultaten uitgevoerd op vroegmoderne en moderne kleipijpjes uit Gent
<p><strong>Resultaten, interpretatie en rapportage van geochemische en petrografische analyses uitgevoerd op vroegmoderne en moderne kleipijpjes uit verschillende opgravingen te Gent.</strong></p> <p>Het deelrapport ‘De onzichtbare vingerafdruk van de Gentse pijpenbakker: een archeometrische studie van Gentse kleipijpjes (ca. 1600-1900), vormt een onderdeel van het archeologisch syntheseonderzoek ‘Pijpen voor Malta: Gentse kleipijpjes uit de periode 1600-1900 in archeologisch en sociaal-cultureel perspectief’, gefinancierd door de Vlaamse Overheid.</p> <p>Dit rapport (<strong>Gentse_Kleipijpen-Archeometrisch_Rapport.pdf</strong>) vormt een bijlage bij het eindverslag “GENTSE KLEIPIJPJES. Gentse kleipijpjes uit de periode 1600-1900 in archeologisch en sociaal cultureel perspectief”.</p> <p>De archeometrische studie wil een zo goed mogelijk beeld vormen van de samenstelling van de Gentse kleipijpen. Hiervoor worden twee technieken gecombineerd: röntgenfluorescentiespectrometrie (XRF) en petrografische microscopie.</p> <p>In totaal zijn er 55 stalen (<strong>ID: PvM-##</strong>) genomen van 11 sites uit Gent. Een volledig overzicht van de onderzochte stalen met de <strong>archeologische informatie</strong> is na te lezen in het bestand <strong>GKP_archeo_info.</strong> De geochemische analyseresultaten per staal zijn terug te vinden in <strong>GKP_geochem_xrf</strong>. De petrografische informatie over de mineralogische samenstelling en textuur zijn opgenomen in <strong>Petro_tabel_basis_nl</strong>. Een schematische voorstelling van hun interpretatie staat in <strong>Petro_tabel_interpretatie</strong>.</p> <p>Alle analyseresultaten worden zowel als .txt, .csv en .xlsx-bestand aangeboden.</p>
Typology of academic degrees in the Modern China Biographical Database
<p>This table presents the bilingual typology of academic degrees used in the Modern China Biographical Database. It is based mainly on the data collected in historical sources.</p> <p>There are two levels:</p> <p>- Degree name: full name of the academic degree in English</p> <p>- Degree_Level_Eng: first level of classification and clustering of academic degrees in English</p> <p>- Degree_Level_ZhT: first level of classification and clustering of academic degrees in Chinese</p>
Modern pollen data from the East Asian Pollen Database (EAPD): pollen, vegetation and climate relationship
<p>This is a modern pollen dataset of eastern Asia, in which a total of 1756 sample sites is selected from the original database EAPD (East Asian Pollen Database) which consists of 2858 samples. The sample types are mainly surface soil, moss, sediment top (lake, delta, peatland, river basin, reservoir and so on), and dust capture. The pollen data are mostly count numbers, but a few was originally given in percentage (marked with TRUE for proportion or percentage). We have checked pollen taxonomic nomenclature and combined some synonym pollen types from different original sources.</p> <p>This dataset includes only the samples collected in the areas under natural vegetation or land cover with low human disturbance, that the sites located in the agriculture areas or strong human intervention have been excluded. This screening procedure makes the pollen data readily available for biome and climate reconstructions. The contributors' original research concerning pollen-climate relationship from EAPD sources have been published in Zheng, et al. (2014 and 2008), which have revealed that pollen taxa in the dataset have significant relationship with climate variables. This dataset is potentially useful for multiscale paleovegetation and paleoclimate reconstruction studies in Asia. </p> <p>EAPD is developed and maintained by the Laboratory of Quaternary Science and Palynology in the School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai, China.</p>
Corpus Criticorum (1450-1650) - Supplement 1 - A comprehensive dataset of early modern publications featuring the notion of critique on their title pages
<p>Complementing the classical bibliography of the Corpus Criticorum (1450-1650), this comprehensive dataset includes: (1) internal project identifiers; (2) URL links to source catalogues used in the survey; (3) a tabular list of the names of all official contributors (authors, editors and translators); (4) an exact transcription of the title page; (5) publication date; (6) place of publication both as it appears on the title page itself and in modernised form; (7) publishing statement as it appears on the title page; (9) book format; (10) URL links to online images of title pages or, when not available, to a library holding a copy of the text in question. </p>
Methods of promoting modern theater on social media / Методи популяризації сучасного театру у соціальних мережах
<p>The dataset "Methods of promoting contemporary theatre on social media", based on a survey of 105 respondents, includes answers to the following questions:</p> <ul> <li>How often do you go to the theatre?</li> <li>What social media do you use to find out about theatre events?</li> <li>What type of content on social media is most effective in drawing your attention to theatre events? - Has social media ever prompted you to buy theatre tickets?</li> <li>What factors influence your decision to attend a theatre performance you saw on social media?</li> <li>How do you assess the overall effectiveness of social media in promoting theatre events?<br>Data downloaded in .csv format.</li> </ul>
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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.