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MIDAS2 protocol example input dataset v1
<p>Example input dataset for MIDAS2 protocols. </p> <p>Include single-end sequencing reads for two HMP mock community samples: SRR172902 and SRR172903.</p>
TNO-CAMS European CO2 emissions 2000-2014 v1
<p><strong>Introduction</strong></p> <p>This TNO_CAMS_CO2 emission dataset was prepared by TNO as a contribution to the H2020 project MACC-III and the subsequent Copernicus Atmospheric Monitoring Service. This model-ready historic emission inventory at high spatial resolution (~7x7 km) for UNECE-Europe for 15 consecutive years (2000–2014) providing CO<sub>2</sub> from fossil fuels and CO<sub>2</sub> from biofuels is intended to support modelling and sub-national scale identification of emissions. Where available and considered fit for purpose, we have used CO<sub>2</sub> estimates as reported by the Parties to UNFCCC. The data have been supplemented by other estimates, most notable from the IIASA GAINS model and the JRC EDGAR database to create a complete coverage. The approach to the spatial distribution of the dataset is similar to the TNO-MACC emission dataset for air pollutants ( see Kuenen et al., ACP, 2014).</p> <p>The emission grid consists of UNECE-Europe in WGS84 projection (lon-lat) with a spatial resolution of 1/8 x 1/16 degrees (lon x lat). The lower left of the grid is at lon = -60, lat = 30 and the upper right is at lon = 60, lat = 72.</p> <p>The grid files TXT (.csv) & netcdf (.nc) both contain annual total emissions per grid cell for the year 2000-2014. A separate file has been prepared for each year. </p> <p>The unit in the .csv files is Mg/gridcell/yr</p> <p>The unit in the .nc files is kg/gridcell/yr</p> <p>Sectoral breakdown uses the SNAP classification. Compared to the default SNAP1 sectors (1 to 10), a couple of refinements have been made to the sectors:</p> <ul> <li> <p>SNAP 3 and SNAP 4 are grouped as SNAP 34</p> </li> <li> <p>SNAP 7 is split in SNAP 71 to 75</p> </li> </ul> <p>The dataset is described in </p> <p>Denier van der Gon, H.A.C., J.J.P. Kuenen, G. Janssens-Maenhout, U. Döring, S. Jonkers, A.J.H. Visschedijk., TNO_CAMS high resolution European emission inventory for anthropogenic CO<sub>2</sub> for 2000-2014 and future years following two different pathways, ESSD, in preparation, 2017.</p> <p> </p>
spanichella/TestSmellDescriber-ReplicationPackage TestSmellDescriber-ReplicationPackage-v1.1
<p># TestSmellDescriber-ReplicationPackage</p> <p>1) "1_Emails-Sent-To-Participants" contains:</p> <p> 1. Email-WS1.docx and Email-WS1.pdf <br> 2. Email-WS2.docx and Email-WS2.pdf<br> <br> As explained in the paper the experiment was conducted offline, i.e., <br> we have send via email to the participants the required experimental material <br> with instructions about the tasks to perform. During the tasks the participants were guided via<br> Google Forms (shared in the above e-mail), this also to collect information about the performed activities. <br> As reported in the emails we send to each participant an experiment package composed by (i) a <br> pre-questionnaire (to collect information about the profile and experience of each participant), <br> (ii) surveys with instructions and materials to perform the tasks, and (iii) a post-questionnaire. <br> Before the study, we explained to the participants the expected tasks: <br> two maintenance and evolution tasks, each involving two pairs of Java and test classes. </p> <p><br> 2) "2_Information-about-smells-and-refactoring-operations" contains, <br> as reported in the paper, to facilitate the two tasks "we provided <br> the document <"Test Smells & Refactorings.pdf"> describing the notion of a test/code smells, the types<br> of smells potentially affecting test cases and the recommended refactoring operations to remove them.</p> <p>3) "3_Information-about-the-smells-detected" contains:<br> (a) "Summary for apache-ofbiz-16.11.04" contains name of all the smelly classes and name of the smell(s) each Java/test<br> class has.<br> (b) "Summary for at method level apache-ofbiz-16.11.04" contains name of the classes with name of the smelly <br> methods and name of the smell<br> each method is suffering with.<br> <br> 4) "4_Surveys-Sent-To-Participants" contains<br> the 4 surveys (in pdf format) performed by the participants.</p> <p> 1. Pre-Task TSD survey.pdf includes Brief Introduction of this experiment and question related to basic information of participants.<br> 2. Task 1 TSD survey.pdf includes testing task 1, additional information to perform the task 1 and questions related to the task 1.<br> 3. Task 2 TSD Survey.pdf includes testing task 2, additional information to perform the task 2 and questions related to the task 2.<br> 4. Post-Task TSD survey.pdf includes questions regarding the TestSmellDescriber tools performance and usefulness, provides opportunity<br> to suggest modifications and questions related to consiceness, completness and precision of the whole survey and tasks.<br> <br> 5) "5_Templates-defined-and-used-to-generate-the-summaries".<br> As reported in the paper, "by leveraging the SWUM model TestSmellDescriber generates descriptions at <br> different levels of abstraction, as reported in Figure 1 of the paper:<br> - a short and long method description, <br> - a short and long refactoring description, <br> - and a quantitative description of the smell in the context of the whole project. <br> The descriptions are generated, as done in previous work, with natural "language templates" that are <br> augmented by the information that is gathered from the smell detection process. </p> <p>6) "6_Working-Dataset" contains the two workspaces we gave to the participants for executing the tasks.<br> <br> a) “Workspace1-Sent-To-Participants” contains the java projects we selected for our experiment;<br> in this case, we have selected 2 target classes, needs to be smell free, one class without <br> summaries and another with summaries<br> b) “Workspace2-Sent-To-Participants” contains the java projects we selected for our experiment;<br> in this case, we have selected 2 target classes, needs to be smell free, one class without <br> summaries and another with summaries<br> c) It is important to mention the test/Java classes used in the study are are located in the <br> workspaces in the following relative paths:<br> 1) ...\src\framework\base\src\main\java\org\apache\ofbiz\base\util\string\test\FlexibleStringExpanderTests.java<br> 2) ...\src\framework\base\src\main\java\org\apache\ofbiz\base\util\test\TimeDurationTests.java<br> 3) ...\src\framework\base\src\main\java\org\apache\ofbiz\base\util\string\FlexibleStringExpander.java<br> 4) ...\src\framework\base\src\main\java\org\apache\ofbiz\base\util\TimeDuration.java<br> 5) ...\src\framework\base\src\main\java\org\apache\ofbiz\base\util\collections\test\FlexibleMapAccessorTests.java<br> 6) ...\src\framework\base\src\main\java\org\apache\ofbiz\base\util\cache\test\UtilCacheTests.java<br> 7)...\src\framework\base\src\main\java\org\apache\ofbiz\base\util\collections\FlexibleStringExpander.java<br> 8) ...\src\framework\base\src\main\java\org\apache\ofbiz\base\util\cache\UtilCache.java<br> </p> <p>7) "7_Results-of-the-questionnaires" contains a folder "data-analysis" containing<br> - "data" folder which contains: <br> - in "figs" several figures about the main results achieved (some of them used in the paper).<br> - the file "full_results-testsmelldescriber-anonymized.csv" reporting all results<br> of the involved study participants. It was used to compute the statistics reported in the paper by using the <br> R script which is in the folder "R-script" (explained later in the readme file).<br> - "R-script" folder which contains<br> - "analysis.R" the script files used to computed all statistics explained in section study<br> and reported in the papers. The script automatically generates the figures in the folder <br> "data/figs". <br> - "summary - Post-Task TSD Survey.pdf" - It reports the summary of results of the post-experiment questionnaires<br> - "summary - Pre-Experiment TSD Survey.pdf" - It reports the summary of results of the pre-experiment questionnaires<br> - "summary---Task1-Survey.pdf" - It reports the summary of results of the of the post-task questionnaire<br> - "summary---Task2-Survey.pdf" - It reports the summary of results of the post-task questionnaire<br> - "tasks-participants" contains the information the participants provided us about the performed changes.</p> <p>8) "8_TestSmellDescriber-research-prototype" contains the<br> prototypical version of TestSmellDescriber we used to generate the summaries <br> evaluated for the experiments. We provide information on how to use and run the tool<br> on the provided data.</p>
WP4_SIM_linewidth&bandwidth_V1
<p>WP4_SIM_LO-linewidth&frequencyDeviation_V1.docx</p> <p>WP4_SIM_photodiodeBandwidth_V1.xlsx</p> <p>Simulations results evaluating the performance of a coherent detector as a function of local oscillator (LO) frequency drifts and non-negligible LO linewidths, with different PD electrical bandwidths.</p> <p>The received signal is obtained by direct modulation with DMT of a SC-long wavelength VCSEL.</p>
WP5_TST_Tunable-vcsel-results_V1
<p>The Excel file contains the experimental results obtained in the characterization and exploitation of a 7-GHz tunable VCSEL. In particular, the laser behavior in function of bias current, MEMS current and temperature is reported both in terms of emitted optical power and emission wavelength. Moreover, the performance of a 8-GHz DMT modulation with and without vestigial sideband filtering on up to 40 km of SMF is reported.</p>
WP3_TST_VCSEL&driverPerformance_v1
<p>WP3_TST_D32-driverHXT14100_10GOOK_V1.mat</p> <p>WP3_TST_D32-driverHXT14100_10GPAM_V1.mat</p> <p>WP3_TST_D32-driverHXT14100-DMT-data-10GHz_V1.mat</p> <p>WP3_TST_D32-driverHXT14100-DMT-SNR-10GHz_V1.mat</p> <p>WP3_TST_D32-driverHXT14100-DMT-trainingSequence-10GHz_V1.mat</p> <p>WP3_TST_D32-driverHXT44100-DMT-data-10GHz_V1.mat</p> <p>WP3_TST_D32-driverHXT44100-DMT-SNR-10GHz_V1.mat</p> <p>WP3_TST_D32-driverHXT44100-DMT-SNR-16GHz_equalizer_V1.mat</p> <p>WP3_TST_D32-driverHXT44100-DMT-SNR-16GHz-noEqualizer_V1.mat</p> <p>WP3_TST_D32-driverHXT44100-DMT-trainingSequence-10GHz_V1.mat</p> <p>WP3_TST_D32-Drivers-characterization_V1.xslx</p> <p>The files contain experimental results (measurements). In particular, the time samples of 10-Gb/s OOK/PAM4 are recorded for a short-cavity VCSEL directly modulated by commercial IDT drivers. The VCSEL is bonded to the driver as explained in PASSION Deliverable D3.2 http://www.passion-project.eu/wp-content/uploads/2018/09/PASSION_D3.2_v1.0.pdf. Moreover, the results obtained with DMT modulation on 10 GHz bandwidth are recorded in the .mat files, together with the SNR obtained using the QPSK uniform loading. Finally, a characterization of the VCSELs with the experimented drivers is reported in the Excel file.</p> <p> </p>
Example Dataset for npstat: Population genetics from Pooled NGS data NPStat v1: User guide
<p>Example Dataset for npstat to test the program and the different options.</p> <p>The example dataset contains a pileup file with sequences of of the 2L chromosome from fifteen pooled inbreed individuals of <em>Drosophila melanogaster </em>(<span>doi: 10.1038/nature10811</span>). The dataset also contains the sequence reference of the 2L chromosome in fasta format, an outgroup sequence in fasta format of <em>D. yakuba</em> (SRR26246471), a GFF3 annotation file and a file with a brief list of selected SNPs to be analyzed.</p>
RRI2SCALE_D2.3_Scenario Validation_2021.10.15_v1
<p>This dataset contains the results of a short survey to assess the realisation probability and the desirability of alternative techno-moral scenarios in the domains of intelligent cities, intelligent transport, and intelligent energy.</p> <p>More info:</p> <p>RRRI2SCALE partners designed and implemented a validation process to assess whether the six techno-moral scenarios developed previously (two per domain, i.e., intelligent cities, intelligent transport and intelligent energy) meet high-quality scenario criteria (are probable, desirable, different from one another, complete and internally consistent).</p> <p>More specifically, for the deliberation with the citizens, videos presenting the scenarios were created and uploaded on the RRI2SCALE website and social media (available here: https://rri2scale.eu/index.php/regional-validation-of-scenarios/). Citizens from the four regions (i.e., Kriti, Galicia, Overijssel and Vestland) were prompted to participate in a short survey, assigning scores to scenarios’ desirability and probability. The dataset uploaded includes all the replies received.</p> <p>Facebook was the primary platform selected to facilitate and promote the scenario deliberation process. According to statistics provided by the Facebook “Ads Manager” tool, the RRI2SCALE campaign reached more than 31,000 people. Almost half of them (about 13,000) watched the videos, more than 2,000 people clicked on the survey link, and finally, we collected 279 valid survey replies from citizens of the four regions.</p> <p>Among the people who clicked on the survey link, 60% were male, while most (about 62%) fall within the age of 45-64. Among 279 valid replies received, 114 replies were received from the citizens of Galicia, 93 from Kriti, 37 from Vestland and 35 from Overijssel. From another perspective, among the 279 valid replies received, 92 replies refer to the Intelligent Cities scenarios, 104 replies refer to the Intelligent Transport scenarios, and 83 replies refer to the Intelligent Energy scenarios.</p>
RRI2SCALE_D2.1_Delphi Survey_2021.10.15_v1
<p>This dataset contains the demographic information and the replies that field experts provided in a Delphi survey performed by the RRI2SCALE project to identify future developments in the domains of intelligent cities, intelligent transport, and intelligent energy.</p> <p>More info:</p> <p>In an age of rapid changes in demographics, businesses, technology and lifestyles, policymakers operating at territorial (regional, local) level are challenged to design policies with a forward-looking perspective. Yet, the future is uncertain and most often unpredictable and hence, they strive to predict future developments and their economic and policy implications. Since the early 1960s, several technology-forecasting methods have been developed. RRI2SCALE performed a Delphi survey aiming to forecast future developments in the domains of intelligent cities, intelligent transport, and intelligent energy.</p> <p>In particular, we first conducted a literature review to develop 38 survey statements that reflect on the probability of appearance of potential drivers (i.e., developments that causes change, affect, or shape the future), trends (e.g., notable characteristics of a phenomenon), and impacts on the domains of focus. Then, 120 experts from across Europe in the fields of intelligent cities, transport, and energy, as well as territorial Responsible Research and Innovation (RRI) were recruited to participate in a Delphi survey. As typical for this type of study, the survey took place in two rounds, enabling participants to revise their responses in the light of other participants’ views. In the second round of the study, 88 experts participated.</p> <p>In the first round of the Delphi survey, 954 experts were contacted, and 120 valid replies were received (13.5% response rate). Almost half of the 120 panel participants self-declared holding “High” level of expertise in the territorial aspects of digitalisation (44%). Similarly, 41% of them were self-reported as experts in RRI, 33% as experts in intelligent cities, 27% in intelligent transport and 17% in intelligent energy. Besides, most participants declared living in Europe. Indicatively, 18% of them resided in Greece, 8% in the United Kingdom, 8% in Italy and 7% in Spain. Finally, 46% of them participated in the survey as members of academia or the research society, 22% of them as members of governmental bodies, and 9% of them as business experts. The dataset uploaded includes all the replies received.</p>
Martian BS crossing_MAVEN_V1
<p>The database is provided by the Institute of Space Weather, Nanjing University of Information Science and Technology, Nanjing, PR China.<br> If you have any questions, please contact us at suihaoyang1998@163.com.</p> <p>The dataset has 5 columns and 10,283 rows, recording the time and location of 10,283 Mars bow shock crossings. The locations of crossings are all calculated in the MSO coordinate system.<br> </p>
FlexiGroBots Ground-level Blueberry Orchard Dataset v1 - RGB Bush Detection Dataset
<p><strong>Ground-level Blueberry Orchard Dataset v1</strong> consists of 2000 RGB images of blueberry orchard scenes captured in the village of Babe, Serbia on three occasions in March, May, and August of 2022. Images are captured using the RGB module of Luxonis OAK-D device, with the resolution of 1920×1080 pixels and stored in the lossless PNG format. </p> <p>The dataset is created for the purpose of training deep learning models for blueberry bush detection, for the task of autonomous UGV guidance. It contains sequences of images captured from the UGV moving and rotating in blueberry orchard rows. Images are captured from a height of approximately 0.5 meters, with the camera angled towards the base of a blueberry plant and the surrounding bank on which it grows. Dataset is captured in real-life outdoor conditions and contains multiple sources of variability (bush shape and size, lighting conditions, shadows, saturation etc.) and artifacts (occlusions by weeds, branches, presence of irregular objects etc.).</p> <p>There are two classes of annotated objects of interest:</p> <ul> <li> <p>Bush, corresponding to the base of the blueberry bush.</p> </li> <li> <p>Pole, corresponding to hail netting poles and similar obstructing objects such as lamp posts or wooden legs of bumblebee hives (distinguishing poles is important to prevent equipment damage in operations such as soil sampling and pruning).</p> </li> </ul> <p>Objects of interest are annotated with bounding boxes. Labels are saved in two formats:</p> <ul> <li> <p>LabelMe JSON format (x1, y1, x2, y2; in pixels)</p> </li> <li> <p>Yolo TXT format (x_center, y_center, width, height; as a ratio of total image size, with numerical labels 0 and 1 corresponding to Bush and Pole)</p> </li> </ul> <p>There are 61 images with no annotated objects, and there are no corresponding label files for these images.</p> <p>The dataset is split into train, validation and test sets with 75%, 10%, and 15% split (1490, 200, and 310 images, respectively). As the data contains sequences of images, the split is made based on sequences rather than individual images to prevent data leakage.</p> <p>Detailed description and statistics are available in:</p> <p>V. Filipović, D. Stefanović, N. Pajević, Ž. Grbović, N. Đurić and M. Panić, "Bush Detection for Vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Vancouver, Canada, 2023. (Accepted)</p>
MainstreamBIO_Collection of Nutrient Recycling Practices_Dataset1_2023.06.01_v1
<p>The dataset contains a developed set of Nutrient recycling practices (NRPs), which refer to the processes of recovering nutrients from organic waste and wastewater and returning them to agricultural land as recycled nutrient fertilizers (RNFs). In the processes, nutrients from nutrient-rich materials created during production and consumption are reused sustainably and safely as recycled nutrients. NRP is a sustainable practice that helps reduce waste and pollution while improving soil health and increasing crop yields. NRP also reduces the use of non-renewable natural resources and can reduce nutrient emissions into the environment.</p> <p>Most of the selected practices are widely used mainly in agriculture, but also in forestry. A large group of them is well recognized and accepted by stakeholders, but some of them are associated with a specific location where appropriate biomass and technology is available. A certain group of practices presents an innovative approach to the problem of nutrient recycling and these practices are not widely known and implemented.</p>
ΝEW ABC_ WP2 _ Task 2-1 Mapping the NEW ABC platform requirements_20230609_v1
<p>This data set consists of an analysis of users of existing platforms of other European projects, and the level of engagement they create, to inform the creation and successful use of NEW ABC project platform. This analysis will assess the number and typology of users who access these platforms and what kind of information and material is available to them. The data set will contain numerical and textual tabular data converted into digital format (survey made by online interviews with platform users), quantitative and qualitative data.</p> <p>Content of the files:</p> <ul> <li>file <strong><em>ΝEWABC_WP2_T2-1_Mapping the NEW ABC platform requirements_20230609_v1.csv </em></strong>contains a description of the data collected for each Horizon project relevant to the NEW ABC.</li> </ul> <p> </p> <ul> <li>file <strong>README_</strong><strong><em> ΝEWABC_WP2_T2-1_Mapping the NEW ABC platform requirements_20230609_v1.rtf</em></strong> contains basic information on the dataset uploaded.</li> </ul>
MainstreamBIO_Needs Analysis_Dataset2_2023.02.28_v1
<p>The dataset comprises the outcomes of 35 semi-structured interviews conducted with renowned experts in the bioeconomy domain. The primary objective of these interviews was to obtain comprehensive insights into various aspects of local bioeconomy development and its social acceptance. The experts were probed on multiple dimensions, including the prevailing framework conditions, existing needs, challenges, and market conditions related to bioeconomy practices. Furthermore, the interviews also delved into exploring nutrient recycling practices in the context of bioeconomy development. These in-depth conversations with experts were intended to gain a profound understanding of the subject matter and gather valuable information to advance research and decision-making in this field.</p>
MainstreamBIO_Needs Analysis_Dataset1_2023.02.28_v1
<p>The dataset comprises the findings of an online survey that aimed to assess awareness levels and perceptions related to the bioeconomy, biobased solutions, products, and nutrient circularity practices among a diverse group of stakeholders, including consumers.</p> <p>The results indicate notable variations both across the targeted countries and among different key-stakeholder groups. Concerning awareness and perceptions of bioeconomy development and biobased products and solutions, the primary challenges and requirements seem to revolve around economic factors, such as the lack of investments and limited access to essential finance. However, other significant determinants also play a role, including the absence of appropriate policy and regulatory frameworks governing the bioeconomy sector and issues pertaining to stakeholder engagement, particularly concerning a lack of relevant awareness.</p>
DORIS-MAE-v1
<p>In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical. Existing evaluation datasets for this task are limited, primarily due to the high costs and effort required to annotate resources that effectively represent complex queries. To address this, we propose a novel task, <strong>S</strong>cientific <strong>DO</strong>cument <strong>R</strong>etrieval using <strong>M</strong>ulti-level <strong>A</strong>spect-based qu<strong>E</strong>ries (DORIS-MAE), which is designed to handle the complex nature of user queries in scientific research.</p> <p>Documentations for the DORIS-MAE dataset is publicly available at <a href="https://github.com/Real-Doris-Mae/Doris-Mae-Dataset">https://github.com/Real-Doris-Mae/Doris-Mae-Dataset</a>. This upload contains both DORIS-MAE dataset version 1 and ada-002 vector embeddings for all queries and related abstracts (used in candidate pool creation). DORIS-MAE dataset version 1 is comprised of four main sub-datasets, each serving distinct purposes.</p> <p>The <strong>Query</strong> dataset contains 100 human-crafted complex queries spanning across five categories: ML, NLP, CV, AI, and Composite. Each category has 20 associated queries. Queries are broken down into aspects (ranging from 3 to 9 per query) and sub-aspects (from 0 to 6 per aspect, with 0 signifying no further breakdown required). For each query, a corresponding candidate pool of relevant paper abstracts, ranging from 99 to 138, is provided.</p> <p>The <strong>Corpus</strong> dataset is composed of 363,133 abstracts from computer science papers, published between 2011-2021, and sourced from arXiv. Each entry includes title, original abstract, URL, primary and secondary categories, as well as citation information retrieved from Semantic Scholar. A masked version of each abstract is also provided, facilitating the automated creation of queries.</p> <p>The <strong>Annotation</strong> dataset includes generated annotations for all 165,144 question pairs, each comprising an aspect/sub-aspect and a corresponding paper abstract from the query's candidate pool. It includes the original text generated by ChatGPT (version chatgpt-3.5-turbo-0301) explaining its decision-making process, along with a three-level relevance score (e.g., 0,1,2) representing ChatGPT's final decision.</p> <p>Finally, the <strong>Test Set</strong> dataset contains human annotations for a random selection of 250 question pairs used in hypothesis testing. It includes each of the three human annotators' final decisions, recorded as a three-level relevance score (e.g., 0,1,2).</p> <p>The file "ada_embedding_for_DORIS-MAE_v1.pickle" contains text embeddings for the DORIS-MAE dataset, generated by OpenAI's ada-002 model. The structure of the file is as follows:<br> </p> <p>├── ada_embedding_for_DORIS-MAE_v1.pickle<br> ├── "Query"<br> │ ├── query_id_1 (Embedding of query_1)<br> │ ├── query_id_2 (Embedding of query_2)<br> │ └── query_id_3 (Embedding of query_3)<br> │ .<br> │ .<br> │ .<br> └── "Corpus"<br> ├── corpus_id_1 (Embedding of abstract_1)<br> ├── corpus_id_2 (Embedding of abstract_2)<br> └── corpus_id_3 (Embedding of abstract_3)<br> .<br> .<br> .</p>
NDC-SDG Connections: Data on first NDC submissions (V1)
<p>NDC-SDG Connections is a joint initiative of the German Institute of Development and Sustainability (IDOS) and the Stockholm Environment Institute (SEI). The research and visualisation project aims at illuminating synergies between the 2030 Agenda for Sustainable Development and the Paris Agreement, and at identifying entry points for coherent policies that promote just, sustainable and climate-smart development.</p> <p>The objective of the NDC-SDG Connections is to: foster a dialogue on meaningful interaction between the 2030 Agenda and the Paris Agreement, globally and at the national level; to increase transparency with easy accessibility to all climate activities; and to cultivate learning and catalyse partnerships between countries and other actors to raise the ambition of future NDCs.<br> <br> With its second version, the NDC-SDG Connections project opened its data for public re-use. The data on first NDC submissions (V1) is provided in the following formats:</p> <ul> <li>single .csv files (per data per SDG)</li> <li>zip .csv file (data per SDG for all SDG in one zip)</li> <li>.xlxs file (Excel)</li> </ul> <p><strong>Visit the Online Data Visualisation to interact directly with the data: www.NDC-SDG.info</strong></p> <p>Additional files:</p> <ul> <li>.pdf file documenting the methodological framework including the coding and data validation process of the NDC-SDG Connections project</li> <li>.csv file with all NDCs included into the analysis (V1)</li> </ul> <p><br> <strong>Note: This data set contains data for first NDC submissions (V1). </strong>The terms ‘First’ and ‘Updated’ do not fully follow the UNFCCC nomenclature. For most countries, updated NDCs are called ‘First updated NDC’ or ‘Enhanced NDCs’, while some countries call their updated NDCs for ‘Second NDC’. In order to make it comprehensible, the tool developers have chosen to distinguish between ‘First’ and ‘Updated’. Detailed description of which version is counted as ‘First’ and which as ‘Updated’ has been documented in the data.</p> <p> </p>
MainstreamBIO_ValueChainAnalysis_Dataset1_2023.09.11_v1
<p>Key characteristics of Regional value chains (feedstock, fates) collected through interviews from key regional stakeholders. The Country Regions and selected value chains per region are:</p> <p>1) South Central (NUTS2: BG42) - Forestry biomass, Greenhouse biomass residue, Local crop biomass.</p> <p>2) Zealand NUTS2: (DK02) Mid Jutland (NUTS2: DK04), Northern Jutland (NUTS2: DK05) - Grass, Animal manures, Cereal straws.</p> <p>3) Southern Ireland (NUTS2: IE05) - Grass, Animal manures, Cereal straws, Seaweed, Apple residues, Hemp.</p> <p>4) Friesland (NUTS2: NL12) Flevoland (NUTS2: NL23) - Grass (roadside and nature), Animal manures, Pumpkin residues.</p> <p>5) Lubelskie (NUTS2: PL81) - Sugar beet residues, Berries residues, Corn residues, Rapeseed residues.</p> <p>6) Catalonia (NUTS2: ES51), Navarre (NUTS2: ES22), Aragon (NUTS2: ES24) - Pig slurry, Lucerne, Forest industry residues, Camelina, Brewers spent grain.</p> <p>7) Middle Norrland (NUTS2: SE32), Upper Norland (NUTS2: SE33) - Forestry logging residues, Bio sludge, Fibre sludge.</p>
Best4Soil_databases_datamining_V1
<p>This document contains data on the host plant status of field crops, vegetable crops and green manure crops for soilborne plant parasitic nematodes and soilborne fungal pathogens.</p> <p>The data was used for the construction of two databases in context of the Best4Soil Thematic Network, a Horizon 2020 project (Grant Agreement n°817696). The databases are accessible through the website <a href="https://ww.best4soil.eu/database">https://ww.best4soil.eu/database</a> ; they are hosted on <a href="https://nematodes.soilhealthtool.eu/">https://nematodes.soilhealthtool.eu/</a></p> <p> </p>
MainstreamBIO_RepositoryMaterial_Dataset1_2024.09.20_v1
<p><span lang="EN-US">Data has been collected through several EU and non-EU funded projects, EU platforms (e.g., Knowledge center for Bioeconomy, European Network for Rural Development etc.), and open Libraries to develop the MainstreamBIO Repository, which is included in the MainstreamBIO Digital Toolkit. The collected material is qualitative and elicited in spreadsheets, reports, standard documents, infographics, games, databases, and tools.</span></p>
ScienceDex guides
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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.